Boundary detection in Medical Images using Edge Field Vector based on Law’s Texture and Canny...

7/27/2019 Boundary detection in Medical Images using Edge Field Vector based on Law’s Texture and Canny Method

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I nternational Journal of Engineer ing Trends and Technology (I JETT) - Volume4I ssue5- May 2013

ISSN: 2231-5381 http://www.ijettjournal.org 912

Boundary detection in Medical Images using Edge

Field Vector based on Law’s Texture and Canny

MethodSwetha.M

1Jyohsna.C

2

Department of E&C, KLS VDRIT, Haliyal Karnataka, india

Abstract− detecting the correct boundary in noisy images is a

difficult task. Images are used in many fields,including

surveillance, medical diagnostics and non-destrucive testing.

Edge detection and boundary detection plays a fundamental role

in image analysis. Boundaries are mainly used to detect the

outline or shape of the object.image segmentation is used to

locate objects and boundaries in images and it assigns a lable in

every pixel in an image such that pixels with the same level share

have certain virtual characteristics. The proposed edge detection

technique for detecting the boundaries of object using the

information from intensity gradient using the vector model and

texture gradient using the edge map modle.the results show that

the technique performs very well and yields better performance

than the classical contour models. The proposed method is robust

and applicable on various kind of noisy images without prior

knowledge of noise properties.

Keywords−

Boundary extraction, vector field model, edgemapping model, edge following technique, boundary detection.

I. INTRODUCTION

Boundary detection is mainly used to detect the

outline or shape of the object, so we can easily

identify objects based upon the outline or shape.

Segmentation is the process in which an image is

divided into its constitutent objects or parts. The

main goal of segmentation is to simplify and/or

change of an image representation into something is

an initial step before performing high-level tasks

such as object recognition and understanding. Imagesegmentation is typically used to locate objects and

boundaries in images. In medical

imaging,segmentation is important for feature

extraction, image measurements, and image display.

In some applications it may be useful to extract

boundaries of objects of interest from ultrasound

images[1],[2], microscopic images[3]-[5].

In recent years,there have been several new methods

to solve the problem of boundary detection, e.g.,

active contour model(ACM), geodestic active

contour(GAC) model, active contours without

edges(ACWE), gradient vector flow(GVF) snake

model, etc. the snake models have become popular

especially in boundary detection where the problemis more challenging due to the poor quality of the

images.

To overcome from this problem, we proposed a new

technique for boundary detection for ill-defined

edges in noisy images using a novel edge following.

The proposed edge following technique is based on

the vector image model and the edge map. The vector

image model provides a more complete description

of an image by considering both directions and

magnitudes of image edges. The proposed edge

vector field is generated by averaging magnitudes

and directions in the vector image. The edge map is

derived from Law’s texture feature and the Canny

edge detection. The vector image model and the edge

map are applied to select the best edges.

II. PROPOSED SYSTEM

In proposed boundary detection algorithm is used to

detect the boundary of object in an image. Boundary

extraction algorithm consists of following three phases.

1. Edge vector gradient

2. Edge mapping model

3. Edge detection technique




ISSN: 2231-5381 http://www.ijettjournal.org Page 1913

III. BLOCK DIAGRAM

A. Average edge vector field model

We exploit the edge vector field to devise a new

boundary extraction algorithm[29]. Give an

image f(x,y), the edge vector field is calculated

according to the following equations:

(i, j) = (Mx(i, j) + My(i, j) )……....(1)

(i, j)

– …….(2)

K= ⏟

( ….(3)

Fig.1. (a) original unclear image.(b) result from the edge vector field and

zoomed-in image.(c) result from the proposed average edge vector field and

zoomed-in image.

Each component is the convolution between the image and the

corresponding difference mask, i.e.,

Mx (i, j) = −Gy × f ( x, y) ≈

...............(4)

My (i, j) = Gx × f ( x, y) ≈ −

…………(5)

Where Gx and Gy are the difference masks of the Gaussian

weighted image moment vector operator in the x and ydirections, respectively,[29]

Gx ( x, y) =

√ ( )exp

…………(6)

Gy ( x, y) =

√ ( )

……….(7)

Edge vectors of an image indicate the magnitudes and

directions of edges which form a vector stream flowing

around an object. However, in an unclear image, the vectors

derived from the edge vector field may distribute randomly in

magnitude and direction. Therefore, we extend the capability

of the previous edge vector field by applying a local averaging

operation where the value of each vector is replaced by the

average of all the values in the local neighborhood, i.e.,

M (i, j) =

∑ ….(8)

D(i, j) = ∑ …………….(9)

Where Mr is the total number of pixels in the neighborhood N.

We apply a 3×3 window as the neighborhood N throughout

our research.

B. Edge Map

Edge map is edges of objects in an image derived

from Law’s texture and Canny edge detection.

1) Law’s Texture: The texture feature images of Law’s

texture are computed by convolving an input image

with each of the masks. Given a column vector L=(1,

4, 6, 4, 1)T, the 2-D mask l(i, j) used for texture

discrimination in this research is generated by L×LT

.

The output image is obtained by convolving input

image with texture mask.

2) Canny Edge detection: The canny approach to edge

detection is optimal for step edges corrupted by

white Gaussian noise. This edge detector is assumed

to be the output of a filter that reduces the noise and

locates the edges. There are four steps to detect

edges, the first step is to convolve the output image

obtained from the aforementioned Law’s texture t(i,

j) with a Gaussian filter. The second step is to

calculate the magnitude and direction of the gradient.

The third step is nonmaximal suppression to identify

edges. The last step is the thresholding algorithm to

detect and link edges. The double threshold

algorithm is used to detect and link edges.

Edge map shows some important information of edge. This

idea is exploited for extracting objects boundaries in unclear

images. Examples of the edge maps are shown in fig.2.

