Medical Image Analysis Using SoftComputing Techniques

D. Jude Hemanth and J. Anitha

Abstract Soft computing methodologies have gained increasing attention overthe past years due to their suitability for problem solving in the processing andevaluation of medical data. The processing of medical data includes two majorprocesses called as segmentation and classification. Image segmentation is theprocess in which a single image is partitioned into several groups based on sim-ilarity measures. Image classification is the process in which several images arecategorized into several groups. Image segmentation techniques are normally usedfor volumetric analysis of abnormalities in medical images and classificationtechniques are used for identification of the nature of disease. In both cases, theaccuracy and convergence rate of the methodologies used must be significantlypositive. Hence, soft computing techniques are widely preferred for such appli-cations. In this chapter, the application of few soft computing techniques such asFuzzy C-Means (FCM), K-means and Support Vector Machine (SVM) for imagesegmentation and classification are explored. Brain image database and Retinalimage database are used in these experiments. The approaches are analyzed interms of some performance measures and found to be more suitable for medicalapplications.

Keywords MR images � Classification � Segmentation � Computing

D. Jude Hemanth (&) � J. AnithaDepartment of ECE, Karunya University, Karunya Nagar, Coimbatore,Tamil Nadu, Indiae-mail: jude_hemanth@rediffmail.com

J. Anithae-mail: rajivee1@rediffmail.com

S. Patnaik and B. Zhong (eds.), Soft Computing Techniquesin Engineering Applications, Studies in Computational Intelligence 543,DOI: 10.1007/978-3-319-04693-8_9, � Springer International Publishing Switzerland 2014

131

1 Introduction

Despite an ever increasing number of groups conducting research in the area ofmedical image segmentation, it remains a challenging problem [1]. Especially, thesegmentation of magnetic resonance (MR) images with a high soft tissue contrastis in focus of investigations due to its usefulness in evaluation of therapy successin treatment of brain tumors [2]. Still today the gold standard for the segmentationof abnormal brain images is manual segmentation done by medical doctors whichis a tedious task as well as prone to human error and bias [3]. A strive towardsreliable automation of this routine task is therefore highly desired by medicalpersonnel. Clustering algorithms have proven to be suitable for this task [4]. Theyare used to categorize the pixels of a digital image into a predetermined number ofclusters. This is possible in medical imaging, because for most anatomic structuresthe number of clusters will be known a priori. For example, in brain images, thereare three main structures, namely gray matter, white matter and cerebrospinalfluid. An alleged abnormal portion such as a brain tumor adds another cluster ifappropriate.

On the other hand, image classification is widely used to differentiate the normaland abnormal images. For example, in ophthalmology, Diabetic retinopathy (DR) isa disease caused by diabetes mellitus leading to severe complications in humansight and on the long run even total loss of sight [5]. Usually, symptoms are almostnon-existent during early onset of the disease. Therefore, DR often is detected justwhen considerable damage to the retina has already taken place and a remaininghampering of sight cannot be avoided any-more. Thus, there is a strong wish todetect DR early [6]. Furthermore, an automatic detection of DR is highly desired, asit can support medical doctors in the tedious task of analyzing retinal images.

Two rather popular clustering algorithms are K-Means Algorithm (KM) [7] andFuzzy C-Means Algorithm (FCM) that has been used for segmentation of brainMR images. Both algorithms have specific assets and drawbacks. However, acomparative analysis of both algorithms for the segmentation of abnormal brainMR images lacks and was accordingly conducted in this project. The algorithmswere implemented in MATLAB and they were compared with respect to seg-mentation efficiency, correspondence ratio, convergence rate and the number ofiterations needed to converge. Support Vector Machines (SVM) [8] have proven tobe essential tools for many classification problems. For binary-class classifications,SVM constructs an optimal separating hyper plane between the positive andnegative classes with the maximal margin. Accordingly, the technique of SVMshall be applied to retinal images showing different grades of DR [9]. Furthermore,the least squares version of SVM [10], Least Squares SVM (LSSVM), shall beapplied and the classification results for both methods shall be compared.

