An Automatic System for Skeletal Bone Age
Transcript of An Automatic System for Skeletal Bone Age
SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 1
An Automatic System for Skeletal Bone Age
Measurement by Robust Processing of Carpal
and Epiphysial/Metaphysial Bones
Daniela Giordano, Member, IEEE, Concetto Spampinato, Giacomo Scarciofalo and Rosalia Leonardi
Abstract
This paper presents an automatic system for bone age evaluation according to the clinical method of Tanner and
Whitehouse (TW2), based on the integration between two systems: the first processes the finger’s bones (EMROI
- Epiphyses/Metaphyses ROI), whereas the second one the wrist bones (CROI - Carpal ROI). The system ensures
an accurate bone age assessment for the age range (0-10) years for males and (0-7) years for females. For both
approaches novel segmentation techniques are proposed. In detail, for CROI analysis the bones extraction is carried
out by integrating anatomical knowledge of the hand and trigonometric concepts, whereas the TW2 stage assignment is
achieved by combining the Gradient Vector Flow (GVF) Snakes and derivative difference of Gaussian (DrDoG) filter.
One of the main difficulties for bone age assessment based on carpal bones is that Trapezium and Trapezoid, which
are among the biggest bones in the wrist, are often fused even in very young patients. This problem is overtaken by a
very effective algorithm that checks the compactness of the identified bones and separates them by using a curvature
function. For EMROI analysis image processing techniques and geometrical features analysis, based on difference of
Gaussian (DoG) are proposed. The system was evaluated on a set of 106 X-Rays, reaching performances of about
90% success rate in bone stages assignment. The system is very reliable and outperforms other effective methods.
Moreover the mean error rate is of about 0.46 ± 0.37 years that is comparable with clinicians reliability, for whom
the error has been estimated to be 0.33 ± 0.6 years.
Index Terms
Skeletal Bone Age, Tanner Whitehouse Method (TW2), X-Ray Image Processing, Trapezium/Trapezoid Fusion
Management, Image Segmentation.
I. INTRODUCTION
The determination of the skeletal maturity or “bone age”, plays a very important role both in diagnostic and in
therapeutic investigations of endocrinological problems, growth disorders of children and even genetics disorders
D. Giordano, C. Spampinato and G. Scarciofalo are with the Department of Informatics and Telecommunication Engineering, University of
Catania, Catania, 95125 Italy e-mail: {dgiordan, cspampin, gscarci}@diit.unict.it
R. Leonardi is with the Department of Orthodontics, Policlinico ”Gaspare Rodolico” - University of Catania Via S.Sofia 78 - 95123 Catania
- Italy e-mail: [email protected]
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[1]. There are various indexes describing the chronological development of humans, e.g. height [2] and dental
age [3]; however, bone age measurement is widely used for its feasibility and reliability in diagnosing hereditary
diseases and endocrine disorders [4] and it can serve as an indication of the therapeutic effect of treatment [5]
because of the discriminant nature of development stages of the non dominant hand. Bone age evaluation is usually
performed by radiological examination of the skeletal development of the left-hand, and then is compared with
the chronological age. A discrepancy between these two values surely indicates abnormalities. This examination is
widely used due to the advantages of simplicity, a minimum radiation exposure, and the availability of multiple
ossification centers for the evaluation of the bone’s maturity. There is no standard clinical procedure in bone age
assessment, even if the most used methods are: 1) the Greulich and Pyle (G&P) method [6] and 2) the Tanner and
Whitehouse (TW2 or TW3) methods [7]. Both methods rely on X-Ray images taken from the left hand, but there
are several differences between the two methods. The G&P method is the most often used approach (by 76% of
radiologists) especially in the Netherlands [8]), mainly because the G&P method is faster and easier to use with
respect to the TW2 or TW3 methods, since it involves only the comparison of the whole hand with a reference
atlas. The main shortcoming of G&P approach is the variability of the analysis performed by various observers with
different levels of training, since studies have shown interobserver differences varying from 0.07 to 1.25 years and
intraobserver differences varying in average from 0.11 to 0.89 years [9]. The average time of single case reading
depends on radiologists’s clinical experience and falls in the range 2 to 5 minutes. The Tanner-Whitehouse methods
(especially used in United States) differ from the G&P method because instead of using the hand as a whole, they
are based on a set of bones, whose standard maturity varies according to age population. In detail, the TW2 method
ROIs (Region of Interest) considered for the bone age evaluation are located in the main bones, including EMROI
(Ephiphysial/Metaphysial ROI), CROI (Carpal ROI), radio and ulna. The development of each ROI is divided into
discrete stages and each stage is given a letter (A, B,C, D, · · · , I). A numerical score is further associated with
each stage of each bone. By adding the scores of all ROIs, an overall maturity score is obtained. The TW2 method
has been modified throughout the years, evolving in the TW3 method, which maintains the description for the
bones’ stages but calibrates the scoring method on North American children [7]. Although the TW2 method yields
a more accurate estimation than the G&P method (the average interobserver spread for TW2 is 0.74 years against
the 0.96 years for the G&P method [10]), it is less used because of its high complexity, yet its modular structure
makes it suitable for automation.
The automation of the bone age assessment has been tackled with two main approaches: image processing and
segmentation techniques for geometrical features extraction and knowledge based methods. Concerning the image
processing methods, the first semi automated system has been developed by Michael [11]. The author claims that
the system was able to automatically segment the bones in a hand radiograph but large scale tests demonstrated
that the system was not reliable when hand bones are fused.
Manos [12] developed a segmentation method for the wrist using region growing and merging. The technique that
is used is basically a bottom up approach. A standard thresholding technique for carpal ROI extraction is proposed
by Pietka in [8] but no assessment was carried out and moreover the used thresholding is quite obsolete. One
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of the most known methods for the automation of TW2, by using EMROI analysis, has been proposed in [13],
where the regions of interest are processed by an ad hoc feature extraction function. This method shows an high
accuracy measured independently at three stages of the image analysis: detection of phalangeal tip, extraction of the
EMROIs, and location of diameters and lower edge of the EMROIs. This is the first work where the EMROI bones
are segmented instead of finding the position of their edges. This system has been extended by assessing the degree
of epiphysial fusion by wavelet transform [14]. Other approaches based on ACM (Active Contours Models- snakes)
[15] have also been proposed. Garcia in [16] proposed a fully automatic system for detecting bone contours based
on prior knowledge to locate initially the snakes (ACM). The ACM models have been used even for 3-D wrist
detection as shown in [17]. Niemeijer [18] introduced a bone age estimation method that uses ASM (Active Shape
Models) but presents the problem of initial conditions, i.e., appropriate positioning of the models on the image. In
[19] carpal bones are segmented by using gradient vector flow GVF - Snakes [20] after a suitable preprocessing
phase via anisotropic diffusion.
