Detection of Lung Cancer Tumor Using Fuzzy Local ...Detection of Lung Cancer Tumor Using Fuzzy Local Information C- means Clustering T. R. GANESH BABU1, M. TAMIL THENDRAL2 and K. VIDHYA3

Detection of Lung Cancer Tumor UsingFuzzy Local Information C- means

Clustering

T. R. GANESH BABU1, M. TAMIL THENDRAL2

and K. VIDHYA3

1ECE, Muthayammal Engineering College,Salem , TamilNadu, [email protected]

2CSE, Shri Andal Alagar College of Engineering,Chennai 603111, TamilNadu, India

[email protected], Velammal Engineering College,Chennai 600062, TamilNadu, India

[email protected]

January 6, 2018

Abstract

Background/Objective: The intention of the paperis to propose Fuzzy local information C means clustering(FLICM) is used to automatically extract tumor from thelungs.

Methods/Statistical Analysis: FLICM method isused to detect the lung cancer from the CT image herealgorithm is assessed and evaluated with 100 samples of CTimages both normal and abnormal subjects. Subsequentlythe results are tested with available documents from expertradiologist to find the sensitivity, specificity and predictivevalue.

1

International Journal of Pure and Applied MathematicsVolume 118 No. 17 2018, 389-400ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

389

Findings: Simulation is done using MATLAB/ version8.5/2015a. The accuracy, sensitivity and predictive valueachieved in proposed system are 76.39%, 88.83% and 85.93%for cancer affected area.

Improvement: The result indicates potential applica-bility of the methods for automated and objective massscreening for early detection of lungs tumor. Hence FLICMmethod is used to detect the lung tumor also which helps toidentify the exact accuracy, sensitivity and predictive value.

Key Words : Lung cancer, computed tomography im-age, Fuzzy local information C means clustering, X-ray Tu-mor.

1 Introduction

Due to abnormal multiplication of cells in the lungs, growth oftumor called lung cancer may occur. Lung cancer holds the highestnormality rates among all types of cancer (Panpaliya et al. 2015).After diagnosis it is the only type of cancer with smallest survivalrate, and the death toll keeps increasing year on year. For the pastfew decades, detection of lung cancer is a serious issue and also atough task by medical image analysis.

If detection can be done in an early stage, the survival rate ofthe patients can also be improved gradually. As the natural flow oflymph out of the lungs is exactly towards the centre of the chest,this cancer spreads mostly towards the centre of the chest. Thereare two major types of lung cancer namely: Non-small cell andsmall cell. Raw chest x-ray images do not provide much clarityin identifying lung modules. So analysis of such medical imagesbecomes complicated. Presently, when compared to plain chest x-ray, CT is more efficient in detecting and diagnosing lung cancer.

(Vijaya et.al 2014) presented a method of automatic detectionof lung cancer from ct images. In this method, detection schemeconsists of four stages: pre-processing, segmentation, feature ex-traction and classification. These four levels are used to enhancethe tumor identification precisions in image processing. (Nareshand Shettar 2014) have presented a method of Early Detection ofLung Cancer Using Neural Network Techniques. Key challenge is

2

International Journal of Pure and Applied Mathematics Special Issue

390

to remove white Gaussian noise from the CT scan image by us-ing non local mean filter and segmentation is done in the lung byOtsus thresholding method. The textural and structural featuresare extracted from the processed image in order to form featurevector. In this paper, three classifiers are involved namely SVM,ANN, and k-NN. They are applied for the detection of lung cancerand to find the severity of disease. Later comparison is made withANN, and k-NN classifier with respect to quality attributes suchas accuracy, sensitivity (recall), precision and specificity. Resultsshow that SVM achieves higher accuracy of 95.12% where ANNachieves 92.68% accuracy on the given data set and k-NN showsleast accuracy of 85.37%. (Hashemi et al.2013) presented a methodof Mass Detection in Lung CT Images Using Region Growing Seg-mentation and Decision Making Based on Fuzzy Inference Systemand Artificial Neural Network. (Gomathi and Thangaraj 2010) pre-sented method by computer aided diagnosis system for detection oflung cancer and expressed that such a diagnosing system whichuses FPMC algorithm for segmentation can improve the accuracy.Rule based technique is applied for classifying the cancer noduleafter segmentation. (Likhitkar et al. 2014) proposed automatedComputer Aided Diagnosis (CAD) system for the detection of lungnodule from the CT images. Lung nodules are classified by theCAD system. The CAD system can classify the lung nodule fromCT images by the nodule features like growth rate, density, shapeand boundary of the nodule. These values are then calculated inimage processing tool by using image enhancement, segmentation,and the feature extraction. The feature values are given as inputto classifier to differentiate the module.

(Dharmarajan and Velmurugan 2015) tried to analyze the ex-tent of lung cancer with K means(unsupervised learning) and far-thest first clustering algorithm focusing on maximum distance. Butthe focus had been with data analysis rather than analyzing thetumor cells. Similarly, (Kumar et al. 2016) discussed watershedsegmentation where the lesion size of cancer cell was denoted as 20mm in the case of normal lung and more than 20 mm in abnormalcell.

