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Image Segmentation using kNN and Region Based Active Contour Model

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image_segmentation_knn_region_acm

Image Segmentation using kNN and Region Based Active Contour Model

A framework which integrates kNN with a region-based active contour model. Classification probability scores from machine learn-ing algorithm, which are regularized using a non-linear function, are used to replace the pixel intensity values during energy minimization.

REFERENCES:

❖ Shutao Li, James T. Kwok, Hailong Zhu, Yaonan Wang, Texture classification using the support vector machines.

❖ Agus Pratondo , Chee-Kong Chui , Sim-Heng Ong, Integrating machine learning with region-based active contour models in medical image segmentation

❖ M. Tuceryan, A.K. Jain, Texture analysis, in: C.H. Chen, L.F. Pau, P.S.P. Wang (Eds.), Handbook of Pattern Recognition and Computer Vision, World Scienti c, Singapore, 1993, pp. 235–276.

❖ R. Haralick, K. Shanmugam, I. Dinstein, Textural features for image classi cation, IEEETrans. Systems Man Cybernet. 3 (1973) 610–621.

❖ J. Rogowska, Chapter 5 – overview and fundamentals of medical image segmentation, in: I.N. Bankman (Ed.), Handbook of Medical Image Processing and Analysis, second ed., Academic Press, Burlington, 2009, pp. 73–90.

❖ M. Kass, A. Witkin, D. Terzopoulos, Snakes: active contour models, Int. J. Comput. Vision 1 (4) (1988) 321–331.

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