Abstract
In recent days, examination of medical images had been carried out using a number of image processing tools, specifically implemented for such purposes. This proposed work is based on a hybrid image processing technique focuses on extracting the tumour section from the brain Magnetic-Resonance-Image (MRI) recorded with various MR sequences. The proposed technique aims to identify the best possible image processing methodology for brain MRI investigation and subsequently to extract the tumour section for clinical setting. For exploring the proposed technique, most popular Radiopedia database, BraTS 2015 dataset is primarily considered for the assessment and later, real time clinical brain MRI slices are investigated. The proposed work implements Shannon Entropy (SE) objective function assisted with Social Group Optimization (SGO) algorithm to enhance the image. The results produced by SGO are compared with the other heuristic approaches like the Firefly-Algorithm (FA), Bat-Algorithm (BA) and Differential-Evolution (DE). Then Distance-Regularized-Level-Set (DRLS) segmentation technique is performed for extracting the tumour part from the enhanced slices. Further, the segmentation comparison of DRLS against traditional Active-Contour (AC) is also adopted for the evaluation. This integrated approach offers better picture-similarity-measures (PSM) compared with the alternatives.
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References
Bao S, Bermudez C, Huo Y, Parvathaneni P, Rodriguez W, Resnick SM, … Lyu I (2019) Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields. Magn Reson Imaging 59:143–152
Beagum S, Dey N, Ashour AS, Sifaki-Pistolla D, Balas VE (2017) Nonparametric de-noising filter optimization using structure-based microscopic image classification. Microsc Res Tech 80(4):419–429. https://doi.org/10.1002/jemt.22811
Brain Tumour Database (BraTS-MICCAI). http://hal.inria.fr/hal-00935640
Bresson X, Esedoḡlu S, Vandergheynst P, Thiran J-P, Osher S (2007) Fast global minimization of the active contour/snake model. J Math Imaging Vis 28(2):151–167
Chaddad A, Tanougast C (2016) Quantitative evaluation of robust skull stripping and tumour detection applied to axial MR images. Brain Inform 3(1):53–61
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277
Chan TF, Vese LA (2002) Active contour and segmentation models using geometric PDE’s for medical imaging. In: Geometric methods in bio-medical image processing, pp 63–75. https://doi.org/10.1007/978-3-642-55987-7_4
Dey N, Ashour AS, Beagum S, Pistola DS, Gospodinov M, Gospodinova EP, Tavares JMRS (2015) Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising. J Imaging 1(1):60–84. https://doi.org/10.3390/jimaging1010060
Dey N, Rajinikanth V, Ashour AS, Tavares JMRS (2018) Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry 10(20):51. https://doi.org/10.3390/sym10020051
ITK-SNAP (http://www.itksnap.org/pmwiki/pmwiki.php)
Jiang et al (2013) 3D brain tumour segmentation in multimodal MR images based on learning population-and patient-specific feature sets. Comput Med Imaging Graph 37(7–8):512–521. https://doi.org/10.1016/j.compmedimag.2013.05.007
Kamalanand K, Ramakrishnan S (2015) Effect of gadolinium concentration on segmentation of vasculature in cardiopulmonary magnetic resonance angiograms. J Med Imag Health Inf 5(1):147–151. https://doi.org/10.1166/jmihi.2015.1370
Kannappan PL (1972) On Shannon's entropy, directed divergence and inaccuracy. Probab Theory Relat Fields 22:95–100
Lakshmi VS, Tebby SG, Shriranjani D, Rajinikanth V (2016) Chaotic cuckoo search and Kapur/Tsallis approach in segmentation of T.cruzi from blood smear images. Int J Comp Sci Infor Sec (IJCSIS) 14(CIC 2016):51–56
Lu H, Kot AC, Shi YQ (2004) Distance-reciprocal distortion measure for binary document images. IEEE Signal Process Letter 11(2):228–231
Manoj RJ, Praveena MA, Vijayakumar K (2018) An ACO–ANN based feature selection algorithm for big data. Clust Comput:1–8
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, … Lanczi L (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024
Moghaddam RF, Cheriet M (2010) A multi-scale framework for adaptive binarization of degraded document images. Pattern Recogn 43(6):2186–2198
Naik A, Satapathy SC, Ashour AS, Dey N (2016) Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Comput Applic 30(1):271–287. https://doi.org/10.1007/s00521-016-2686-9
Osher S, Fedkiw RP (2001) Level set methods: an overview and some recent results. J Comput Phys 169(2):463–502
Paul S, Bandyopadhyay B (2014) A novel approach for image compression based on multi-level image thresholding using Shannon entropy and differential evolution. In: Students’ technology symposium (TechSym). IEEE, pp 56–61. https://doi.org/10.1109/TechSym.2014.6807914
Priyadharshini C, Nithysri V, Pavithra G, Raja NSM (2017) Contrast enhanced brain tumour segmentation based on Shannon's entropy and active contour. In: Third international conference on biosignals, images and instrumentation (ICBSII). IEEE, pp 1–4. https://doi.org/10.1109/ICBSII.2017.8082278