The paper is divided as follows:

• Section-2: Briefly discuss about the pre-processing methodand FLICM method.

3


391

• Section-3: It discuss about result and Discussion.

• Section-4: Conclusion will explain about accuracy, sensitivity,specificity and predictive value.

2 System overview

2.1 Pre-processing

The bone region usually affects the accuracy in segmentation. Thefirst step is to remove the bone region from the lung CT image.Then the R- Plane, G- Plane and B- Plane images are slowly sep-arated from input RGB input image. The bone regions are thendetected from these three planes and subtracted to get the resultantimage T=R-G-B (1)

T is added with difference between G-plane and B-plane to getthe enhanced bone eliminated image.

S=T+[G-B] (2)

Figure 1 shows the input RGB image. The figure 2 shows thebone eliminated image. This image is used to input of the FLICMclustering.

Figure 1: Input RGB image

4


392

Figure 2: Enhanced bone removed image

2.2 Lung Cancer Detection Using Fuzzy LocalInformation C-Means Clustering Algorithm

The standard FCM (Sopharak et al. 2009) does not take for consid-eration the spatial information of pixels and so the segmentationresult gets affected. Hence to improve the segmentation results,FLICM clustering is employed over simple FCM clustering tech-nique.

The main characteristic of a FLICM is noise immunity thatpreserves image detail and free of any parameter selection. FLICMincorporates local spatial and gray level information into its objec-tive function.

Thus in this work FLICM is used as a novel method to detecttumor from lungs CT image. This FLICM method is used by theprevious researchers (Krinidis and Chatzis, 2010) for natural imagesegmentation. Though the quality of CT image may be poorer thanthe natural image FLICM method is adopted as a novel method fortumor from lung cancer segmentation by exploiting the inter pixelcorrelation in the corresponding region.

The FLICM is applied to the images in which bone region arealready removed.

The objective function defined in terms of

5


393

Jm =∑N

i=1

∑ck=1

[Umki

∣∣∣∣xi− vk∣∣∣∣2 +Gki

](3)

Where G =fuzzy factor

Gki =∑

jεN i︸︷︷︸i 6=j

1dij+1

(1− Ukj

)m∣∣∣∣Xj − Vk∣∣∣∣2 (4)

Where the i-th pixel is the center of the local window, k is thereference cluster and the j-th pixel belongs in the set of the neigh-bors falling into a window around the i-th pixel (Nj). di,j is thespatial Euclidean distance between pixels i and j, Ukj is the degreeof membership of the j-th pixel in the k-th cluster , m is the weight-ing exponent on each fuzzy membership, and V k is the prototypeof the center of cluster k.

Uki = 1

∑Cj=1

(||xi−vk||2+Gki||xi−vj ||2+Gji

) 1m−1

(5)

Vk =∑N

i=1 Umkixi∑N

i=1 Umki

(6)

Thus, the FLICM algorithm is given as follows:Step1. Set the number c of the cluster prototypes, fuzzificationparameter m and the stopping condition ”.Step2. Initialize randomly the fuzzy partition matrix.Step3. Set the loop counter b = 0.Step4. Calculate the cluster prototypes using 6Step5. Compute membership values using 5Step6. If max U (b) U (b+1)¡ then stop, otherwise,set b = b + 1 and go to step 4.

When the algorithm has converged a defuzzification processtakes place in order to convert the fuzzy partition matrix U toa crisp partition FLICM. Figure 3 shows the result of FLICM clus-tering.

6


394

Figure 3: Result of FLICM

The clustered image consists of three forms of cluster namelybackgrounds and affected area. From these three clusters, leastcluster size is focused upon as region of interest. The Figure 4shows the detected cancer and affected area. Figure 5 shows thesuperimposed image with original RGB image.

Figure 4: Identified clustered image

7


395

Figure 5: Affected cancer area superimposed with input RGB image

3 RESULTS AND DISCUSSION

In this work up to hundred cancer affected images are used for anal-ysis. All images are 8-bit gray scale images of size 512x512pixels.In all the images, to obtain the accuracy, an area of cancer affectedarea is marked manually to determine the accuracy. Boundaries ofcancer affected area are then detected.

A simple and effective overlap measure of the ground truth re-gion is detected. This region (R) is used to measure the accuracy(M) as follows in equation 7 (Kavitha and Devi 2005).

M = AREA(T∩R)AREA(T∪R)

(7)

The other 2 accuracy measures used are

Sensitivity (S) = TPTP+FN

(8)

Specificity = TNTN+FP

(9)

Where True positive TP= R ∩ T;False Positive FP=R - (R ∩ T);

8


396

False Negative FN=T - (R ∩ T).

