Extracting tumor in MR brain and breast image with Kapur's entropy based Cuckoo Search Optimization and morphological reconstruction filters

• Autorzy: Sumathi R. , Venkatesulu M. , Arjunan S. P.

Identyfikatory

DOI

10.1016/j.bbe.2018.07.005

• Języki publikacji: EN

Abstrakty

EN

Magnetic Resonance Imaging (MRI) scanners are used to determine the presence of tumors in human bodies. In clinical oncology, algorithms are heavily used to analyze and identify the tumor region in the slice images produced by the MRI scanners. This article presents an unique algorithm which is developed based on Kapur's Entropy-based Cuckoo Search Optimization and Morphological Reconstruction Filters. The former is used to locate and segment the boundary of tumors, while the later to remove unwanted pixels in the slice images. The proposed method yields 97% accuracy in the identification of the exact topographical location of tumor region. It requires less computational time (about 3 milliseconds, on average) for processing. Thus the proposed method can help radiologists quickly detect the exact topographical location of tumor regions even when there are severe intensity variations and poor boundaries. The method fares well in terms also of other standard comparison metrics like entropy, eccentricity, Jaccard Index, Hausdorff distance, MSE, PSNR, precision, recall and accuracy, when compared to the existing methods including Fuzzy C Means clustering and PSO. Above all, the algorithm developed can detect the tumor regions in the MR images of both brain and breast. The method is validated using various types of MR images (T1, T2 for MRI brain, and T1 post contrast and post processed images for breast) available in the online datasets of BRATS, RIDER and Harvard.

Słowa kluczowe

EN

image segmentation; Kapur's entropy; Cuckoo Search Optimization; morphological reconstruction filter; performance measures

PL

segmentacja obrazu; algorytm kukułki; filtr rekonstrukcji morfologicznej; pomiar wydajności

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 4
• Strony: 918--930
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Sumathi R.

• Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, India

autor

Venkatesulu M.

• Department of Information Technology, Kalasalingam Academy of Research and Education, Tamil Nadu, India

autor

Arjunan S. P.

• Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Tamil Nadu, India; Biosignals Lab, RMIT University, Melbourne, Australia