Thus, in this chapter, the K-Means and FCM are analyzed for image segmen-tation with brain images. Similarly, SVM and LSSVM are analyzed for imageclassification with retinal images. The experimental results in terms of the per-formance measures are shown in the end of this chapter.

132 D. Jude Hemanth and J. Anitha

2 Image Database

Two different types of applications are illustrated here and hence two differentdatabases are used in this work. In case of brain image segmentation, real patientMR brain image data sets (Devaki Scan Centre, Madurai, India) displaying dif-ferent abnormalities (3 9 metastase, 2 9 glioma) were used for this work. TheMR images contain an axial slice of the brain with a slice thickness of 5 mm and aT2-FLAIR weighting (TR/TE of 6160/89 ms), acquired using a Turbo Spin Echosequence. For clustering four clusters centre were used corresponding to anabnormal portion and the three main structures in the brain, white matter, greymatter and cerebrospinal fluid. For the clustered image, segmentation efficiencyand correspondence ratio were calculated and the values were averaged over 30iterations of the entire codes. Thereby, the clustering as performed with differentinitial values. Finally, the number of iterations needed to reach convergence wasregistered.

In case of retinal image classification, the MESSIDOR online database(MESSIDOR Techno Vision Project) is used. It is intended to provide scientistsworking on computer-assisted diagnoses for DR with easy access to medical ret-inal images. It serves as a platform where the scientific community can test newalgorithms. In DR four different grades of the disease are distinguished, namely:

Grade 0: mild non-proliferative abnormalitiesGrade 1: moderate proliferative abnormalitiesGrade 2: severe proliferative abnormalitiesGrade 3: proliferative abnormalities

While grade 0 is characterized by increased permeability of the vessels, grade 1and grade 2 patients will show extensive vessel closure. DR of grade 3 is finallycharacterized by growth of new blood vessels on the retina and the posteriorsurface of the eyeball. MESSIDOR provides 1,200 images that belong to three setsof 400 images each stemming from different health institutions. As the images ofone data set were distributed over four different archives each, the images had tobe sorted according to the grade of DR. The segmentation and classificationalgorithms were implemented in MATLAB (Version 2007, The MathWorks Inc.,Natick, USA) with a processor clock frequency of 2 GHz and 2 GB RAM.

3 Methodology

The clustering algorithms used for brain image segmentation are K-means andFCM whereas the algorithms used for retinal image classification are SVM andLSSVM.

Medical Image Analysis Using Soft Computing Techniques 133

3.1 Algorithms for Brain Image Segmentation

K-Means Algorithm is an unsupervised clustering algorithm. It uses a fixednumber of cluster centre, which are called centroids. Those cluster centre areinitialized using random values and are updated during the process of clusteringand iteration of the algorithm. The pixels are assigned to the clusters according tothe smallest measurable distance between a centroid and the intensity of the pixel.This assignment can mathematically be given in the form of responsibilities rk

(n):

r nð Þk ¼

1; if k nð Þ^ ¼ k� �

0; if k nð Þ^ 6¼ k� �

�������ð1Þ

where with k^(n), it is assumed that the current pixel with intensity x(n) is closest tothe mean of cluster k(n) and n denotes the total number of pixels. In the update step,the means m(k) are recalculated using the formula:

mðkÞ ¼

Pn

r nð Þk :x nð Þ

Pn

r nð Þk

ð2Þ

The algorithm will be iterated until the criterion of convergence is met, whichrequires that the deviation between the ith set of centroid and the (i ? 1)th set ofcentroid is smaller than the specified limit value.

FCM Algorithm is based on KM algorithm with the major difference that themembership values are not crisp values but fuzzy sets. Therefore, FCM clusteringis also referred to as soft clustering. The membership is not given as a yes/nodecision but in the form of a membership degree between 0 and 1. There is anobjective function that is to be minimized by the algorithm, given as:

J U; c1; c2. . .ccð Þ ¼Xc

i¼1

Xn

j¼1

umij :d

2ij ð3Þ

where ci is the centroid of the ith cluster and uij = [0; 1]. dij specifies the Euclideandistance between the ith centroid and the jth pixel intensity. ‘m’ is a weighingexponent which for this work was set to m = 2. Updating the fuzzy membershipvalues can be given as:

uij ¼1

Pck¼1

dij

dkj

� �2=m� 1ð4Þ

The centroids are then accordingly updated:

134 D. Jude Hemanth and J. Anitha

ci ¼

Pnj¼1

umij :x

j

Pnj¼1

umij

ð5Þ

As long as the criterion of convergence is not met; the process is iterated over(4) and (5).

