Autofocus functions for tuberculosis diagnosis with conventional

Autofocus functions for tuberculosis diagnosis with conventional sputum smear microscopy

C. F. Fernandes Costa Filho*1, M. G. Fernandes Costa1and A. Kimura Júnior2 1Universidade Federal do Amazonas, Technological and Information Center, Campus Universitário, Setor Norte, Av. Gal.

Rodrigo Otavio Jordao Ramos, 3000, Aleixo, Manaus, Amazonas, 69077-000, Brasil 2Universidade Estadual do Amazonas, Escola Superior de Tecnologia, Av. Darcy Vargas, 1200, Parque 10, Manaus,

Amazonas, 69195-000,Brasil

This paper presents a systematic analysis of focus functions in conventional sputum smear microscopy for tuberculosis (TB). This is the first step in the development of automatic microscopy. Nine autofocus functions are analyzed in a set of 1200 images with varying degrees of content density. These functions were evaluated using quantitative procedures. The main accomplishment of this work was to show that an autofocus function based on variance measures produced the best results for tuberculosis images.

KeywordsSputum Smear Microscopy, Autofocus Functions, Tuberculosis Diagnosis

1. Introduction

Tubeculosis (TB) is an infectious bacterial disease caused by Mycobacterium tuberculosis, typically affecting the lungs. It is transmitted from person to person via air-borne droplets from the throat and lungs of people with the active respiratory disease. Most tuberculosis cases occur in developing or moderately developed nations, where sputum microscopy with a conventional light microscope is the primary method for diagnosing pulmonary tuberculosis [1]. Two techniques are used for TB diagnostic with sputum smear microscopy: Fluorescence microscopy and conventional microscopy. Fluorescence microscopy uses an acid-fast fluorochrome dye (eg, auramine O orauramine-rhodamine), while conventional microscopy uses thecarbolfuchsinZiehl-Neelsen - ZN or Kinyoun acid-fast stains. While the first one uses an intense light source, such as a halogen or high-pressure mercury vapor lamp, the second one uses a conventional artificial light source. Manual identification and counting of TB bacilli is a tiring process with some drawbacks. The sensitivity of tuberculosis diagnostic through manual sputum smear analysis reported in the literature varies greatly. While reported sensitivities of conventional microscopy range from 0.32 to 0.94, reported sensitivities of fluorescence microscopy range from 0.52 to 0.97. On average the specificity of fluorescence microscopy is similar to conventional microscopy and range from 0.94 to 1 [1]. In addition to the huge variability in sensitivity, the manual screening for bacillus identification is a labor-intensive task that consumes between 40 minutes and 3 hours, depending on patient’s level of infection and it is needed to analyse 40-100 images [2]. To improve these rates and speed up the process, automation of screening is necessary [2]. Automatic methods for bacilli screening were first developed for fluorescence microscopy images [3-4]. The first methods for automatic bacilli screening in conventional microscopy were published only in 2008 [5-7]. Some other methods for automatic bacilli screening were published in recent years [2,8-12]. Some authors [2,9,11] claimed that the main advantages of an automatic bacilli screening over a manual one are better reproducible values for sensitivity and specificity and a faster screening process. Automatic microscopy is accomplished through coupling an electronic camera to a microscope. Auto focusing of electronic cameras is accomplished by searching for the lens position that gives the best focused image [13]. A focused image can be thought of as one that, for a set of images captured with different microscope stages, presents the best average focus over an entire field of view. In a frequency viewpoint, a focused image can be thought of as one that has more high frequency components. It is important that samples be well prepared, resulting in thin structures, because thick samples present structures with different foci. An auto focusing process employs a focus measure and a procedure to determine the best focused image. A focus measure can be defined as follow: “First, the image for which the focus measure needs to be computed is normalized for brightness by dividing the image by its mean brightness. Then, it is convolved with a focus measure filter (FMF). Then, the energy (sum of squared values) of the filtered image is computed. This energy is the focus measure” [14]. The maximum value of the measure or function corresponds to the point of optimal focus. Many autofocus functions have been proposed in the literature [15]-[21]. Despite the variety of focus functions, there is no solution generally applicable for microscopy [15]. Table 1 shows published papers involving TB auto focusing, detailing the focus measure employed in each one.

Current Microscopy Contributions to Advances in Science and Technology (A. Méndez-Vilas, Ed.)

© 2012 FORMATEX 13

Table 1Published papers involving TB auto focusing.