All the above works only make use of computed features, but there are other methods that assess the development
stage with knowledge based techniques, taking as input the computed features. The methods proposed by Mahmoodi
in [21] and in [22] employs decision theoretic approaches based on Bayesian Networks; fuzzy systems have been
used in [23] and [24]. In [25] the authors describe a system that implements the TW2 method using a neural network
architecture. Each bone complex is localized on the image, and preprocessed using either a Gabor transform or a
multi-scale Difference of Gaussian filtering.
The main problem of all the above methods is that they rely only on EMROI analysis, which is a interesting and
an relatively easy task from the image processing field side, but is not reliable for a complete bone age assessment,
because it doesn’t facilitate evaluation the bones growth. This would require a more complete analysis including the
CROI processing. Not many approaches have been proposed for the CROI processing and most of the approaches
for CROI extraction and analysis use classical image segmentation, such as region growing, or edge detection
techniques for features extraction and not for bone evaluation such as [8] and [26]. An approach where a radius
ulna TW3 bone age assessment is carried out is proposed in [27] where bone age is estimated with the help of
a Generalized Softmax Perceptron (GSP) Neural Network (NN) whose optimal complexity is estimated via the
Posterior Probability Model Selection (PPMS) algorithm. A very recent method is the BoneXpert proposed by
Thodberg in [28], where a model for the bone’s reconstruction and an unified modeling of TW and G&P bone age
are proposed. However, BoneXpert lacks in the X-ray preprocessing capabilities, in fact it rejects images with poor
image quality or abnormal bone structure, thus making the analysis in some case manual.
Another shortcoming of all the above methods is that they depend strongly on the user settings, for example on
hand orientation.
The recognition and separation of carpal bones from the image is a very difficult task, because 1) of the presence of
several overlapping regions of interest, 2) of ROI having completely different degree of calcification and overlapping
with soft tissue and 3) of the borders of individual carpal bones hardly visible. A method that tried to integrate the
CROI analysis and EMROI analysis was proposed in [29], but it is largely a manual method, since just single parts
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of the system are automatized.
In this work we propose a fully automatic system for bone age evaluation, according to the TW2 method, based on
the integration between EMROI and CROI analysis and ensuring accurate bone age assessment for the age range
(0-9) years for males and (0-7) years for females. Indeed medical studies [30] indicate that the analysis of carpal
bones do not provide accurate and significant information for patients older then 9-12 years; but, on the other hand,
over that range the bone evaluation is not very reliable and sometime is not required.
The main difficulty in CROI integration is the partial or total fusion between the Trapezium and the Trapezoid,
which has been solved in our work with a novel and effective segmentation technique in conjunction with the
GVF- Snakes and ASM for carpal bone labeling and classification. This method allows both ASM and ACM to
be independent from the initial conditions because the snakes are applied only after detecting and managing the
fusion between Trapezium and Trapezoid.
The remainder of the paper is organized as follows. Section II presents an overall description of the system. Section
III shows the preprocessing system that aims 1) to remove labels from the input X-Ray and 2) to rotate the hand when
it is necessary. Section IV describes how the different features of the EMROI system are extracted and measured
by image processing techniques. In section V a novel segmentation technique for CROI fusion management and
features extraction by using GVF-snakes is presented. Section VI illustrates the methods used for evaluating the
performance of the system. Finally, section VII presents the results of the evaluation and the conclusions.
II. PROPOSED SYSTEM OVERVIEW
The proposed system is based on three stage model. The first stage (preprocessing) enhances specific radiological
areas, segments out predefined anatomical regions, rotates the hand when it is needed. The second stage delivers
quantitative parameters or measures for the automatically extracted CROIs and EMROIs. The final stage performs
the decision making process (i.e. stage assignment and overall bone assessment). Due to various factors, the hand
X-rays images are often poor in contrast, and image features in the cross sectional part are often obscured and
degraded by artifacts. In order to obtain accurate feature information, we apply image preprocessing methods to
remove artifacts and degradations such as blurring and noise. Moreover, a preprocessing phase for background
removal and hand rotation is needed. For both EMROI and CROI analysis, novel segmentation techniques are
proposed. In detail, for CROI analysis active contour model (ACM) based on GVF-Snakes and ASM, according
to [31], and derivative difference of Gaussian (DrDoG) techniques are proposed. For EMROI analysis, image
processing techniques and geometrical features analysis, based on difference of Gaussian (DoG), are introduced.
An innovative method, which is inspired by a method proposed in a motion detection system [32], for carpal bones
separation is also presented. The flow diagram of the proposed system is shown in fig. 1, whereas fig. 2 shows the
fifteen bones taken into account: eight phalanges (EMROI) and seven carpal bones (CROI).
III. PREPROCESSING SYSTEM
The preprocessing stage is a very important task in order to standardize X-ray images, because there are many
irregularities present in the radiograph, the bone contours are not well defined and there is a large variability
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Fig. 1: General Procedure for Skeletal Bone Age Assessment.
Fig. 2: EMROIs and CROIs.
between radiographs. The proposed preprocessing systems includes: 1) background suppression, 2) radiological
markers removal and 3) hand rotation. The first two steps aim at removing the background and the radiological
markers on the X-ray radiograph. Since the difference in gray levels between bone and non bone pixels (labels and
soft tissue) is not consistent across the image, a simple thresholding on the original image is not always successful.
To overcome this problem, for background separation we use an algorithm that operates only over the background
pixels, identified as those pixels satisfying some conditions obtained by an analysis of the image histogram statistics
[33]. Therefore, in this stage we eliminate all pixels not belonging to the hand and exclude any information as
labels and / or impurities resulting from the scan.
A. Background and Radiological Markers Removal
At the beginning of this step the X-ray is converted from RGB to gray level. In some cases the image is cropped
to remove white borders from the digitalized X-ray or to extract the left hand when both hands are present in the
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same X-ray. We define the background as an area outside the radiation field, caused by blocking of the collimator
and resulting in white borders surrounding the radiation field. Many algorithms have been proposed in literature
which put to black the unexposed background such as [34], [35]. EMROI and CROI analysis requires an increase
of the hand-to-background ratio, which is important when an under or over exposed image is the input of the
system. For background separation we apply an algorithm that works only over the background pixels, i.e. those
pixels satisfying some conditions obtained by extracting image histogram statistics. Let Mg and Dg be the mean
and the standard deviation of the histogram (gray levels distribution) of the entire image, let MSx,y and DSx,y be
the local mean and the standard deviation of the histogram in the region Sx,y surrounding a generic pixel (x, y).