The number of true negatives that are not classified as tumorpixels, neither by the grader nor by the algorithm is usually veryhigh. So the specificity is mostly near100%. Since this is not so ap-preciable or meaningful, an alternative to calculate the PredictiveValue is using the formula,

PV = TPTP+FP

(10)

PV is the probability that a pixel classified as tumor is really atumor. Here the overall accuracy, sensitivity and predictive valueachieved in proposed system are 96.39%, 91.83% and 95.93% (Babuand Nandakumar 2015) for cancer affected area respectively.

4 Conclusion

A fast and reliable detection method for finding the lungs tumor hasbeen presented in this work. Lungs tumor is found using Fuzzy lo-cal information C mean clustering. Results of the proposed methodfor both normal and abnormal CT images have been presented anddiscussed. The performance of our work is observed to be goodand leads to a large scale database. We thereby conclude that ourapproach is an efficient tool to enhance the diagnostic abilities andalso add to the sensitivity of the existing techniques to improvethe performance to a greater extent. To further improve the re-sults, the number of testing images can be increased. As a futurework, incorporates PET image fused with CT image to enhance theaccuracy.

References

[1] Vijaya, G., Suhasini, A., & Priya, R. (2014). Automatic De-tection Of Lung Cancer In CT Images. International Journalof Research in Engineering and Technology.

[2] Naresh, P., & Shettar, D. R. (2014). Early Detection of LungCancer Using Neural Network Techniques. Prashant Naresh

9


397

Int. Journal of Engineering Research and Applications www.ijera. com ISSN: 2248, 9622, 78-83.

[3] Hashemi, A., Pilevar, A. H., & Rafeh, R. (2013). Mass De-tection in Lung CT Images Using Region Growing Segmenta-tion and Decision Making Based on Fuzzy Inference Systemand Artificial Neural Network. International Journal of Image,Graphics and Signal Processing, 5(6), 16.

[4] Gomathi, M., & Thangaraj, P. (2010). A computer aided di-agnosis system for lung cancer detection using support vectormachine. American Journal of Applied Sciences, 7(12), 1532.

[5] Likhitkar, M. V. K., Gawande, U., & Hajari, M. K. O. (2014)Automated Detection of Cancerous Lung Nodule from the Com-puted Tomography Images. IOSR Journals (IOSR Journal ofComputer Engineering), 1(16), 5-11.

[6] Sopharak, A., Uyyanonvara, B., & Barman, S. (2009). Auto-matic exudate detection from non-dilated diabetic retinopathyretinal images using fuzzy c-means clustering. Sensors, 9(3),2148-2161.

[7] Krinidis, S., & Chatzis, V. (2010).A robust fuzzy local infor-mation C-means clustering algorithm. IEEE Transactions onImage Processing, 19(5), 1328-1337.

[8] Kavitha, D., & Devi, S. S. (2005, January). Automatic detec-tion of optic disc and exudates in retinal images. In Proceed-ings of 2005 International Conference on Intelligent Sensingand Information Processing, 2005. (pp. 501-506). IEEE.

[9] Ramesh Babu. V and Nandakumar, A.N 2015, Lung CancerDetection Using Spatially Weighted Fuzzy C-Mean ClusteringAlgorithm International Journal of Pharmaceutical SciencesReview and Research, Article no.60, pp.321.325.

[10] Panpaliya, N., Tadas, N., Bobade, S., Aglawe, R., & Gudadhe,A. (2015). A Survey on Early Detection And Prediction OfLung Cancer. International Journal of Computer Science andMobile Computing, volume. 04 (01), pp. 174-184.

10


398

[11] Vijay, A. and Deshpande L.M. (2014), Detection of Lung Can-cer Stages on CT scan Images by Using Various Image Process-ing Techniques Journal of Computer Engineering (IOSR-JCE),Volume. 06 (05), pp. 28-35.

[12] Dharmarajan, A., & Velmurugan, T. (2015). Lung cancer dataanalysis by k-means and farthest first clustering algorithms.Indian Journal of Science and Technology, 8(15).

[13] Kumar, S. L., Swathy, M., Sathish, S., Sivaraman, J., & Ra-jasekar, M. (2016). Identification of Lung Cancer Cell usingWatershed Segmentation on CT Images. Indian Journal of Sci-ence and Technology, 9(1).

[14] G. Gasper, M. Rahman, Basic Hypergeometric Series, Cam-bridge University Press, Cambridge (1990).

[15] M. Rosenblum, Generalized Hermite polynomials and theBose-like oscillator calculus, In: Operator Theory: Advancesand Applications, Birkhauser, Basel (1994), 369-396.

[16] D.S. Moak, The q-analogue of the Laguerre polynomials, J.Math. Anal. Appl., 81 (1981), 20-47.

11


399

400

Documents

Detection of Lung Cancer Tumor Using Fuzzy Local ...Detection of Lung Cancer Tumor Using Fuzzy Local Information C- means Clustering T. R. GANESH BABU1, M. TAMIL THENDRAL2 and K. VIDHYA3