Authors Microscopy Type Evaluation Functions Results

[8] FluorescenceVariance, Energy of Image Laplacian, Wavelet Transform, Autocorrelation, Variance of Log Histogram

The best results were obtained with Variance of Log Histogram Function. Other measure produced meaningful results

This paper Conventional

Brenner Gradient, Energy of Image Laplacian, Wavelet Transform, Variance, Normalized Variance, Autocorrelation, Standard Deviation-Based Correlation, Entropy, Variance of Log Histogram

The best results were obtained with Variance and Normalized Variance. Entropy was the quickest function. Wavelet function was the slower function.

[23] Conventional

Normalized Variance, Brenner Gradient, Energy of Image Laplacian, Autocorrelation, Tenembaum Gradient

The best results were obtained with Normalized Variance Function

[22] ConventionalThe best results were obtained with Energy Laplacian Function. Compared with manual focus: average difference = 1.45μm. Standard deviation = 1.88μm.

Energy of Image Laplacian, First Order Gaussian Derivative, Variance of Log Histogram

In the case of sputum smear microscopy for tuberculosis, only Russel et al [22] analyzed the autofocus in conventional sputum smear microscopy; however, only three autofocus functions were analyzed. In this paper, we present the systematic evaluation of the nine most efficient and used focus function methods published in the literature. We applied these nine functions in conventional sputum smear microscopy for tuberculosis stained with the Kinyoun method. The focus function performance is analyzed and an overall ranking was obtained.

2. Materials and methods

The acquisition of the images was accomplished at the Micobacteriologia Laboratory of National Research Institute of the Amazonian– INPA. For the development of the proposed work, an acquisition station was set up (a digital camera, a light microscope and a computer).The digital camera used in this work was a Canon PowerShot A640, which couples a 10-megapixel CCD imager sensor with a 4x optical zoom. We set up the spatial resolution to 2816 x 2112 pixels. The microscope used was a Zeiss Axioshop 40 with a magnification of 100x and numerical aperture of 1.25. The PC used had a core 2 duo 2.0 GHz with 3GB RAM. The sputum samples from patients suspected of pulmonary TB cases were prepared by the Kinyoun stain method. Three groups of images were created: the first group of images was acquired from fields of slides stained with the standard Kinyoun method, featuring high density of background content. In contrast to the first, the images of the second group were taken from fields of slides with low density background content, and in the third group images were acquired from fields of slides stained with the modified Kinyoun method, without being counterstained with methylene blue solution. These groups were created with the aim of identifying the influence of the density of background content and the absence of a counterstain in the focus of the image. Figure 1 shows examples of images from three groups used in the experiment. Each group contained 4slides. From each slide, 10 fields were selected, and from each field 10 samples were acquired with different focal lengths. The focal length step was 2.5 µm. The ten samples acquired from one field constitute one set of images. A trained operator found the sample in focus. We set the sample in focus at image five, and the sharpness decreased moving in the direction toward images one and ten. The total image sets for the experiment was 120 (40 per group)totaling 1200 images.


© 2012 FORMATEX 14

( a) (b)

(c) (d)

(e) (f)

Fig. 1. Examples of microscopy images from: (a) and (b) first group, (c) and (d) second group, (d) and (f) third group

3. Autofocus functions

The autofocus function applied to an image results in a mathematical value that shows the degree of focus for each image of the same sample. The best behavior an autofocus function should display is when a maximum value is at the best-focused image position and decreases monotonically as defocus increases. The shape of a focus function typically resembles a Gaussian curve as shown if Figure 2.

Fig.2Typical shape of a focus function. The horizontal coordinate of the graph of Figure 2 corresponds to the z position of the microscope vertical axis. To plot the focus function it is necessary to vary this z position and obtain a stack of points described in equation (1). The z position movement for obtaining the image stack is illustrated in Figure 3. Stack = {(FM1, z1), (FM2,z2),……..(FMn/2zn/2)….(FMn-1zn-1) (FMn,zn)} (1) Where: FMi = Focus measure at position zi


© 2012 FORMATEX 15

Fig. 3 Z position movement for obtaining the image stack Another attribute that the autofocus function should have is to quantify the contrast and high frequency components of an image. The nine autofocus functions used in this work can be classified into three groups: Derivative-Based, Statistics-Based, and Histogram-Based Functions. To simplify the nomenclature used in all functions ( , ) represents the gray level intensity of pixel( , ) .