The local mean is a measure of the mean gray level value in the region Sx,y around the pixel (x, y); the local
standard deviation is an index of the contrast in that region. Usually, the background pixels are characterized by
low gray level and low contrast; therefore the conditions to verify are:
MSx,y≤ k0 · Mg (1)
where k0 is a positive constant lower than one, and
k1 · Dg ≤ DSx,y ≤ k2 · Dg (2)
where k2 is a positive constant lower than one; and k1 is a positive constant lower than k2 that represents the
lower bound.
In our method we set to zero (black pixels) all the pixels that satisfy the conditions 1 and 2, and to one (white
pixels) the remaining pixels. The output of the proposed system is shown in fig. 3 where for a better view the
negative of the images are shown.
A flooding fill algorithm is then applied, selecting as the starting point the barycenter of the white pixels (black
point in fig. 3(b)). The flooding algorithm paints the image pixels with a flooding process from an initial pixel
A to a sequence of pixels (A1, A2, . . . A8) around A. The stopping condition occurs if on the ith direction the
difference between A and Ai is greater than a fixed threshold. The proposed algorithm is able to remove every label
or hand-written mark from the X-ray. The necessary parameters for the algorithm are k0, k1, k2. Parameters k1,
k2 are set, respectively, to 0, 06 and 0, 85, whereas k0 is computed according to the formula 3 by using histogram
properties such as mean μ, standard deviation σ, smoothness S, third moment γ, uniformity Uand entropy E.
k0 =α1 · μ + α2 · σ + α3 · S + α4 · γ + α5 · U + α6 · E
6(3)
with∑6
i=1 αi = 1.
B. Orientation Correction
A study reports that in radiology departments about 35% - 40% of the skeletal bone procedures have been
not performed with the conventional image orientation [34]. Therefore a hand orientation correction procedure is
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(a) (b) (c)
Fig. 3: a) Negative of the original image, b) White Pixels Identified by the local enhancement algorithm image, c)
Output Image
required. Indeed, usually, in a X-ray the hand is not perfectly aligned to the vertical line of the acquired image. The
standard position of the hand has to be fixed by rotating the hand. The procedure for automatic alignment consists
of the following steps:
• Thresholding of the acquired image (Fig. 4(a)) obtaining the image shown in Fig. 4(b).
• Detection of the third finger, by using the wedge functions (described in the next section), and of its symmetry
axis (r2 line in Fig. 4(c)).
• Detection of the hand centroid (point P1 in Fig. 4(c)) and identification of a vertical line passing trough this
point (see r1 line in Fig. 4(c)).
• Detection of the angle between the line r1 and r2 and rotation of the image so that such angle is zero obtaining
the image shown in Fig. 4(d).
IV. EMROI ANALYSIS SYSTEM
The TW2 method is based on the clinical observations that certain regions are sensitive to skeletal maturation.
Indeed, size and shape of the epiphyses indicate the stage of maturation until it reaches the edge of the metaphyses.
The fusion between the two bones increases when the patient gets older. The EMROI Analysis systems starts with
the localization and the extraction of the thumb, the 3rd and the 5th finger. Each finger, because of the anatomical
differences, is extracted by an ad hoc algorithm. After that, the EMROIs of the TW2 method are extracted by
taking into account the gray level profiles of the detected fingers as shown in [33] and in [36]. Finally, EMROI
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(a) (b) (c) (d)
Fig. 4: a) Original left hand image, b) Thresholded Image, c) Angle scanning and d) Rotated Image
enhancement, carried out by DoG (difference of Gaussian) filter, and geometrical features extraction for TW2 stage
assignment is performed. The flow diagram for the EMROI System is shown in fig. 5.
Fig. 5: Functional Diagram of the EMROIs Analysis System .
A. EMROI Extraction
The 3rd and the 5th finger are extracted with high accuracy since they are easy to identify and since our algorithm
suitably exploits the anatomical properties of these fingers. The procedure starts with the ”wedge functions”; a wedge
is a sort of array that contains as many elements as the pixels making the images width. In detail, the elements of
a generic wedge function are zeros in correspondence of the background, ones in correspondence of the object, as
is shown in fig. 6. The procedure, based on the peaks number of the wedge functions, finds the finger tips, width
and base points for rotation; each finger region is then rotated and extracted.
Thumb extraction is implemented by a different procedure, consisting of identifying the two points A and B
as shown in fig. 7 by the following method: starting from the bottom of the image we scan, from right to left,
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Fig. 6: Wedge functions. Fig. 7: Computing Thumb’s width.
measuring for each image row the distance between the right margin and the beginning of the hand. When this
distance becomes greater than 20% with the running average distance we have found the thumb tip. Going down
along the thumb left side we find the point A in which the soft tissue between thumb and index presents a minimum.
For thumb extraction we compute a set of distances from A to the right edge of the finger. The finger width is set
to the minimum of this set of distances.
The EMROIs extraction has been carried out by taking into account, for each finger, the long axis, as a sequence of
points not necessarily interpolated. Along this axis we compute the first order derivative to search for local maxima
in the gray level profile: these values indicate the bone borders, that cover metaphysis, epiphysis, diaphysis and
are used to extract the EMROIs. More in detail, after the finger extraction (fig. 8(a)), we find the long axis that is
composed of a sequence of points not necessarily laying on a line but equidistant from the finger sides (fig. 8(b)).
We then apply, for reducing noise, a fourth order Butterworth filter on the gray levels long axis. The first derivative
is applied to the smoothed signal in order to enhance the EMROI, as shown in fig. 8(c). Finally, by thresholding
the previous signal we obtain the filter shown in fig. 8(d), which allows us to extract the desidered EMROI. In fact,
based on the peaks of the obtained filter, distances between the middle and the distal part of the finger and between
the proximal and the middle part of the finger are calculated (fig.8(e)). If these distances are out of an anatomically
plausible range a warning message is displayed and the procedure starts again by working on the derivative of the
gray level profile.
