Analysis of Quality Measures for Medical Image Segmentation
Performance Analysis for Quality Measures Using K means Clustering and EM Models SVD in segmentation of Medical images
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Digital Image Processing and Medical Image Processing present an exciting and dynamic part of cognitive and pattern recognition techniques. Diagnostic applications of medical images are very exciting and they throw more insight about segmentation algorithms. This monograph reflects an introductory methodology for medical image segmentation using k-means clustering and subsequent optimization of clusters by means of EM and SVD. Chapter 1 introduces the need for segmentation of medical images and focus of the research. Chapter 2 discusses the general clustering algorithm and chapter 3 discusses ...
Digital Image Processing and Medical Image Processing present an exciting and dynamic part of cognitive and pattern recognition techniques. Diagnostic applications of medical images are very exciting and they throw more insight about segmentation algorithms. This monograph reflects an introductory methodology for medical image segmentation using k-means clustering and subsequent optimization of clusters by means of EM and SVD. Chapter 1 introduces the need for segmentation of medical images and focus of the research. Chapter 2 discusses the general clustering algorithm and chapter 3 discusses the review of K-means clustering algorithm, EM models for optimization of clusters are analyzed in chapter 4. SVD optimization techniques are elucidated in the chapter 5. chapter 6 shows the overall results and discussion of this work. This monograph is concluded in chapter 7.To achieve a complete segmentation, cooperation with higher processing levels which use specific knowledge of the problem domain is necessary. This monograph is useful for all Engineering undergraduate and graduates students specializing in ECE, EEE, and computer science. This monograph will be useful reference.
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