3.1 Derivative-Based Functions

Functions based on a derivative assume that images in focus have more high-frequency content than defocused images. These functions are sensitive to high-frequency noise. To obtain a derivative-based measure, the image is convolved with the focus measure mask (FMM).

3.1.1 Brenner gradient [19]: This function computes the first difference between a pixel and its neighbor with a distance of 2. 1 = ∑ ∑ | ( , + 2) − ( , )| (2)

While| ( , + 1) − ( , )| ≥ ( ℎ ℎ ).

3.1.2 Energy Laplace [9]: This function implements the image convolution with Laplace mask.

2 = ∑ ( ( − 1, ) + ( + 1, ) + ( , − 1) + ( , + 1) − 4 ( , )). (3)

3.1.3 Wavelet Transformation [18]: is based on discrete wavelet transformation. The more focused the image, the larger the function. The wavelet focus function calculates the energy in low and high pass bands.

3 = ‖ ( )‖‖ ( )‖ (4)

Where ℎ ( ) is the discrete Wavelet transformation in high pass band, and ( ) is the discrete Wavelet transformation in the low pass band.

3.2 Statistics-Based Functions

Statistics-Based Functions use features such as variance and standard variation to calculate how well focused the image is. Generally Statistics-based functions are less sensitive to noise than derivative-based functions.

3.2.1 Variance [16]: This function measures the variations in the gray level among the image pixels. In this measure, bright and dark pixels have the same influence. 4 = ∑ ( ( , ) − ̅). (5)

Where ̅is the mean gray level value g, and M and N are the length and width of the image, respectively.


© 2012 FORMATEX 16

3.2.2 Normalized Variance [16]: By normalizing the function with the g, this function compensates for the differences in image brightness among different images. 5 = ∑ ( ( , ) − ̅), (6) 3.2.3 Autocorrelation [17]: This function together with the standard-deviation based correlation performs well in the presence of noise. 6 = ∑ ( , ) ( + 1, ) − ∑ ( , ) ( + 2, )., (7)

3.2.4 Standard-deviation-based correlation [17]: 7 = ∑ ( , ) ( + 1, ) − ̅, (8) 3.3 Histogram-Based Functions

Based on the assumption that focused images have a greater number of gray levels than unfocused images, the histogram-based functions analyze the distribution and frequency of image intensities.

3.3.1 Entropy (AF8) [20]: The entropy function is a measure of information content. 8 = − ∑ (9) Where is the relative frequency of gray level l.

3.3.2 Variance of log histogram [3]: This function emphasizes the bright pixels in the image by multiplying the variance by the logarithm. 9 = ∑ − log ( ) (10)

Where is the probability of the intensity level l,and = ∑ log( ) is the expected value of the log-histogram.

4. Ranking Evaluation Methodologies

The performance of the nine focus functions were evaluated according to four criteria proposed in [20], defined as: 1) Accuracy: expressed here as the number of steps by which the maximum of a particular focus function departs from

the correct focal position. 2) Range: the number of steps between the two neighboring local minima around the global maximum 3) Number of false maxima: number of spurious focus function maxima. 4) Width: computed at 50% of the height focus curve. This criterion describes the sharpness or narrowness of the

peak. Execution time was analyzed apart. In order to identify the focus functions that had the best performance in the evaluation of image focus, we used two

types of analysis. In the first analysis, called Semiquantitative analysis [20], each of these four criteria was applied to the nine focus functions. For each of the four criteria, a value from 1 (the best) to 9 (the worst) was assigned to each function and image set. The global score of a function was obtained by adding the score (Sc) for each criterion.

Quantitative analysis [21]: It is a more detailed study based on the measure of the “Euclidean distance” that separates each function from the ideal one. To do these analyses, the mean and the standard deviation of each image set criterion were obtained. After this the value of the four criteria for each image set were normalized by subtracting the corresponding mean and dividing by the standard deviation. Different criteria can now be compared because they have a mean zero and standard deviation equal to unity. For each function and for each criterion, the Euclidian distance of the ideal function was computed. The mean value of the four distances for each function was obtained.

5. Results

5.1 Semiquantitative evaluation

To illustrate the performance of semiquantitative analysis, Table 2 shows the results of the focusing functions applied to one set of images from group 1. Table 3 shows the global results in the entire image set of groups 1 and 2, and Table 4 show the global results in the entire image set of group 3.


© 2012 FORMATEX 17

Table 2 Results of Semiquantitative Analysis in One Image Set of Group 1.