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(a)
(b)
(c)
(d)
(e)
Fig. 8: a) Longitudinal axis of the third finger, b) Gray levels of the Longitudinal axis, c) Derivative of the Gray
levels signal, d) Obtained filter to be applied and e) Extracted EMROIs
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B. EMROI Enhancement
Once the EMROIs have been extracted, DoG filter is applied in order to facilitate the features extraction. The
DoG filter allows us to identify the soft tissue (typically appearing as a smoothed region) by using a gaussian
function (smoothing filter) with a suitable standard deviation and then to remove it by subtracting to such Gaussian
another one with a lower standard deviation (hence with a less smoothing effect). By applying at the EMROI image
(Fig. 9(a)) two Gaussian filters with different standard deviations (1 and 1.5) we obtain two images (Fig. 9(b) and
9(c)); by subtracting these two images we obtain a new image with enhanced contours (fig. 9(d)), while uniform
areas settle to an almost uniform gray value.
(a) (b) (c) (d)
Fig. 9: a) Original EMROI, b) Enhanced EMROI applying a Gaussian filter with standard deviation 1 and c) 1,5,
d) Difference Image between (b) and (c).
Then to separate bones and soft tissue we apply to a thresholding operation that transforms the histogram of the
image obtained after DoG filtering into a bimodal one by circular shifting of half range (127 for eight bits image),
as shown in fig. 10.
C. EMROI Features Analysis and TW2 stage assignment
Finally, to extract the features needed for stage assignment to each EMROI, we improve the quality of the
thresholded image by filling the holes and by better defining the contours of the previous thresholded image with
a segmentation algorithm, based on the approach proposed by [37], that uses Gibbs random fields for a priori
probability modeling combined with a Gaussian model for the conditional probabilities. Since human bones have
shapes often not convex, the above method allows us a more accurate analysis of the bones extracted, thus avoiding
to use the approximation due to the convex hulls as in [36].
To describe each detected EMROI, we extract and measure some geometrical features (fig. 12), inspired by [14],
that are compared with the same features extracted from the TW2 stage classification model. According to this
comparison a TW2 stage is assigned to the considered bone. The final result is the TW2 score assignment for the
evaluation of the skeletal bone age. More in detail, we store for each stage and for each bone a vector of features
(fig. 12):
F bonestage = [dmeta, dist m 1, dnv1, · · · , dnv5, dhepi, area1, · · · , area6] (4)
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(a) (b)
(c) (d)
Fig. 10: a) Image after DoG processing, b) Thresholded image, c) Histogram of the processed image and d) Shifted
histogram.
(a) (b) (c)
Fig. 11: a) Detected EMROI, b) Thresholded Image and c) Output of our segmentation algorithm
where dmeta is the width of the metaphysis, dnv1, · · · , dnv5 are the heights of different lines what divide the
epiphysis width in six equal parts, whereas area1, · · · , area6 are the areas of the six identified parts. Finally, dhepi
is the distance between the metaphysis and the diaphysis. The stage assignment, after the EMROI Analysis, is done
by simply calculating the minimum euclidean distance between the extracted features of the bone under analysis
and all the F bonestage stored as reference.
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Fig. 12: Features Vector for each EMROI.
V. CROI SYSTEM
The wrist is a complex anatomical region containing seven carpal bones (fig. 13), with numerous articular surfaces,
ligaments, tendons, and neurovascular structures.
Fig. 13: Carpal bones in a X-Ray image.
In the early stage carpal bones appear as a set of dense pin points on a X-ray image. While patients get older,
they increase in size until they reach their optimal size and characteristic shape. Fig. 14 shows the growth pattern
of the carpal bones of Asian males in particular from zero to seven years.
The order of appearance of carpal bones is as follows: Capitate, Hamate, Triquetral, Lunate, followed usually
by Scaphoid, then the Trapezium and the Trapezoid. Usually the carpal bones are used in clinical methods, but, as
pointed out in the introduction section, not many methods for the automatization have been proposed, due to the
fact that usually Trapezium and Trapezoid after seven years for males and five years for females are fused, see fig.
13 or fig. 14(h). In this section we propose a system for the CROI processing able to manage the fusion between
these bones. The CROIs procedure, shown in fig. 15, consists of the following steps:
• Localization of region that include the carpal bones;
• Enhancement and Extraction of the seven carpal bones;
• Trapezium/Trapezoid fusion management;
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(a) (b) (c) (d)
(e) (f) (g) (h)
Fig. 14: Growth pattern of carpal bones of Asian male from new born to 7-year-old.
• TW2 Stage assignment by using GVF-Snakes and ASM.
Fig. 15: Functional Diagram of the CROIs Processing System.
A. Carpal Bones Extraction
The first step in CROI analysis is the extraction of the carpal bones from the entire hand, performed by a suitable
application of the wedge functions. The top - right point of the carpal ROI (point 1 in fig. 16 (a)) is found by
detecting the soft tissue junction between the second finger and the thumb. Starting from this point it is easy to
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find the other point (point 2 in fig. 16 (a)). According to these two points we define a rectangle, which identifies
the region called carpal ROI as shown in fig. 16(a).
(a) (b)
Fig. 16: (a) Wedge functions for identifying the points 1 and 2 and (b) Carpal ROI extracted.
In order to better identify the single bones a DrDoG (Derivative Difference of Gaussian) filter has been
applied. The principle of this anisotropic filter is to smooth out noise locally by diffusion while at the same
time preventing diffusion across object details. This filter allows us to to better differentiate carpal bones (fig. 17)
from the background for performing an effective dynamic thresholding.
(a) (b)
Fig. 17: (a) Original Carpal ROI, (b) Enhanced ROI by applying DrDoG.
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(a) (b) (c)
Fig. 18: (a) Output of DrDoG filter, (b) Bone contours detection after the application of the Canny filter, (c) Output
of the filling algorithm.
Afterward, the single carpal bones are extracted by using a method able to identify the image edges and to fill
the closed ones. More in detail, the image contours are detected through a Canny edge detector followed by a fifth
order filter, see fig. 18 (b). Finally, a filling algorithm is applied obtaining the image shown in fig. 18 (c).
The choice of the potential carpal bones is made according to the eccentricity of the shapes: indeed we proved
experimentally that all the carpal bones have an eccentricity in the range [0.4-0.9]. Instead the identification of the
carpal bones is carried out by integrating anatomical knowledge of the hand. In detail, the algorithm starts the from
the bone with the greatest area, that is the Capitate. As far as we know this is the biggest bone and the first to
appear in chronological order. Afterward the algorithm computes all the angles between the centroid of the Capitate
and the centroids of the other extracted objects (Fig. 19). After the Capitate detection, we identify five virtual zones
(A, B, C,D, E), where potentially the other carpal bones can be located. The identification of the bones is done
by estimating the Capitate’s vertical axis (axis 1 in fig. 19) and by computing the angles between this axis and the
lines passing through the centroid of the capitate and the centroids of the other objects.
