Abstract
Image segmentation is a much opted technique in the image processing arena. Fuzzy C-Means clustering method has been widely used for medical image segmentation. In the Standard FCM the cluster centers are chosen randomly, which may lead to the dismal performance of clustering. In order to overcome the drawback of the FCM, this paper proposes a Novel Coherence Particle Swarm Optimization Algorithm with Specified Scrutiny of Fuzzy C-Means (CPSO-SSFCM). In this work, the Standard Fuzzy C-Means algorithm is fine-tuned using Particle Swarm Optimization algorithm to find the optimal cluster heads for segmentation of the White Blood Cells. Following the segmentation of nucleus and cytoplasm regions, the proposed algorithm is applied for the classification and optimisation of the result using Support Vector Machine. The values obtained from this method are compared with the Standard FCM, HFCMCCE and EHFCMCCE using quality parameters like Full Reference pixel based measures PSNR, MSE and statistical measures such as sensitivity, specificity and accuracy.
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Jasmine Begum, A.R., Abdul Razak, T. (2018). A Novel Coherence Particle Swarm Optimization Algorithm with Specified Scrutiny of FCM (CPSO-SSFCM) in Detecting Leukemia for Microscopic Images. In: Ganapathi, G., Subramaniam, A., Graña, M., Balusamy, S., Natarajan, R., Ramanathan, P. (eds) Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation. ICC3 2017. Communications in Computer and Information Science, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-13-0716-4_6
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DOI: https://doi.org/10.1007/978-981-13-0716-4_6
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