A NON INVASIVE COMPUTER AIDED DIAGNOSIS SYSTEM FOR EARLY DETECTION OF LUNG CARCINOMA IN CT MEDICAL IMAGES

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1 International Journal of Latest Trends in Engineering and Tecnology Vol.(8)Issue(4-1), pp DOI: ttp://dx.doi.org/ / e-issn: x NON INVSIVE COMPUTER IDED DIGNOSIS SYSTEM FOR ERLY DETECTION OF LUNG CRCINOM IN CT MEDICL IMGES V.I.Mebin Jose (Researc Scolar) 1, Dr.T.rumuga Maria Devi (ssistant Professor) 2 bstract-lung cancer is a potentially fatal disease caused mainly by environmental factors tat mutate genes encoding critical cell regularities proteins. Tis paper investigates te early detection of lung cancer to improve te performance of computer aided diagnosis system. Te Existing multimodal sparse representation based classification cannot produce te optimal detection rate due to te in consistency of feature selection. Moreover, te computation time is also ig. To overcome tese limitations, a novel cancer detector developed wit iger detection rate wit minimal computation time is called EHFD. Te proposed (EHFD) Enanced and Hybrid Feature based Detector outperforms well in real time because of combining optimal features from enanced and non-enanced images. Te experimental results validated tat te proposed metod improved te IEF by 19% and detection oversoot by 93% compared wit existing metods. Terefore, te proposed metod is most suitable for early estimation and diagnosis of lung cancer pretty well. Te produced results demonstrate tat te detection performance fits for diagnosis of lung cancer in early stage and integrate wit te current diagnosis system. Keywords Enanced and Hybrid based Feature Detector, Image Enancement Factor, Local binary Pattern, stationary wavelet transform I. INTRODUCTION ccording to te report from te World Healt Organization, lung cancer is te most common cause of cancer- related deat worldwide wit an estimated 27% of all cancer deats. it is te second most common cancer in bot men and women along tis Non-small cell lung cancer (NSCLC) is mostly affected cancer compared to oter kind of lung cancers. Te leading cause of cancer is deat. Every year, many people died because of lung cancer oter tan colon, breast, and prostate cancers combined. Te overall five-year relative survival rate of lung cancer for all stages is approximately 15%, but if te primary tumor is treatable, te survival rates are improved [2]. Terefore, accurate staging is critical for prognosis and appropriate management. Non-small cell lung cancer (NSCLC) is te major type of lung cancer. Conventionally, lung cancer can be categorized into four types: squamous carcinoma (SC), adenocarcinoma (C), small cell cancer (SCC), and Nuclear typia (N). Non-small cell lung cancer (NSCLC) is more predominant wen compared to varied types of lung cancer. Te key to lung cancer treatment relies on its early stage diagnosis only. Traditionally, te diagnosis of lung cancer is made by te patologist. However, te patologist s inexperience in clinical practice can cause misdiagnosis. Eventoug we ave so many records to prove ow deadly lung cancer can be, still noting is done to increase te survival rate of Non small cell lung cancer and te researc is still slow. Te two main reasons beind tis is tat very little federal funding is commited to lung cancer researc and very few cases are diagnosed at early stage wen cancer is most curable. Hence centre sould plan and excecute fund raisal especially in te lung cancer researc wic will definitely prove to be effective in te longer run Te worst part is tat te diagnosing process is time consuming and sometimes tedious wen a patologist is asked to analyze a uge volume of sample images from patients. Te accuracy of te diagnosis is related not only to te patologist s educational background and clinical experience but also is/er pysical and psycological conditions. Tus, misdiagnosis of cancer can appen even for an experienced patologist. Compared to traditional manual reader approaces, computer-aided approaces offer numerous advantages suc as reducing inter and intra-reader variability. Te best imaging tecnique to stage NSCLC is te combined positron emission tomograpy (PET) computed tomograpy (CT) using Fluoro Deoxy Glucose (FDG) [3]. Tese cancers are very ard to cure. But for te cance of survival, te early detection of tis carcinoma or cancer is very necessary wit te elp of computer aided diagnosis