The procedure to locate Trapezium and Trapezoid is more complicated. Usually, in the first identified zone we
can extract a number of objects different from two. If we get more than two objects we eliminate the objects with
the smallest identified angles. When we obtain two objects there are two possibilities: 1) Trapezium and Trapezoid
Identified and 2) a spurious object and the Trapezium and Trapezoid fused. The differentiation is done by computing
the distance between the two identified blobs: if this distance is greater than a experimentally identified threshold
Th we eliminate the object with the smallest angle, the Trapezium and the Trapezoid are fused and the fusion
management algorithm must be carried out; otherwise we have correctly detected the two carpal bones. The flow
diagram of the procedure for carpal bones identification is shown in fig. 20.
B. Trapezium/Trapezoid Fusion Management
As pointed out in the last section, one of the main difficulties for the skeletal bone age assessment systems based
on Carpal ROI analysis is the fusion between Trapezium and Trapezoid in patients older than five years for males
or sever for females. In this paper we propose a fusion management algorithm, based on an algorithm of motion
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Fig. 19: Estimation of Angles and Positions of the carpal bones with respect to the Capitate.
detection [32], able to successfully identify the fusion and subsequently to separate the two bones for the final TW2
stage assignment.
In most situations, it is reasonable to assume that the shape of unfused bones is convex while the shape of
partially fused bones is concave. It is easy to understand that an unfused bone can reach a good fit by its convex
hull, whereas this does not hold for fused bones. Consequently, we use a quantity called compactness γ to detect
the fusion between Trapezium and Trapezoid, which is defined as follows:
γ =BL2
AO(5)
where BL is the boundary length and AO is the area of the object that identified the Trapezium/Trapezoid fusion
Let γ0 be the compactness of the identified object and γc be the compactness of its convex hull. Obviously, γ0
is always greater than γc because the area of a bone is less than the area of its convex image, whereas its boundary
length is greater than the one of its convex image. Therefore, the condition that must hold for identifying if there
is or not the fusion is:
γc
γo<< 0.9 (6)
To suppress the detected fusion, we have to separate the two bones by using an appropriate cutting line based
on the analysis of the fusion’s shape. In order to get the cutting line, we first apply an edge detection filter (Canny
filter) to the identified blob edges, then we compute a curvature function on these edges, and iteratively we smooth
them until when the number of points where the curvature is zero (zero crossing point) is equal to two. These
points represent the cutting points where the cutting line passes through.
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Fig. 20: Flow Diagram of the Carpal Bones Identification Procedure.
The formula for computing the curvature function is expressed as follows:
κ(u) =.x(u)
..y(u) − ..
x(u).y(u)√
(.x(u)2 +
.y(u)2)3
(7)
where u is the curve formed by the edges. To find the two cutting points, we use the idea of ”curve evolution ‘
as explained below. Let g(u, σ) be a 1−D Gaussian kernel of width σ, then the components of the evolved curve
Λσ may be represented by X(u, σ) and Y (u, σ) according to the properties of convolution:
X(u, σ) = x(u) ∗ g(u, σ)
Y (u, σ) = y(u) ∗ g(u, σ)
where (∗) is the convolution function. The derivatives of every component can be calculated as follows:
SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 19
Xu(u, σ) = x(u) ∗ gu(u, σ)
Xuu(u, σ) = x(u) ∗ guu(u, σ)
where gu(u, σ) and guu(u, σ) are, respectively, the first and the second derivative of the gaussian function. The
same holds for Yu(u, σ) and Yuu(u, σ). Since the exact forms of gu(u, σ) and guu(u, σ) are known, the curvature
of the evolved digital curve is given by:
κ(u, σ) =Xu(u, σ)Yuu(u, σ) − Xuu(u, σ)Yu(u, σ)
(Xu(u, σ)2 + Yu(u, σ)2)3/2(8)
As σ increases, the shape of Λσ changes (fig.21). Thus, we have to calculate several times the curvature zero
crossing points of Λσ during the curve evolution, until when the number of such points will be two. Such points
represent the “cutting points” that identify the “cutting line” as shown in fig. 22.
(a) (b) (c) (d)
Fig. 21: a) Λ1, b) Λ3, c) Λ5 and d) Λ7 with their relative curvature zero crossing points.
(a) (b)
Fig. 22: a) Trapezium/Trapezoid Fusion, b) Cutting Line detected by using the last two crossing points of the shape
Λ7 shown in fig. 21(d).
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C. TW2 Stage Assignment
After having identified all the carpal bones, we need to classify them in a TW2 stage, this is performed by applying
GVF-Snakes and Active Shape Models. In detail, the bone shape is extracted by applying the GVF-snakes, whereas
the model for each bone is made with the active shape models (ASM) by using the Greulich and Pyle ATLAS [6].
This method has a very low sensitivity to snakes model initialization and it does not suffer of an improper contour
detection when the contrast varies along the bone contour, for the following reasons:
• The correct identification of the carpal bones circumscribes, from the beginning, the regions where the snakes
have to be applied, thus reducing the errors that may arise from arbitrary positioning; indeed models such
as AAM (Active Appearance Models) [38] cannot be applied because of the complex topology of the carpal
region;
• The peculiar property of the GVF-Snakes is that they are able to ensure an effective convergence to the bones
boundary edges, regardless of their initial positioning (inside or outside the sought bone).
Finally, the fusion management algorithm allows us to correctly apply the snakes to the Trapezium and the
Trapezoid even when they are fused, as shown in fig. 23.
(a) (b) (c)
Fig. 23: GVF Snakes on a) Capitate, b) Trapezium and c) Trapezoid
For the TW2 stage assignment, we compute for each stage an ASM model, by using the images of the Greulich
and Pyle ATLAS [6]. Let CBonestage the mean shape for a specific carpal bone and a specific stage, computed as:
CBonestage =
1N
N∑i=1
CBonestage (i) (9)
where N is the total number of bones with a specific stage and CBonestage (i) is the ith shape for the specific set
bone-stage. The covariance of the specific set is given by the formula 10.