3.2 Algorithms for Retinal Image Classification

Support Vector Machines (SVM) has been introduced by Vapnik for solvingpattern recognition problems. They are frequently used for classification. In theprocess, the data is mapped into a higher-dimensional input space and an optimalseparating hyper plane is then constructed that classifies the data. As an SVMclassifier needs training data, this method is referred to as supervised machinelearning whereas the clustering techniques belong to unsupervised machinelearning. In this section only the basics of support vector theory shall behighlighted.

Given a data set of N samples fxk; ykgNk¼1, where xk 2 \ n is the kth input data

and yk 2 f1; 1g is the kth output data. yk can also be interpreted as a labeldescribing two different statuses like \healthy or diseased’’ or \normal or abnor-mal’’. In the case of DR the distinction can be \DR grade 0 and DR grade 2’’.Binary-class classification using SVM or LSSVM aims at learning a function f :x ! y. In case that the data set is separable, a hard margin SVM is supposed to finda separating hyper plane.

f xð Þ¼ ðx; wÞ þ b ¼ 0 ð6Þ

where w is the normal to the hyperplane. Accordingly, any test point x can beassigned to the positive class only if (w; x) ? b [ 0. It will be assigned to thenegative class otherwise. This works well for linearly separable data. However, inlots of classification problems the data is not linearly separable so that furtherparameters have to be introduced to solve the problem.

4 Experimental Results and Discussions

Initially, the results of brain image segmentation are analyzed followed by theanalysis of retinal image classification.

Medical Image Analysis Using Soft Computing Techniques 135

4.1 Result Analysis of Brain Image Segmentation

The qualitative results of segmentation approaches are shown in Figs. 1, 2, 3.The qualitative analysis of the above given images shows that there is only

small difference between the two clustering algorithms. Hence, a quantitative

Fig. 1 Input MR image with metastase, manually segmented abnormal portion, clustered imagefor FCM and K-Means clustering displayed in pseudo colouring (left to right)

Fig. 2 Input MR image with metastase, manually segmented abnormal portion, clustered imagefor FCM and K-Means clustering displayed in pseudo colouring (left to right)

Fig. 3 Input MR image with metastase, manually segmented abnormal portion, clustered imagefor FCM and K-Means clustering displayed in pseudo colouring (left to right)

136 D. Jude Hemanth and J. Anitha

Table 1 Performanceanalysis of the clusteringalgorithms

Abnormality Parameter (%) KM FCM

Metastase SE 96.00 95.62CR 69.29 72.21

Metastase SE 96.28 95.73CR 70.58 72.26

Metastase SE 84.75 83.78CR 60.27 62.36

Glioma SE 98.21 98.45CR 21.50 18.67

Glioma SE 98.39 98.36CR 79.17 79.55

Average SE 94.73 95.39CR 60.16 61.01

Table 2 Convergence rateanalysis of the approaches

KM FCM

Convergence rate (s) 3.7 0.4 13.5 1.3Nr. of iterations 20 3 43 5

Table 3 Classificationresults of the proposedapproaches

Informationabout used data

SVM LSSVM SVM LSSVM

Base 1 Base 178 9 Grade 0 125 9 Grade 078 9 Grade 2 125 9 Grade 3

Correctrate 0.6581 0.6573 0.5376 0.4661Sensitivity 0.6385 0.6368 0.4661 0.4661Specificity 0.6778 0.6778 0.6091 0.6091

Base 2 Base 291 9 Grade 0 51 9 Grade 091 9 Grade 2 51 9 Grade 3

Correctrate 0.5011 0.5011 0.5267 0.5273Sensitivity 0.5207 0.5207 0.5627 0.5640Specificity 0.4815 0.4815 0.4907 0.4907

Base 3 Base 386 9 Grade 0 53 9 Grade 086 9 Grade 2 53 9 Grade 3

Correctrate 0.6647 0.6616 0.6878 0.7019Sensitivity 0.8891 0.8946 0.9269 0.9603Specificity 0.4403 0.4287 0.4487 0.4436

Medical Image Analysis Using Soft Computing Techniques 137

analysis between the two algorithms in terms of the performance measures is givenin Table 1.