system. II. PROPOSED FLOW Te proposed metod provides te automated diagnosis system for lung cancer wit ig detection rate and minimum computation time. Generally tis metod consists of four major steps ie, 1) Enancement 2) Segmentation 3) Hybrid feature generation 4) Classification. Te enancement and ybrid feature generation leads to improve te detection result. Move over te computation time also reduced wit respect to generated features compared to oter existing diagnosis metods. Te skewness based image enancement will reveal te fine information to detect te cancer region pretty good. Te segmentation elps to separate te lung area from te cest wall for furter proceeding like feature generation and 1 Centre for Information Tecnology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India 2 Centre for Information Tecnology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India

2 Non Invasive Computer ided Diagnosis System For Early Detection of Lung Carcinoma in Ct Medical Images 126 classification lso te feature normalisation will be done to make all te features in a certain range also to improve e training status of te SVM classifier, and te ybrid feature set generation elps to improve te detection accuracy for better diagnosis.. ENHNCEMENT - CONTRST LIMITED DPTIVE HISTOGRM Wile performing HE, it is quite acceptable tat noise in te image gets enanced. It can bring about some sort artifacts to appear on tose regions. To constrain te presence of suc artifacts and noise, a variation of HE called CLHE can be utilized. Te amount of contrast enancement for some intensity is directly inline to te slope of te CDF function at tat intensity. Hence contrast enancement can be controlled by restricting te slope of te CDF. Te slope at eac bin is determined by its istogram eigt. In tis way controlling te eigt limits te slope of te CDF and tereby contrast enancement is acieved. Te main contrast betweeormal HE and CLHE is tat tere is one additional step to clip te istogram before te calculation of its CDF wile mapping function is performed. Following is te step by step explanation of te algoritm for tis function: Figure 1. Proposed rcitecture Based On Classifiers So wen we combine tese two matematical functions we can get te most suitable enancement factor in between zero and one. It gives te cange in te intensity of original pixels. Figure 2.2. sows te enancement result of different normal and abnormal Lung CT images. Figure 2. a) Normal, b) bnormal, c) Normal Enanced, d) bnormal Enanced image B. SEGMENTTION daptive tresolding followed by morpological operations is done to separate te lung region from cest wall, its matematically represented by F( I( T( (1) '' G( Id( F ( (2) Were, T( represents te tresold, Id( represents dilation and F ( denotes te complement of an image. Here te daptive tresolding [14] performed based on otsu tresolding, it separates te lung region from te background. Eac pixel intensity ave different tresold so tat it makes te segmentation even better. Even after tresolding, because of cange in intensities of inside lung region, some oles may be present. Inorder to fill tat region, morpological operation is performed to fill tose regions. Te morpological dilation operation done by disc structuring element wit te radius 3, so tat exact lung region can be extracted from cest wall.

3 V.I.Mebin Jose (Researc Scolar), Dr.T.rumuga Maria Devi (ssistant Professor) 127 III. FETURE GENERTION Te ybrid feature is te combination of specific set of feature descriptor wic is taken from direct and enanced images. Tese TOF (Topological Oriented Feature), POF (Pattern Oriented Feature), and FOG (Frequency Oriented Feature),are te specific set of feature descriptors wic is used to train te classifier. Tese tree set of features are discriminating te geometrical, spatial arrangements and coarse information of te images. Especially Te TOF gives te geometrical oriented description and continuous cange in sape. Te POF gives te consistency of te surface or te spatial arrangement of te gray levels. Finally te FOG elps to generate te variation in te different subband analysis in te form of frequency. Tese set of features leads to improve te classifier performance. a) Topological Oriented Features Te Topological oriented features are Delaunay Triangulation and Voronoi Diagram. Te Triangulation is a matrix representing te set of simplexes tat make up te triangulation. It defines te constrained