SBonestage =
1N − 1
N∑i=1
(CBonestage (i) − CBone
stage )(CBonestage (i) − CBone
stage )T (10)
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Afterward the eigensystem of the covariance matrix has been computed, the eigenvectors φi corresponding to
the t largest eigenvalues λi are put into a matrix Φ = (φi · · ·φt). The deformable model for a specific stage and a
specific bone ModelBonestage is approximated by the following formula:
ModelBonestage ≈ CBone
stage + Φb (11)
where b is the matrix describing the modes of variation derived from the training set, whose elements have to lie
in the range ±m√
λi and m is set to 2. By computing the minimum Mahalanobis distance between the extracted
CROI and by varying the shape parameters in the above limits we are able to assign a TW2 stage.
VI. MATERIALS AND METHODS
To evaluate the performance of the system in predicting the patient bone age we used a database consisting
of 106 analogical X-ray images (56 for males and 40 for females). The analogical (printed film) X-Ray images
were digitized with a conventional scanner, EPSON Expression 1680 Pro, with a resolution of 150 dpi and a color
depth of eight bits (Tagged Image File Format - TIFF). The quality of the X-ray images is not uniform, that’s why
particular attention was devoted to design and implement a robust preprocessing method and a reliable features
measurement system. A standardization of the grabbed X-ray would be very useful for reducing the complexity
of the algorithm. As pointed out in the previous section, we have two systems: one for the EMROIs and one for
CROIs. Each extracted bone from both the ephiphysial and carpal set is compared with the model evaluated by
using the Greulich and Pyle ATLAS. According to this comparison we assign to each bone a TW2 stage and hence
a numerical score. By adding all the scores for the 15 considered bones we obtain an overall maturity score. The
skeletal bone maturity is obtained by using two functions, experimentally identified, that correlate the overall TW2
score with the age maturity both for males and for females (fig. 24).
(a) (b)
Fig. 24: Correlation between the overall score and the bone age maturity for a) males and b) females
Fig. 25 shows the Graphical User Interface (GUI) of the proposed system that has been implemented by using
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Matlab 7.5. All the evaluations are done automatically (also parameters settings), even if the final result has to be
validated by the radiologists.
(a) (b)
(c)
Fig. 25: System User Interfaces for a) Preprocessing, b) EMROI Analysis and c) CROI analysis and final bone age
estimation.
Accuracy is measured in terms of: 1) EMROI and CROI Stages Assignment, 2) Mean Absolute Error (MAE)
between the final estimated bone age and the one diagnosed by two independent experts over all the 106 X-ray
images. Concerning the EMROI and CROI stages assignment we report the performances of the two systems
separately. The evaluation of the stage assignment is done by presenting each of the segmented ROIs to the two
independent radiologists, who classify the system stage assignment as Perfect, Good or Bad. This classification is
done by taking into account the differences between their own stage assignment for each bone and the one obtained
by the system according to the criteria reported in Table I.
A classification is considered correct if the radiologists mark the results as Perfect or Good. A difference of
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Perfect Good Bad
System and experts stage System and experts stage System and experts stage
assignment are exactly the same assignment differ of ±1 stage assignment differ of 2 or more stages
TABLE I: Classification of the Performances for the ROI Stage Assignment
one stage (e.g. if the expert classified it as D and our system assigned E), in the age range (0-9) years, can be
considered as a correct classification because the error of one stage in the final bone age evaluation is clinically
negligible. Similarly, an error of one stage in all the fifteen bones would produce an error in the final age estimation
that lies in the range [0.2-0.6] years, which is compatible with the average interobserver spread of 0.74 years for
the TW2 method [10]. Fig. 26 shows the bone age variations with respect to the exact value (blue line) when an
under-estimation error (black line in fig. 26(a)) and an over-estimation error (red line in fig. 26(a)) occurs over
all the fifteen bones. However it is very unlikely that a systematic over-estimation or under-estimation would take
place for all the considered bones.
(a) (b)
Fig. 26: Bone age variations when a) an under-estimation error and b) an over-estimation error occurs over all the
fifteen bones
The Absolute Error is computed as the difference between the final automatically estimated bone age and the
final estimates provided by the experts. Both for EMROI and CROI stage assignment and for overall bone age
evaluation we compare our system with implementations of other methods proposed in literature using the same
database mentioned at the beginning of this section. For each of the methods used for comparison the obtained
performances were checked for consistency with the results reported in literature, if available either for the single
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stage assessment or for the overall age estimation.
VII. RESULTS AND DISCUSSION
Table II presents the expert’s evaluation of our EMROI subsystem compared with other well-know methods based
on EMROI stage classification, such as the methods of Pietka et al. [13], [14] and Gertych et al. [29]. Over all
the X-ray images of our databases we obtain an average success rate of about 90%. This is mainly due to the
fusion among EMROI, which is not treated in our system. This performance is a very promising result, especially if
compared with the other methods that obtain an average performances rate, respectively, of 74%, 83% and 79.7%.
Expert % Perfect % Good % Bad
Our Proposed Method 1 81.2 9.4 9.4
2 83.9 6.7 9.4
Pietka1 in [13] 1 63.2 9.4 27.4
2 66.9 7.6 25.5
Pietka2 in [14] 1 66.0 9.4 24.6
2 64.2 13.2 22.6
Gertych’s method [29] 1 56.6 14.1 29.3
2 50.0 18.8 30.2
TABLE II: Expert’s Evaluation of EMROI Stage Assignment
The EMROI stage assignment was especially reliable in the subset [5-7] years for females and [7-9] years for
males, failing just in about 3% of the cases. Table III reports the same evaluation for the carpal bones. Here the
the system reaches an average success rate of about 87% and is compared with the ones proposed by Hsieh [26]
(66%) and Gertych [29] (71%).
In terms of stages assignment our system outperforms the one we proposed in [36] that was based on a less robust
EMROI and CROI segmentation algorithms and achieved an average performance on both CROIs and EMROIs of
about 83%, whereas in the current system we obtain an average success rate of about 89%. In addition to the single
stage assignment evaluation we evaluated performance in the overall bone age estimation.
Table IV reports the mean absolute error MAE and the standard deviation STD of the final assessed bone age,
computed as the difference between the system and the experts’ evaluations of the bone age over all the 106 X-Ray
images, compared with the methods of Pietka [14], Hsieh et al. [29] and Gertych et al. [26].