Bibliografia

• [1] Yang XS, Deb S. Cuckoo search via Levy flights. Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009 India) 2009;210–4.
• [2] Yang X, Deb S. Engineering optimization by Cuckoo search. Int J Math Model Num Optim 2010;1:330–43.
• [3] Roushdy M. Comparative study of edge detection algorithms applying on gray scale noisy image using morphological filters. Int J Graph Vis Image Process 2006;6:17–23.
• [4] Pratheeba S, Sheeja Kumari V. Healthy and pathological tissue classification in MRI brain images using HCSONN algorithm. Int J Innov Res Sci Eng Technol 2016;5.
• [5] Ilunga-Mbuyamba E, Cruz-Duarte JM, Avina-Cervantes JG, Correa-Cely CR, Lindner D, Chalopind C. Active contours driven by Cuckoo Search strategy for brain tumor images segmentation. Expert Syst Appl 2016;56:59–68.
• [6] Zhang Y, Sun Y, Phillips P. A multilayer perception based smart pathological brain detection system by fractional Fourier entropy. J Med Syst 2015;40:173–84.
• [7] Dey N, Ashour AS, Beagum S, Pistola DS, Gospodinov M, Gospodinova EP, et al. Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising. J Imaging 2015;1:60–84.
• [8] Liu J, Li M, Wang J, Wu F, Liu T, Pan Y. A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci Technol 2014;19:578–95.
• [9] Ramathilagama S, Pandiyarajan R, Sathya A, Devi R, Kannan SR. Modified fuzzy c-means algorithm for segmentation of T1–T2-weighted brain MRI. J Comput Appl Math 2011;235:1578–86.
• [10] Vishnuvarthan G, Pallikonda Rajasekaran M. Segmentation of MR brain images for tumor extraction using fuzzy inference systems. Curr Med Imaging Rev 2013;9:2–6.
• [11] Krishna Priya R, Thangaraj C, Kesavadas C. Fuzzy C-means method for color image segmentation with L*U*V colour transformation. IJCSI Int J Comput Sci 2011;1. 1694-0814.
• [12] Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition. Med Phys 1993;20:1033–48.
• [13] Serra J. Image analysis and mathematical morphology. New York Academic; 1982.
• [14] Sudha MN, Selvarajan S. Selection based on enhanced Cuckoo Search for breast cancer classification in mammogram image. Feature Circuits Syst 2016;7:327–38.
• [15] Osman MA, Darwish A, Ghalwash AZ, Hassanien AE. Enhanced breast cancer diagnosis system using fuzzy clustering means approach in digital mammography. Handbook of research on machine learning innovations and trends. USA: IGI; 2017.
• [16] Gubern-Merida A, Kallenberg M, Mann RM, Mart R, Karssemeijer N. Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE J Biomed Health Inform 2015;19:349–57.
• [17] Ashraf AB, Gavenonis CS, Daye D, Mies C, Rosen MA, Kontos D. Multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression based breast cancer recurrence risk. IEEE Trans Med Imaging 2013;32:637–48.
• [18] Cascio D, Fauci F, Magro R, Raso G, Bellotti R, De Carlo F, et al. Mammogram segmentation by contour searching and mass lesions classification with neural network. IEEE Trans Nucl Sci 2006;53:2827–33.
• [19] Panetta K, Zhou Y, Agaian S, Jia HW. Nonlinear Unsharp masking for mammogram enhancement. IEEE Trans Inf Technol Biomed 2011;15:918–28.
• [20] Kapur JN, Sahoo PK, Wong AKC. A new method for graylevel picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 1985;29:273–85.
• [21] Kavitha AR, Chitra L, Kanaga R. Brain tumor segmentation using genetic algorithm with SVM classifier. Int J Adv Res Electr Electron Instrum Eng 2016;5:1468–71.
• [22] Hu Q, Yu D. Entropies of fuzzy indiscernibility relation and its operations. Int J Uncertain Fuzziness Knowl-Based Syst 2004;12:575–89.
• [23] Mahalakshmi S, Velmurugan T. Detection of brain tumor by particle swarm optimization using image segmentation. Indian J Sci Technol 2015;8:13–9.
• [24] Vadaparthi N, Yarramalle S, Suvarna Kumar G, Vamsee Krishna V. An improved medical image segmentation using charged fluid model. Int J Eng Res Appl 2012;2:666–8.
• [25] Mookiah MRK, Rajendra Acharya U, Martis RJ, Chua CK, Lim CM, Ng EYK, et al. Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading. A hybrid feature extraction approach. Knowl-Based Syst 2013;39:9–22.
• [26] Rajendra Acharya U, Fujita H, Koha JEW, Sudarshan VK, Vijayananthan A, Yeong CH, et al. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput Biol Med 2016;79:250–8.
• [27] Krishnan Mookiah MR, Rajendra Acharya U, Koh JEW, Chua CK, Tan JH, Chandran V, et al. Decision support system for age-related macular degeneration using discrete wavelet transform. Med Biol Eng Comput 2014;52:781–96.
• [28] Suresh Manic K, Krishna Priya R, Rajinikanth V. Image multithresholding based on Kapur/Tsallis entropy and firefly algorithm. Indian J Sci Technol 2016;9:1–6.
• [29] Sri Madhava Raja N, Vishnupriya R. Kapur's entropy and Cuckoo Search algorithm assisted segmentation and analysis of RGB images. Indian J Sci Technol 2016;9:1–6.
• [30] Ajala Funmilola A, Oke OA, Adedeji TO, Alade OM, Adewusi EA. Fuzzy k-c-means clustering algorithm for medical image segmentation. J Inf Eng Appl 2012;2:21–32.
• [31] Daamouche A, Melgani F, Alajlan N, Conci N. Swarm optimization of structuring elements for VHR image classification. IEEE Geosci Remote Sens Lett 2013;10:1334–8.
• [32] Ratna Raju A, Suresh P, Rajeswara Rao R. Bayesian HCS-based multi-SVNN: a classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern Biomed Eng 2018;38:646–60.
• [33] Dograa J, Jaina S, Sooda M. Segmentation of MR images using hybrid k mean-graph cut technique. Procedia Comput Sci 2018;132:775–84.
• [34] Charron O, Lallement A, Jarnet D, Noblet V, Clavier J-B, Meye P. Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 2018;95:43–54.
• [35] Ella Hassanien A, Moftah Hossam M, Taher Azar A, Shoman M. MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl Soft Comput 2014;14:62–71.
• [36] Ball CG, Butchart M, MacFarlane JK. Effect on biopsy technique of the breast imaging reporting and data system (BI-RADS) for non palpable mammographic abnormalities. Can J Surg 2002;45:259–63.
• [37] Sathya PD, Kayalvizhi R. Optimum multi level image thresholding based on Tsallis entropy method with bacterial foraging algorithm. Int J Comput Sci 2010;7:336–43.
• [38] Gonzalez RC, Woods RE. Digital image processing. 3rd ed. New Jersey: Prentice Hall, Incorpa.; 2009.
• [39] Sumathi R, Arjunan S. Towards better segmentation of object of interest using histogram equalisation and morphological reconstruction. Int J Signal Imaging Syst Eng 2014;7:189–94.
• [40] Jaccard P. The distribution of the flora in the alpine zone. New Phytol 1912;11:37–50.
• [41] Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 1993;15:850–63.
• [42] Jesorsky O, Kirchberg K, Frischholz R. Robust face detection using the Hausdorff distance audio and video-based biometric person authentication. 3rd International Conference AVBPA 2001 2001.
• [43] Kaczynski K, Mikolajczak P. Information theory based medical image processing. OPTO-Electron Rev 2003;11: 253–9.