edge data in te triangulation. Te matrix provides te number of simplexes and te amount of vertices per simplex. Te triangulation is signified by standard simplex-vertex format; eac row specifies a simplex defined by indices into X. b) Frequency Oriented Features Te wavelets used ere generate te FOF features. Te discrete wavelets allow analysing te temporal evolution of te frequency content of te image, Te SWT (Stationary Wavelet Transform) captures bot te spatial and frequency information of a signal. It analyses te image by decomposing it into a coarse estimate via low-pass filtering and into feature information via ig-pass filtering. It is adequate tat we consider four decomposition directions corresponding to 0 (D), 45 (Dd), 90 (Dv), and 135 (Dd) orientations. Te four coefficients are computed by te following Figure 3. SWT Decomposition H V D ( 0 (3) ( g0 ( 0 ( g0 (4) (5) (6) were, were, g 0 represents te low pass filter, 0 represents te ig pass filter, S refers to te input image and (n 1-2k 1 ) is te scaling factor. Te features calculated for different sub bands is given below 1 2 Energy 2 2 Dv1( (7) p q x p y q Entropy P( Dv 1 )log( Dv1 ) (8) x p 1 verage Dv1( ( 9) p q x p y q IV. FTURE NORMLISTION Tese all set of features is subject to z-score normalization. In te process of z-score normalization, te feature vector is converted to zero mean and unit variance. Te mean and standard deviation of te input vector are computed as follows: F new Fold mean (10) std

4 Non Invasive Computer ided Diagnosis System For Early Detection of Lung Carcinoma in Ct Medical Images 128 Were, Fold is te original value, Fnew is te new value and te mean and std are te mean and standard deviation of te original data range respectively. Figure. 6 sows te z-scored normalized distribution for eac of te features across te 100 cancerous and 120 normal samples used in tis study. Figure 4. Segementation of a-b) Normal c-d) bnormal Enanced lung region Figure 5. Suspecious regions of two lung images V. CLSSIFICTION Te selection of classifier is very important for accurate classification. Bot te supervised and unsupervised classifiers are used for classification but te supervised classifier outperforms te oter. Here we use SVM [13], [6] classier wit RBF kernel to classify te input feature data. Tables sow tat te classifier detection rate and sensitivity and specificity of SVM are very good compared to oter classifiers. VI. EXPERIMENT ND RESULT We ave done our proposed metod for 40 different patients wit te training data of 120 normal and 100 abnormal samples. Te detection rate exceeds 94 percentage, wic sows te optimal rate to diagnose te cancerous CT lung images. Table 1 sows te enancement acievement like PSNR, MSE and IEF. It acieves 10 for te lung images Table.1 Performance evaluation for enancement Metod PSNR MSE IEF Histogram Gaussian Hessian ,Histogram Proposed Table. 2 Comparison among classifiers Different classifier ccuracy Sensitivity KNN PNN BYES FF SVM Table.6.3 Comparison among te existing metods

5 V.I.Mebin Jose (Researc Scolar), Dr.T.rumuga Maria Devi (ssistant Professor) 129 Figure 6. Eigt FOF features among various images Figure 7. True positive vs false positive rate Figure 8. Comparison of computational time wen finding te number of feature sets among classifiers Figure 9. Comparison of detection rates (wo-tof--wit out Topological Features,w-ToF--wit Topological Features) VII. CONCLUSION In tis paper, we propose a new metod for finding te lung cancer in CT images wit te elp of SVM classifier and ybrid features. Te proposed ybrid features elps to improve te classification accuracy so tis metod is well suitable for detecting te lung cancer from te given medical image. lso tis metod performs well compared to oter conventional metods wit respect to detection rate and computation time. REFERENCES [1] Samuel. Hawkins, jo. Korecki1, yoganand balagurunatan2, yuua gu2, Virendra kumar2, satrajit basu1, lawrence(2014), predicting outcomes of nonsmall cell lung Cancer using ct image features, IEEE access 2014 [2] Ganesan, B., baleke, S., Young, R. C., Catwin, C. R., & Miles, K.. (2010). Texture analysis of non-small cell lung cancer on unenanced computed tomograpy: initial evidence for a relationsip wit tumour glucose metabolism and stage. Cancer imaging, 10(1), [3] Samala, R., Moreno, W., You, Y., & Qian, W. (2009). novel approac to nodule feature optimization on tin section toracic ct. cademic radiology, 16(4), [4] Way, T. W., Hadjiiski, L. M., Sainer, B., Can, H. P., Cascade, P. N., Kazerooni, E..,& Zou, C. (2006). Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Medical Pysics, 33(7),