Our system has a mean error of 0.67±0.43 years in the worst case and of 0.25±0.31 in the best case, whereas the
other methods obtain at best a mean error of 1.34± 0.81 years and at worst a mean error 2.6± 0.59. Performances
of the implementations of all the methods we used for comparison are compatible with the ones reported in the
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Expert % Perfect % Good % Bad
Our Proposed Method 1 60.4 26.4 13.2
2 65.1 21.7 13.2
Hsieh’s Method [26] 1 51.0 14.1 34.9
2 57.5 9.4 33.1
Gertych’s Method [29] 1 56.6 18.8 24.6
2 51.8 14.2 34.0
TABLE III: Expert’s Evaluation of CROI Stage Assignment
Expert % Mean Error % STD
Our Proposed Method 1 0.67 0.43
2 0.25 0.31
Pietka2 in [14] 1 2.03 1.13
2 1.76 0.72
Hsieh’s Method [26] 1 1.49 0.46
2 1.34 0.81
Gertych’s Method [29] 1 2.6 0.59
2 1.7 1.4
TABLE IV: Mean Error and Standard Deviation (STD) Results between automatic systems and the experts’
evaluations of the bone age over all the 106 X-Ray images
original works by the respective authors; for instance our implementation of Pietka2 [14] shows an average error
of 1.89 years, whereas in the paper [14] they reported an average error (on a larger age range (1-15)), of about
1.05. This lower value was due to a rejection of about 35 X-ray images on their original database that consisted
of about 230 X-Ray images. Implementation of Hsieh’s method obtained 1.42 years average error rate, which is
comparable with the one of 1 year claimed by the authors in [26].
Even if the BoneXpert [28] reaches a very high performances (in terms of error rate and standard deviation in
the age range [2-17] years), we couldn’t compare it with our system over all the images of our database, because
it automatically rejects the low quality images. For example, the X-Ray image shown in fig. 27 was rejected by
BoneExpert (as reported in [28]), whereas our system was able to process it and got a bone age of 4.00 (if female)
SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 26
and 5.10 (if male), meanwhile the average radiologists diagnosis was 4.12 (if female) and 5.23 (if male).
(a) (b)
Fig. 27: a) X-Ray Image rejected by BoneExpert b) Preprocessed Image by our system
The error rate of the proposed system is also stable when the patients get older. This check was performed on
12 patients for which three X-Ray images at three consecutive years were available, obtaining a mean error of
0.57 ± 0.49. As an example fig. 28 shows the X-Ray images of a patient at three consecutive years. We obtain,
respectively, for the three images the following bone ages: 6.8, 6.9 and 7.2 and the radiologists identify the following
values: 1) 6.9 - 7.3 - 7.4 and 2) 7.0 - 7.2 - 7.4.
(a) (b) (c)
Fig. 28: a) X-Ray Images for a female patient in three consecutive years
SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 27
The proposed method represents a significant step forward in the automatic skeletal bone age measurement. Our
system is completely automatic and does not require a radiologist’s manual intervention. No images of our database
were rejected, thus outperforming very effective method such as BoneExpert. Since the proposed method is fully
automatic, the re-rating variability is zero, whereas clinicians intraobserver differences has 95% confidence limits
of -0.87 to +1.53 years [10]. Moreover, our method reaches very high performances in terms both of accuracy and
sensitivity to image quality. In fact the mean error is of 0.46 ± 0.37 (averaged over the two experts); whereas the
sensitivity to image quality is less than 0.2 years. Sensitivity to image quality was estimated by adding 30% of
gaussian noise on a sample of 50 X-ray images; performing the bone age evaluation and estimating the difference
with the bone age measured when noise is absent. Future work will regard the bone age assessment on patients
over 10 years old and the integration with clinical PACS.
REFERENCES
[1] A. Poznanski, R. Hernandez, K. Guire, U. Bereza, and S. Garn, “Carpal length in children–a useful measurement in the diagnosis of
rheumatoid arthritis and some concenital malformation syndromes,” Radiology, vol. 129, no. 3, pp. 661–668, 1978.
[2] R. D. Milner, M. A. Preece, and J. M. Tanner, “Growth in height compared with advancement in skeletal maturity in patients treated with
human growth hormone,” Arch. Dis. Child., vol. 55, pp. 461–466, Jun 1980.
[3] R. J. Hedge and P. Sood, “Dental Maturity as an indicator of chronological age : Radiographic evaluation of Dental age in 6 to 13 years
children of Belgaum using Demirjian Methods.,” J Indian Soc Pedo Prev Dent December (2002), vol. 20, no. 4, pp. 132–138, 2002.
[4] J. M. Tanner, J. Realy, and H. Goldstein, Assessment of Skeletal Maturity and Prediction of Adult Height (TW3 Method). New York:
Harcourt Publishers, 2001.
[5] D. Darlin, Radiography of Infants and Children. New York: Harcourt Publishers, 1979.
[6] W. W. Greulich and S. Pyle, Radiographic Atlas of Skeletal Development of Hand Wrist. Stanford University Press, 1971.
[7] A. Ortega, “Comparison of TW2 and TW3 skeletal age differences in a Brazilian population,” J. Appl. Oral Science, vol. 14, pp. 142–146,
2006.
[8] E. Pietka, L. Kaabi, M. Kuo, and H. Huang, “Feature extraction in carpal-bone analysis,” IEEE Transactions on Medical Imaging, vol. 12,
pp. 44–49, Mar 1993.
[9] M. J. Berst, L. Dolan, M. M. Bogdanowicz, M. A. Stevens, S. Chow, and E. A. Brandser, “Effect of Knowledge of Chronologic Age on the
Variability of Pediatric Bone Age Determined Using the Greulich and Pyle Standards,” Am. J. Roentgenol., vol. 176, no. 2, pp. 507–510,
2001.
[10] D. G. King, D. M. Steventon, M. P. O’Sullivan, A. M. Cook, V. P. Hornsby, I. G. Jefferson, and P. R. King, “Reproducibility of bone ages
when performed by radiology registrars: an audit of Tanner and Whitehouse I versus Greulich and Pyle methods,” Br J Radiol, vol. 67,
pp. 848–851, Sep 1994.
[11] D. Michael and A. Nelson, “HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs,” IEEE
Transactions on Medical Imaging, vol. 8, pp. 64–69, Mar 1989.
[12] G. K. Manos, A. Y. Cairns, I. W. Rickets, and D. Sinclair, “Segmenting radiographs of the hand and wrist,” Computer Methods and
Programs in Biomedicine, vol. 43, pp. 227–237, 1994.