From the above analysis, it is evident that the FCM algorithm is better than theK-Means (KM) algorithm. An analysis on the convergence rate of these twoapproaches is also given in Table 2.

Even though, the time requirement is slightly higher for FCM, it is highlypreferred because of the high accuracy in segmenting the abnormal portions.

4.2 Result Analysis of Retinal Image Classification

The classification results of the SVM and LSSVM are analyzed in terms of correctrate, sensitivity and specificity. Table 3 illustrates the classification results of theproposed approaches.

From the results, it is evident that the LSSVM is marginally better than theSVM approach.

5 Conclusion

In this work, both image segmentation and image classification are performed withvarious soft computing approaches. A comparative analysis of KM algorithm andFCM algorithm has been successfully conducted and it could be verified that thealgorithms tend to achieve highly useful results in segmentation of abnormal brainMR images. However, the clustering was aimed at segmenting the abnormalportion while other structures like grey and white matter were not further regarded.Future work shall therefore not only improve the coding to achieve an even highercorrespondence ratio, but also improve the entire clustering. On average, theperformance of FCM was slightly better concerning the correspondence ratio butat the cost of higher processing times and convergence rate.

In image classification, the results show that there is potential for SVM andLSSVM to serve as classification system for retinal images with different grades ofDR. Likewise, the performance of the classifiers shall be considerably improvedby finding better parameter settings.

Acknowledgments The authors wish to thank M/s. Devaki Scan Centre for their help regardingdatabase and validation.

138 D. Jude Hemanth and J. Anitha

References

1. D.J. Hemanth, D. Selvathi, J. Anitha, Artificial Intelligence Techniques for Medical ImageAnalysis, Basics, Methods, Applications (VDM Verlag, Germany, 2010)

2. D.L. Pham, C. Xu, J.L. Prince, Current methods in medical image segmentation. Annu. Rev.Biomed. Eng. 2, 315–337 (2000)

3. D.J. Hemanth, C.K.S. Vijila, J. Anitha, Fuzzy based experimental verification of significanceof skull tissue removal in brain tumor image segmentation. Int. J. Comput. Appl. 1, 56–61(2010)

4. D.J. Hemanth, D. Selvathi, J. Anitha, Effective fuzzy clustering algorithm for abnormal MRbrain image segmentation. Proceedings of IEEE International Advance ComputingConference, 2009, pp. 609–614

5. H.P. Ng, et al., Medical image segmentation using k-means clustering and improvedwatershed algorithm. Proceedings of IEEE Southwest Symposium on Image Analysis andInterpretation, 2006, pp. 61–65

6. P. Vasuda, S. Satheesh, Improved fuzzy c-means algorithm for MR brain imagesegmentation. Int. J. Comput. Sci. Eng. 2, 1713–1715 (2010)

7. P. Vijayalakshmi, K. Selvamani, M. Geetha, Segmentation of brain MRI using k-meansclustering algorithm. Int. J. Eng. Trends Technol. 3, 113–115 (2011)

8. C. Correa et al., A comparison of fuzzy clustering algorithms applied to feature extraction onvineyard. Proceedings of International Conference in Advances in Artificial Intelligence,2011, pp. 234–239

9. J. David, C. MacKay, Information Theory: Inference, Learning Algorithms, 4th edn.(Cambridge University Press, London, 2005)

10. M.H. Ahmad Fadzil et al., Analysis of retinal fundus images for grading of diabeticretinopathy severity. Med. Biol. Eng. Compu. 49, 693–700 (2011)

Medical Image Analysis Using Soft Computing Techniques 139