6 Non Invasive Computer ided Diagnosis System For Early Detection of Lung Carcinoma in Ct Medical Images 130 [5] Lee, M. C., Boroczky, L., Sungur-Stasik, K., Cann,. D., Borczuk,. C., Kawut, S. M., & Powell, C.. (2010). Computer-aided diagnosis of pulmonary nodules using a two-step approac for feature selection and classifier ensemble construction. rtificial intelligence in medicine, 50(1), [6] Zu, Y., Tan, Y., Hua, Y., Wang, M., Zang, G., & Zang, J. (2010). Feature selection and performance evaluation of support vector macine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomograpy. Journal of digital imaging, 23(1), [7] l-kadi, O. S., & Watson, D. (2008). Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE transactions on biomedical engineering, 55(7), [8] Kido, S., Kuriyama, K., Higasiyama, M., Kasugai, T., & Kuroda, C. (2003). Fractal analysis of internal and periperal textures of small periperal broncogenic carcinomas in tin-section computed tomograpy: comparison of broncioloalveolar cell carcinomas wit nonbroncioloalveolar cell carcinomas. Journal of computer assisted tomograpy, 27(1), [9] Segal, E., Sirlin, C. B., Ooi, C., dler,. S., Gollub, J., Cen, X. & Kuo, M. D. (2007). Decoding global gene expression programs in liver cancer by noninvasive imaging. Nature biotecnology, 25(6), [10] De_niens Developer XD User Guide, Deniens G, Müncen, Germany, [11] Gu, Y., Kumar, V., Hall, L. O., Goldgof, D. B., Li, C. Y., Korn, R., &Lambin, P. (2013). utomated delineation of lung tumors from CT images using a single click ensemble segmentation approac. Pattern Recognition, 46(3), [12] H. W. Tsao and Y. C. Fan, Metod and device for IC identification, R.O.C. Patent I , Jan. 1, [13] rumuga Maria Devi, Mebin Jose V. I, Kumar Parasuraman P, novel approac for automatic Non small cell lung carcinoma in CT images. International Conference on Control, Instrumentation, Communication and Computational Tecnologies (ICCICCT), (2016). [14] rumuga Maria Devi, Implementation of Lung Cancer Nodule Feature Extraction using Tresold Tecnique, International dvanced Researc Journal in Science, Engineering and Tecnology, Vol. 3, Issue 8, ugust 2016 [15] rumuga Maria Devi, Hyperspectral Image Classification using Spatial and Spectral Features, International Journal of Scientific and Engineering Researc, Jul 2013 [16] rumuga Maria Devi,Grap Cut Based Metod for utomatic Lung Segmentation for Tuberculosis by using Screening Metod in Cest Radiograps,Ciit International journal of digital image processing Volume 7, No 09, October November 2015 [17] rumuga Maria Devi, Novel Tecnique of Resolution Enancement in Hyperspectral Images on Proposed CHLE Tecnique, Journal of Cemical and Parmaceutical Sciences (JCPS) Scopus indexed journal Volume 9, Issue 1, January-Marc 2016 [18] rumuga Maria Devi,utomatic Liver Segmentation using Mean Sift Tecniques, International Journal of dvanced Researc in Computer Engineering & Tecnology (IJRCET) Volume 3 Issue 11, November 2014 Dr. T. rumuga Maria Devi Received B.E. Degree in Electronics & Communication Engineering from Manonmaniam Sundaranar University, Tirunelveli India in 2003, M.Tec degree in Computer & Information Tecnology from Manonmaniam Sundaranar University, Tirunelveli, India in 2005 and Worked as Lecturer in department of Electronics & Communication Engineering in Sardar Raja College of Engineering and also received P.D Degree in Information Tecnology Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelveli, India in 2012 and also te ssistant Professor of Centre for Information Tecnology and Engineering of Manonmaniam Sundaranar University since November 2005 onwards. Her researc interests include Signal and Image Processing, Multimedia and Remote Communication. Mebin Jose V.I was born in Nagercoil, india, R.O.C., in He received te B.E.degree in Electronics and Communication Engineering from CSI Institute of tecnology Tovalai in 2009 and done is M.tec in 2011 at Karunya University,Coimbatore, currently e is doing Pd in Centre for Information Tecnology and Engineering, MS University, India,, His researc interests are Digital and Medical image processing,multi media communication, macine vision,macine learning,wireless communication.