[13] E. Pietka, A. Gertych, S. Pospiech, F. Cao, H. Huang, and V. Gilsanz, “Computer-assisted bone age assessment: image preprocessing and
epiphyseal/metaphyseal ROI extraction,” IEEE Transactions on Medical Imaging, vol. 20, pp. 715–729, Aug. 2001.
[14] E. Pietka, S. Kurkowska, G. Arkadiusz, and F. Cao, “Integration of Computer Assisted Bone Age Assessment with Alinical PACS,”
Computerized Medical Imaging and Graphics, vol. 27, pp. 217–228, 2003.
[15] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” International Journal of Computer Vision, vol. V1, pp. 321–331,
January 1988.
SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 28
[16] R. De Luis-Garcia, M. Martin-Fernandez, J. Arribas, and C. Alberola-Lopez, “A fully automatic algorithm for contour detection of bones
in hand radiographs using active contours,” in Proceedings of International Conference on Image Processing, vol. 3, pp. III–421–4 vol.2,
Sept. 2003.
[17] J. Snel, H. Venema, and C. Grimbergen, “Detection of the carpal bone contours from 3-D MR images of the wrist using a planar radial
scale-space snake,” IEEE Transactions on Medical Imaging, vol. 17, pp. 1063–1072, Dec. 1998.
[18] M. Niemeijer, B. van Ginneken, C. Maas, F. Beek, and M. Viergever, “Assessing the Skeletal Age From a Hand Radiograph: Automating
the Tanner-Whitehouse Method,” in SPIE Medical Imaging (M. Sonka and J. Fitzpatrick, eds.), vol. 5032, pp. 1197–1205, SPIE, SPIE,
2003.
[19] P. Lin, F. Zhang, Y. Yang, and Z. C., “Carpal-Bone Feature Extraction Analysis in Skeletal Age Assessment based on Deformable Model,”
Journal of Computer Science and Technology, vol. 4, no. 3, pp. 152–156, 2004.
[20] C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” Image Processing, IEEE Transactions on, vol. 7, no. 3, pp. 359–369,
1998.
[21] S. Mahmoodi, B. Sharif, E. Chester, J. Owen, and R. Lee, “Automated vision system for skeletal age assessment using knowledge based
techniques,” in Sixth International Conference on Image Processing and Its Applications, vol. 2, pp. 809–813 vol.2, Jul 1997.
[22] S. Mahmoodi, B. Sharif, E. Chester, J. Owen, and R. Lee, “Skeletal growth estimation using radiographic image processing and analysis,”
IEEE Transactions on Information Technology in Biomedicine, vol. 4, pp. 292–297, Dec. 2000.
[23] S. Aja-Fernandez, R. de Luis-Garcıa, M. A. Martın-Fernandez, and C. Alberola-Lopez, “A computational TW3 classifier for skeletal
maturity assessment: a computing with words approach,” J. of Biomedical Informatics, vol. 37, no. 2, pp. 99–107, 2004.
[24] A. Pathak and S. Pal, “Fuzzy Grammars in Syntactic Recognition of Skeletal Maturity from X-rays,” IEEE Transactions on Systems, Man
and Cybernetics, vol. 16, pp. 657–667, Sept. 1986.
[25] L. Bocchi, F. Ferrara, I. Nicoletti, and G. Valli, “An artificial neural network architecture for skeletal age assessment,” in Proceedings of
International Conference on Image Processing, vol. 1, pp. I–1077–80 vol.1, Sept. 2003.
[26] C. W. Hsieh, T. L. Jong, and C. M. Tiu, “Bone age estimation based on phalanx information with fuzzy constrain of carpals,” Med Biol
Eng Comput, vol. 45, pp. 283–295, Mar 2007.
[27] A. Tristan-Vega and J. I. Arribas, “A radius and ulna TW3 bone age assessment system,” IEEE Trans Biomed Eng, vol. 55, pp. 1463–1476,
May 2008.
[28] H. Thodberg, S. Kreiborg, A. Juul, and K. Pedersen, “The Bonexpert Method for Automated Determination of Skeletal Maturity.,” IEEE
Trans Med Imaging, vol. 28, no. 1, pp. 52–66, 2009.
[29] A. Gertych, A. Zhang, J. Sayre, S. Pospiech-Kurkowska, and H. K. Huang, “Bone age assessment of children using a digital hand atlas,”
Comput Med Imaging Graph, vol. 31, pp. 322–331, 2007.
[30] F. E. Johnston and S. B. Jahina, “The contribution of the carpal bones to the assessment of skeletal age,” Am. J. Phys. Anthropol., vol. 23,
pp. 349–354, Dec 1965.
[31] T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam, “The Use of Active Shape Models for Locating Structures in Medical Images,” in
Proceedings of 13th Int. Conf. on IPMI, (London, UK), pp. 33–47, Springer-Verlag, 1993.
[32] W. Zhang, Q. Wu, X. Yang, and X. Fang, “Multilevel Framework to Detect and Handle Vehicle Occlusion,” IEEE Transactions on
Intelligent Transportation System, vol. 9, pp. 161–174, March 2008.
[33] D. Giordano, R. Leonardi, F. Maiorana, G. Scarciofalo, and C. Spampinato, “Epiphysis and Metaphysis Extraction and Classification by
Adaptive Thresholding and Dog Filtering for Automated Skeletal Bone Age Analysis,” in Proceedings of the 29th Conference on IEEE
Engineering in Medicine and Biology Society, pp. 6551–6556, Aug. 2007.
[34] M. F. McNitt-Gray, E. Pietka, and H. K. Huang, “Image preprocessing for a picture archiving and communication system,” Invest Radiol,
vol. 27, pp. 529–535, Jul 1992.
[35] J. Zhang and H. Huang, “Automatic background recognition and removal (ABRR) in computed radiography images,” IEEE Transactions
on Medical Imaging, vol. 16, pp. 762–771, Dec. 1997.
[36] D. Giordano, C. Spampinato, G. Scarciofalo, and R. Leonardi, “Automatic Skeletal Bone Age Assessment by Integrating EMRI and CROI
Processing,” in International Workshop on MeMeA, May 2009.
[37] T. Pappas and N. Jayant, “An Adaptive Clustering Algorithm for Image Segmentation,” in ICCV88, pp. 310–315, 1988.
[38] T. F. Cootes, G. Edwards, and C. J. Taylor, “Active Appearance Models,” IEEE Trans. on PAMI, vol. 23, no. 6, pp. 681–685, 2001.