Fig.2.(a) Synthetic noisy image.(b) Left ventricle in the MR image.

(c) Prostate ultrasound image. (d)-(f) Corresponding edge maps derived from

Law’s texture and canny edge detection.

Input

image

Average

edge vector

field

Boundary

detected

Edge map

Edge

following

technique

Initial

position





C. Edge Following Technique

The edge following technique is performed to find

the boundary of an object. Most edge following

algorithms take into account the magnitude as

primary information for edge following. However,

the edge magnitude information is not efficient

enough for searching the correct boundary of objects

in noisy images because it can be very weak in some

contour areas.

Fig.3. Edge masks used for detecting of image

edges(normal direction constraint)

The magnitude and direction of the average edge vector field

give information of the boundary which flows around an

object. In addition, the edge map gives information of edge

which may be a part of object boundary. Hence, both average

edge vector filed and edge map are exploited in the decision

of the edge following technique. At the position (i, j) of animage, the successive positions of the edges are then

calculated by a 3×3 matrix.

Lij(r, c) = αMij(r, c)+ βDij(r, c)+εEij(r,c)

0 ≤ r ≤ 2, 0≤ c ≤ 2…….(10)

Where α, β and ε are the weight parameters that control the

edge to flow around an object. The larger value of an element

in Lij indicates the stronger edge in the corresponding

direction. The 3×3 matrices Mij, Dij and Eij are calculated as

follows:

Mij (r, c) =

…….(11)

Dij (r, c) = 1-|| ……(12)

Eij (r, c) = E (i + r − 1 , j + c − 1) , 0 ≤ r ≤ 2 ,

0 ≤ c ≤ 2….. (13)

Where M(i, j) and D(i, j) are the proposed average magnitude

and direction of edge vector fields as shown in (8) and (9).

E(i, j) is the edge map from Law’s texture and canny edge

detection. It should be noted that the value of each element in

the matrices M ij , Dij and E ij are

Ranged between 0 and 1. The direction can be calculated by

= ∑ ∑ ….(14)

Where k=1, 2,…….,8 denoted the eight directions as indicated

by the arrows at the center of the masks shown in fig.3.

The edge following is started from the initial position to end

position.

D. Initial positionIn this section, we present a technique for

determining a good initial position of edge following

that can be used for the boundary detection. The

initial position problem is very important in the

classical contour models. Snake models can converge

to a wrong boundary if the initial position is not close

enough to the desired boundary. Finding the initial

position of the classical contour models is still

difficult and time consuming[32], [33]. In this

proposed technique,the initial position of edge

following is determined by the following steps. Thefirst step is to calculate the average magnitude [ M(i,

j)] using (8). the second step is to calculate the

density of edge length for each pixel from an edge

map. An edge map [ E(i, j)], as a binary image, is

obtained by Law’s texture and Canny edge detection.

The idea of using density is to obtain measurement of

the edge length. The density of edge length [ L(i, j)] in

each pixel can be calculated from

L(i, j) =

.........................(15)

Where C(i,j) is the number of connected pixels at each

position of pixel. An example of counting the number of

connected pixels is shown in fig.4(a) and (b). The density of

edge length from the example is shown in fig.4(c). The third

step is to calculate the initial position map P(i, j) from

summation of average magnitude and density of edge length,

i.e.,

P (i, j) = …………….(16)

The last step is the thresholding of the initial position map.

We have to threshold the map in order to detect the initial

position of edge following. If P(i,j)>T max , then P(i, j) is the

initial position of egde following. We obtained the initial

position by setting Tmax to 95% of the maximum value. The

initial positions from our method are positions that are close to

the edges of interested areas.





Fig.5. (a)Aorta in cardiovascular MR image. (b) Averaged magnitude

[M(i, j)]. (c) Density of length edge [L(i, j)]. (d) Initial position map [P(i, j)]

and initial position of edge following derived by thresholding Tmax=0.95.

An example of the initial position derived from our method is

shown in fig.5 to illustrate that method can be applied to ill-

defined edges in medical images.

We can see that any one of the white circle points in the initial

position map is a good candidate to be the initial position for

our edge following technique. However, the maximum valueof the white circle points is used in this research. After

determining the suitable initial position, the proposed

technique will follow edges along the object boundary until

the closed loop contour is achieved. This causes a limitation

of the technique, i.e., the boundary must be closed loop.

IV. RESULTS

Fig1. Original image.

Fig2.Preprocessed image.

Fig3. Magnitude of the image.

Fig4. Direction of the image.

Fig5.Law’s texture output image.

Fig6.Canny magnitude of the image.





Fig7. Canny direction of the image.

Fig8.Non maxmimal suppression image.

Fig9. First thresholding image.

Fig10. Edge map of the image.

Fig11.Density of the image.

Fig12.Position image.

Fig13.Boundary detected.

V. CONCLUSION

We have designed a new edge following technique for boundary detection and applied it to object segmentation

problem in medical images. Our edge following technique

incorporates a vector image model and the edge map

information. The results of detecting the object boundaries in

noisy images show that the proposed technique is much better

than the five contour models. We have successfully applied

the edge following technique to detect ill-defined object

boundaries in medical images. The proposed method can be

applied not only for medical imaging, but can also be applied





to any image processing problems in which ill-defined edge

detection is encountered.

ACKNOWLEDGEMENT

We sincerely thank our college, KLS VDRIT college for

humble facilities and necessary infrastructure made available

during the course of our work. We wish to express our thanksand sincere gratitude to our Principal, Head of the Department

and guide for their guidance to complete this work

successfully and enthusiastic encouragement.

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Boundary detection in Medical Images using Edge Field Vector based on Law’s Texture and Canny...

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