Abstract
Ischemic Stroke (IS) is usually initiated due to the neurological shortfall in human brain and which can be recognized by inspecting the periphery of the brain sections. In this paper, a two-step procedure is proposed to extract and evaluate the IS injury from brain Magnetic Resonance Image (MRI). In the initial step, Social Group Optimization and Shannon’s entropy based tri-level thresholding is executed to enhance the IS section of the test image. During the second step, enhanced IS section is then mined using the marker-controlled Watershed (WS) algorithm. The proposed practice is tested on benchmark ISLES 2015 dataset. Performance of the WS segmentation is also verified with the segmentation approaches, like seed-based region growing (SRG) and the Markov Random Field (MRF). The outcome of this study authenticates that, WS provides enhanced picture likeness indices, like Jaccard (90.34%), Dice (94.92%), FPR (7.77%) and FNR (2.65%) compared with SRG and MRF.
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References
Palani, T.K., Parvathavarthini, B., Chitra, K.: Segmentation of brain regions by integrating meta heuristic multilevel threshold with Markov random field. Curr. Med. Imaging Rev. 12(1), 4–12 (2016)
Rajinikanth, V., Raja, N.S.M., Satapathy, S.C.: Robust color image multi-thresholding using between-class variance and cuckoo search algorithm. Adv. Intel. Syst. Comput. 433, 379–386 (2016)
Havaei, M., Davy, A., Warde-Farley, D., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017). https://doi.org/10.1016/j.media.2016.05.004
Schmidt, M.A., Payne, G.S.: Radiotherapy planning using MRI. Phys. Med. Biol. 60, R323–R361 (2015). https://doi.org/10.1088/0031-9155/60/22/r323
STROKE: http://www.world-stroke.org/
Usinskas, A., Gleizniene, R.: Ischemic stroke region recognition based on ray tracing. In. Proceedings of International Baltic Electronics Conference (2006). https://doi.org/10.1109/bec.2006.311103
Kabir, Y., Dojat, M., Scherrer, B., Forbes, F., Garbay, C.: Multimodal MRI segmentation of ischemic stroke lesions. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC. Lyon, France, (2007). https://doi.org/10.1109/iembs.2007.4352610
Rajinikanth, V., Satapathy, S.C.: Arab J Sci Eng (2018). https://doi.org/10.1007/s13369-017-3053-6
Yahiaoui, A.F.Z., Bessaid, Y.: Segmentation of ischemic stroke area from CT brain images. In: International Symposium on Signal, Image, Video and Communications (ISIVC) (2016). https://doi.org/10.1109/isivc.2016.7893954
Mitra, et al.: Lesion segmentation from multimodal MRI using random forest following ischemic stroke. NeuroImage 98, 324–335 (2014)
Kanchana, R., Menaka, R.: Computer reinforced analysis for ischemic stroke recognition: a review. Indian J. Sci. Technol. 8(35), 81006 (2015)
Maier, O., et al.: ISLES 2015—A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)
Maier, O., Schröder, C., Forkert, N.D., Martinetz, T., Handels, H.: Classifiers for ischemic stroke lesion segmentation: a comparison study. PLoS ONE 10(12), e0145118 (2015)
Rajinikanth, V., Satapathy, S.C., Dey, N., Vijayarajan, R.: DWT-PCA image fusion technique to improve segmentation accuracy in brain tumor analysis, Lecture Notes in Electrical Engineering, vol. 471, pp. 453–462 (2018). https://doi.org/10.1007/978-981-10-7329-8_46
Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundam. Informaticae 41, 187–228 (2001)
Shih, F.Y., Cheng, S.: Automatic seeded region growing for color image segmentation. Image Vis. Comput. 23, 877–886 (2005)
Satapathy, S., Naik, A.: Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intell. Syst. 2(3), 173–203 (2016)
Naik, A., Satapathy, S.C., Ashour, A.S., Dey, N.: Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Comput. Appl. (2016). https://doi.org/10.1007/s00521-016-2686-9
Kannappan, P.L.: On Shannon’s entropy, directed divergence and inaccuracy. Probab. Theory Rel. Fields 22, 95–100 (1972). https://doi.org/10.1016/S0019-9958(73)90246-5
Paul, S., Bandyopadhyay, B.: A novel approach for image compression based on multi-level image thresholding using Shannon entropy and differential evolution, In: IEEE Students’ Technology Symposium (TechSym), pp. 56–61 (2014). https://doi.org/10.1109/techsym.2014.6807914
Rajinikanth, V., Satapathy, S.C., Fernandes, S.L., Nachiappan, S.: Entropy based segmentation of tumor from brain mr images—A study with teaching learning based optimization. Pattern Recogn. Lett. 94, 87–94 (2017)
Shanthakumar, P., Kumar, P.G.: Computer aided brain tumor detection system using watershed segmentation techniques. Int. J. Imaging Syst. Technol. 25(4), 297–301 (2015). https://doi.org/10.1002/ima.22147
Rajinikanth, V., Raja, N.S.M., Kamalanand, K.: Firefly algorithm assisted segmentation of tumor from brain MRI using Tsallis function and Markov random field. J. Control Eng. Appl. Inform. 19(3), 97–106 (2017)
Lu, H., Kot, A.C., Shi, Y.Q.: Distance-reciprocal distortion measure for binary document images. IEEE Signal Process. Lett. 11(2), 228–231 (2004)
Moghaddam, R.F., Cheriet, M.: A multi-scale framework for adaptive binarization of degraded document images. Pattern Recogn. 43(6), 2186–2198 (2010)
Radiopedia: Sub-acute middle cerebral artery infarct database—Case courtesy of Dr. David Cuete, Radiopaedia.org, rID: 35732
ISLES: (2015). www.isles-challenge.org
ITK-SNAP: http://www.itksnap.org/pmwiki/pmwiki.php
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Rajinikanth, V., Palani Thanaraj, K., Satapathy, S.C., Fernandes, S.L., Dey, N. (2019). Shannon’s Entropy and Watershed Algorithm Based Technique to Inspect Ischemic Stroke Wound. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_3
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