Table 3 Global Results of Semiquantitative Analysis of Groups 1 and 2

Table 4 Global Results of Semiquantitative Analysis of Group 3

5.2 Quantitative Evaluation

Tables 5, 6 and 7 show quantitative results of the nine focusing function evaluations for groups 1, 2 and 3, respectively.

6. Discussion and Conclusion

The semiquantitative analysis providesa list of focus functions ranked, according to the four criteria. Considering the autofocus function of group 1 and 2, it is clear that the function Variance (AF4) and Normalized Variance (AF5) were consistently among the best function. The quantitative analysis provided an absolute indication of the adequacy of the focus function. From this analysis, in group 1, the best autofocus function was the Variance (AF4), and in group 2, it was Entropy (AF8) followed by Variance (AF4). Evaluating the results we can say that there is no significant influence on the density of background content in the definition of the best autofocus function. It can be concluded that the best function for sputum smear microscopy for tuberculosis is the Variance (AF4). The definition of variance (AF4) as the best autofocus function is noteworthy because the only article found in the literature [22] regarding tuberculosis microscopy in a bright field did not use this function in the analysis. Another conclusion drawn from the analysis is that for group 3, the best function is Entropy (AF8), since in the two

Rank Function Score

1 AF8 499 2 AF4 506 3 AF5 511 4 AF6 558 5 AF1 574 6 AF7 732 7 AF9 736 8 AF3 784 9 AF2 789

.

GROUP 1 GROUP 2

Rank Function Score Rank Function Score

1 AF6 440 1 AF5 441 2 AF5 464 2 AF4 469 3 AF4 468 3 AF8 513 4 AF1 475 4 AF1 522 5 AF8 524 5 AF6 603 6 AF2 755 6 AF7 765 7 AF7 759 7 AF9 768 8 AF3 773 8 AF3 789 9 AF9 963 9 AF2 855

.

Function Accuracy Sc Range Sc False max

Sc Width Sc Total Score

AF1 0 1 2 1 1.03 1 2 8 11 AF2 4 9 3 6 2.07 8 3 6 29 AF3 0 1 2 1 2.07 8 6 1 11 AF4 0 1 2 1 1.44 4 4 2 8 AF5 0 1 2 1 1.42 3 4 2 7 AF6 0 1 3 6 1.04 2 2 8 17 AF7 1 7 3 6 1.49 5 3 6 24 AF8 0 1 2 1 1.87 7 4 2 11 AF9 1 7 4 9 1.68 6 4 2 24

.


© 2012 FORMATEX 18

analyses, this function performed best. This finding is important because these types of images present a uniform background, facilitating the future segmentation of the bacilli Mycobacterium Tuberculosis. The fastest function was Entropy (AF8) and the slower function was the Wavelet Transformation (AF3). Table VII shows the mean execution time of all functions.

Table 5 Global Results of Quantitative Analysis of Group 1



Table 8 Mean Execution Time of All Autofocus Functions

Acknowledgements The support of CNPq (process 470972/2011-4)is gratefully acknowledged.

Function AF1 AF2 AF3 AF4 AF5 AF6 AF7 AF8 AF9

Time (s) 1.20 0.96 106.5 0.66 0.68 1.10 0.81 0.03 0.03

.

Function Accuracy Range False max Width Global Score

AF8 4.59 5.77 6.21 58.13 18.67 AF5 4.92 5.31 4.82 59.69 18.69 AF4 5.05 5.89 4.90 59.87 18.93 AF6 5.39 5.55 4.79 63.76 19.87 AF1 5.66 5.78 5.81 64.32 20.39 AF9 7.59 6.32 6.89 63.33 21.03 AF2 6.46 6.84 5.92 66.60 21.45 AF7 6.82 6.21 6.91 66.92 21.72 AF3 6.51 5.85 6.88 68.31 21.89

.

Function Accuracy Range False max

Width Global Score


.

Function Accuracy Range False max

Width Global Score


.


© 2012 FORMATEX 19

References

[1] Steingart KR, Henry M, Ng V, Hopewell PC, Ramsay A, Cunningham J, Urbanczik R, Perkins M, Abdel Aziz MA, Pai M. Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review.Lancet Infect Diseases.2006; 6: 570-581.

[2] Sotaquirá, M, Rueda L, Narvaez R. Detection and quantification of bacilli and clusters present in sputum smear samples: a novel algorithm for pulmonary tuberculosis diagnosis.Proceedings of International Conference on Digital Image Processing, 2009, 117-121.

