Top-Down Method Used for Pancreas Segmentation

Abstract

Image segmentation is actively an imperative title role in image analysis. Image segmentation is advantageous in many applications like traffic detection, surface crack identification, medical image analysis, face recognition, crop disease detection. Two Approaches are used for automatic pancreas segmentation. Top-Down and Bottom-Up approach used for CT image segmentation. In Top Down approach, Grey Level Co-occurrence Matrix, Simple Linear Iterative Clustering, Scale-Invariant Feature transform, Novel Modified Kernel fuzzy c-means clustering (NMKFCM) and Kernel Density Estimator methods used and automatic bottomup technique is used for pancreas subgrouping in C.T. scans. Top-Dow approach accuracy rate is less than bottom-up approach. Top-down approach required less time period as compare to bottom-up approach. In top-down approach, input image manually selected and processed it. KDE, NMKFCM and SIFT are used to detect feature of image. NMKFCM works on neighborhood point value. In KDE, Edge detection based on the kernel estimation of the probability density function .In SIFT, comprehensive information of local feature of image is focused.