Hybrid Segmentation Algorithm using M-F based Optimization Model and Modified Fuzzy Clustering
Pavan Kumar Reddy , Dr. K. Fayaz. Srikrishna
In this paper, we propose and present an algorithm for medical image segmentation (MIS). By analyzing the current state-of-the-art related algorithms, we introduce the multi-band active contour model based limit function to make the multilayer segmentation available. With the development of image segmentation technology, the development of medical image segmentation technology also got very big, because there is no find common, accepted effect ideal is suitable for medical image segmentation method, almost existing each kind of segmentation method has application in the field of medical image segmentation. Further, with the optimized aims of being robust to the noise and avoiding the bad effluence on the result, we adopt the kernel method and new initialization curve. This model suffers from low noise robustness, and model algorithm is difficult to achieve. Integrated segmentation technology refers to two or more technology is used, combined with their own advantages, so they can on the accuracy or efficiency to as the robustness and effectiveness a concerned, our method is better than the existing medical image segmentation algorithms. Experiment analysis verifies the success of our method.
Medical Image Segmentation (MIS), Kernel Function, Modified Fuzzy Clustering, Multi-band Active Contour Model, Optimization Model.
 J. H. Kim, B. Y. Park and F. Akram, “Multipass Active Contours for an Adaptive Contour Map [J]”, Sensors, vol. 13, no. 3, (2013), pp. 3724-3738.
 C. Li, C. Y. Kao and J. C. Gore, “Minimization of Region-scalable Fitting Energy for Image Segmentation [J]”, IEEE Transactions on Image Processing, vol. 17, no. 10, (2008), pp. 1940-1949.
 B. Sridhar, K. V. V. S. Reddy, and A. M. Prasad, “Automated Medical image segmentation for detection of abnormal masses using Watershed transform and Markov random fields”, ACEEE International Journal of Signal & Image Processing, vol. 4, no. 3, (2013).
 X. Qian, “An active contour model for medical image segmentation with application to brain CT image”, Medical physics, vol. 40, no. 2, (2013), pp. 021911.
 W. Cui, “Localized FCM clustering with spatial information for medical image segmentation and bias field estimation”, Journal of Biomedical Imaging 2013 (2013), p. 13.
 X. Wang, “New Region-Scalable Discriminant and Fitting Energy Functional for Driving Geometric Active Contours in Medical Image Segmentation”, Computational and mathematical methods in medicine 2014 (2014).
 E, Corona, “An information theoretic approach to automated medical image segmentation”, SPIE Medical Imaging, International Society for Optics and Photonics, (2013).
 H. Wang and J. Wang, “An Effective Image Representation Method using Kernel Classification”.
 S. Yongxuan, X. Zhao, and G. Jun, “A Novel Kernel Classification Method via Image Novelty Detection”, ActaOpticaSinica, vol. 10, (2013), p. 028.
 Y. Chen, N. M. Nasrabadi, and T. D. Tran, “Hyperspectral image classification via kernel sparse representation”, Geoscience and Remote Sensing, IEEE Transactions on, vol. 51, no. 1, (2013), pp. 217-231.
 J. Zou, L. Chen, and CL P. Chen, “Ensemble Fuzzy C-means Clustering Algorithms based on KL-Divergence for Medical Image Segmentation”, (2013).
 A. Li, “Medical image segmentation based on Dirichlet energies and priors”, (2014).
 A. Bhagat and M. Atique, “Medical Image Retrieval using Fuzzy Connectedness Image Segmentation: A Web based System in Oracle”, Int. J. on Recent Trends in Engineering and Technology, vol. 11, no. 1, (2014).
 P. Ferrarese, Francesca, and G. Menegaz, “Performance evaluation in medical image segmentation”, Current Medical Imaging Reviews, vol. 9, no. 1, (2013), pp. 7-17.
 S.-H. Chae, “Automatic lung segmentation for large-scale medical image management”, Multimedia Tools and Applications (2014), pp. 1-17.
 M. Rastgarpour and J. Shanbehzadeh, “The Status Quo of Artificial Intelligence Methods in Automatic Medical Image Segmentation”, International Journal of Computer Theory and Engineering, vol. 5, no. 1, (2013), pp. 5-8.
 M. Strzelecki, “A software tool for automatic classification and segmentation of 2D/3D medical images”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 702, (2013), pp. 137-140.
 X. F. Wang, D. S. Huang and H. Xu, “An Efficient Local Chan–Vese Model for Image Segmentation [J]”, Pattern Recognition, vol. 43, no. 3, (2010), pp. 603-618.
[Pavan Kumar Reddy, Dr. K. Fayaz. Srikrishna (2016), Hybrid Segmentation Algorithm using M-F based Optimization Model and Modified Fuzzy Clustering, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-4, Issue-5, Page No-144-147], (ISSN 2347 - 5552). www.ijircst.org
Pavan Kumar Reddy
Research Scholar, Rayalaseema University, Kurnool, Andhra Pradesh, India