Qian X, Wang J, Guo S, Li Q (2013) An active contour model for medical image segmentation with application to brain CT image. Med Phys 40(2):021911
Raja NSM, Lakshmi PRV, Gunasekaran KP (2018) Firefly algorithm-assisted segmentation of brain regions using Tsallis entropy and Markov random field. LNNS 1:229–237. https://doi.org/10.1007/978-981-10-3812-9_24
Raja NSM, Fernandes SL, Dey N, Satapathy SC, Rajinikanth V (2018) Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J Ambient Intell Humaniz Comput:1–12. https://doi.org/10.1007/s12652-018-0854-8
Raja NSM, Arunmozhi S, Lin H, Dey N, Rajinikanth V (2019) A study on segmentation of leukocyte image with Shannon's entropy. Histopathological image analysis in medical decision making, pp 1–27. https://doi.org/10.4018/978-1-5225-6316-7.ch001
Rajinikanth V, Satapathy SC (2018) Segmentation of ischemic stroke lesion in brain MRI based on social group optimization and fuzzy-Tsallis entropy. Arab J Sci Eng 43(8):4365–4378. https://doi.org/10.1007/s13369-017-3053-6
Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumour from brain MR images–a study with teaching learning based optimization. Pattern Recogn Lett 94:87–94. https://doi.org/10.1016/j.patrec.2017.05.028
Rajinikanth V, Raja NSM, Kamalanand K (2017) Firefly algorithm assisted segmentation of tumour from brain MRI using Tsallis function and Markov random field. Control Eng Appl Inf 19(3):97–106
Rajinikanth V, Raja NSM, Satapathy SC, Fernandes SL (2017) Otsu's multi-thresholding and active contour snake model to segment dermoscopy images. J Med Imag Health Inf 7(8):1837–1840. https://doi.org/10.1166/jmihi.2017.2265
Rajinikanth V, Fernandes SL, Bhushan B, Sunder NR (2018) Segmentation and analysis of brain tumour using Tsallis entropy and regularised level set. LNEE, vol 434, pp 313–321. https://doi.org/10.1007/978-981-10-4280-5_33
Rajinikanth V, Satapathy SC, Dey N, Vijayarajan R (2018) DWT-PCA image fusion technique to improve segmentation accuracy in brain tumour analysis. LNEE, vol 471, pp 453–462. https://doi.org/10.1007/978-981-10-7329-8_46
Rajinikanth V, Satapathy SC, Dey N, Fernandes SL, Manic KS (2019) Skin melanoma assessment using Kapur’s entropy and level set—a study with bat algorithm. Smart Intelligent Computing and Applications 104:193–202. https://doi.org/10.1007/978-981-13-1921-1_19
Roopini IT, Vasanthi M, Rajinikanth V, Rekha M, Sangeetha M (2018) Segmentation of tumour from brain MRI using fuzzy entropy and distance regularised level set. LNEE, vol 490, pp 297–304. https://doi.org/10.1007/978-981-10-8354-9_27
Satapathy S, Naik A (2016) Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intell Syst 2(3):173–203
Sengupta A, Ramaniharan AK, Gupta RK, Agarwal S, Singh A (2019) Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumour components. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26704
Shriranjani D, Tebby SG, Satapathy SC, Dey N, Rajinikanth V (2018) Kapur’s entropy and active contour-based segmentation and analysis of retinal optic disc. LNEE, vol 490, pp 287–295. https://doi.org/10.1007/978-981-10-8354-9_26
Sub-acute middle cerebral artery infarct database (Case courtesy of Dr David Cuete, Radiopaedia.org, rID: 35732)
Suresh K, Sakthi U (2018) Robust multi-thresholding in noisy grayscale images using Otsu’s function and harmony search optimization algorithm. Lecture Notes in Electrical Engineering 443:491–499. https://doi.org/10.1007/978-981-10-4765-7_52
Thanaraj P, Parvathavarthini B (2017) Multichannel interictal spike activity detection using time–frequency entropy measure. Australas Phys Eng Sci Med 40(2):413–425. https://doi.org/10.1007/s13246-017-0550-6
Vaishnavi G, Jeevananthan K, Begum SR, Kamalanand K (2014) Geometrical analysis of schistosome egg images using distance regularized level set method for automated species identification. J Bioinformatics Intell Cont 3:147–152. https://doi.org/10.1166/jbic.2014.1080
Vijayakumar K, Arun C (2017) Automated risk identification using NLP in cloud based development environments. J Ambient Intell Humaniz Comput:1–13
Wang et al (2019) Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network. Appl Soft Comput 74:40–50. https://doi.org/10.1016/j.asoc.2018.10.006
Yang Y, Yan LF, Zhang X, Nan HY, Hu YC, Han Y, … Yu Y (2019) Optimizing texture retrieving model for multimodal MR image-based support vector machine for classifying glioma. J Magn Reson Imaging 49:1263–1274
Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128
Zhu Y, Wei R, Gao G, Ding L, Zhang X, Wang X, Zhang J (2019) Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. J Magn Reson Imaging 49(4):1149–1156
Acknowledgements
The clinical brain MRI data for carrying out the experimental analysis was contributed by M/S. Proscans Diagnostics Pvt. Ltd., a prominent scan Centre in Chennai. Herewith the authors of this article duly acknowledge their contribution.
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Suresh, K., Sakthi, U. A soft-computing based hybrid tool to extract the tumour section from brain MRI. Multimed Tools Appl 79, 4133–4147 (2020). https://doi.org/10.1007/s11042-019-07934-1
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DOI: https://doi.org/10.1007/s11042-019-07934-1