[3] Veropoulos K, Campbell C, Learmonth G, Knight B, Simpson J. The Automated Identification of Tubercle Bacilli using Image Processing and Neural Computing Techniques.Proceedings of 8th International Conference on Artificial Neural Networks.1999;2:797-802.

[4] Forero MG, Cristóbal G.Automatic identification techniques of tuberculosis bacteria.Proc. SPIE. 2003;5203: 71–81. [5] Costa MGF., Costa Filho CFF, Sena JF, Salen J, Lima MO. Automatic identification of mycobacterium tuberculosis with

conventional light microscopy.Proceedings of the 30th Annual International Conference of the IEEE EMBS. 2008; 382-385. [6] Sadaphal P, Rao J, Comstock GW, Beg, MF. Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-

Neelsen stains. International Journal of Tuberculosis Lung Disease. 2008; 12,5: 579-582. [7] Raof, RA, Salleh Z, Sahidan SI, Mashor MY, Noor SS, Idris FM, Hasan H. ColorThresholding Method For Image Segmentation

Algorithm of Ziehl-Neelsen Sputum Slide Images.Proceedings of 5th International Conference on Electrical Engineering, Computing Science and Automatic Control.2008; 212-217.

[8] Forero MG., Sroubek F, Cristóbal G. Identification of tuberculosis bacteria based on shape and color. Real Time Imaging.2004; 10:251–262.

[9] Forero MG, Cristóbal G, Desco M. Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models, Journal of Microscopy. 2006;223:120–132.

[10] Lenseigne B, Brodin P, Christophe T, Genovesio A. Support vector machines for automatic detection of tuberculosis bacteria in confocal microscopy images.Proceedings of 4th IEEE International Symposium on Biomedical Imaging.2007; 85-88. [11] Khutlang R, Krishnan S, Dendere R, Whitelaw A, Veropoulos K, Learmonth G, Douglas, TS. Classification of Mycobacterium

tuberculosis in Images of ZN-Stained Sputum Smears.IEEE Transactions on Information Technology in Biomedicine. 2010;14,4:949-957.

[12] Makkapati V, Agrawal R, AcharyaR. Segmentation and Classification of Tuberculosis Bacilli from ZN-stained Sputum Smear Images.Proceddings of 5th Annual IEEE Conference on Automation Science and Engineering.2009;217-220.

[13] Subbarao M, Tyan JK. The Optimal Focus Measure for Passive Autofocusing and Depth-from-Focus.Proc. SPIE Conf. Viedometrics IV. 1995;2, 598: 89-99.

[14] Subbarao M, Tyan JK. Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus.IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998; 20, 8: 864-870.

[15] Sun Y, Duthaler S, Nelson BJ.Autofocusing in computer microscopy: selecting the optimal focus algorithm.Microscopy Research and Technique.2004; 65:139–149.

[16] Groen F, Young IT, Ligthart G. A comparison of different focus functions for use in autofocus algorithms. Cytometry, 1985; 12:81- 91.

[17] Vollath D. Automatic focusing by correlative methods.Journal of Microscopy.1987; 147: 279-288. [18] Kautsky J, Flusser J, Zitove B, Simberova S. A new wavelet based measure of image focus. Pattern Recognition Letters.2002;

23:1785-1794. [19] Brenner JF, Dew BS, Horton JB, King JB,Neirath PW, Sellers WD.An automated microscope for cytologic research.Journal of

Histochemistry and Cytochemistry.1971; 24:100-111. [20] Firestone L, Cook K, Culp K, Talsania N, Preston Jr K. Comparison of autofocus methods for automated

microscopy.Cytometry; 1991; 12:195-206. [21] Santos A, Solórzano CO, Vaquero JJ, Peña JM, Malpica N, Pozo F. Evaluation of autofocus functions in molecular cytogenetic

analysis. Journal of Microscopy.1997;188:264-272. [22] Russel MJ,Douglas TS.Evaluation of autofocus algorithms for tuberculosis microscopy, 29th Annual International IEEE EMBS

Conference.2007; 3489-3492. [23] Osibote OA, Dendere R, Krishnan S,Douglas TS.Automated focusing in bright-field microscopy for tuberculosis detection,

Journal of Microscopy.2010; 240, 2: 155-163.


© 2012 FORMATEX 20

Autofocus functions for tuberculosis diagnosis with conventional

Documents

Transcript of Autofocus functions for tuberculosis diagnosis with conventional