Performance Evaluation of Geometric Active Contour (GAC) and Enhanced Geometric Active Contour Segmentation Model (ENGAC) for Medical Image Segmentation

Abstract

Segmentation is an aspect of computer vision that deals with partitioning of an image into homogeneneous region. Medical image segmentation is an indispensable tool for medical image diagnoses. Geometric active contour (GAC) segmentation is one of the outstanding model used in machine learning community to solve the problem of medical image segmentation. However, It has problem of deviation from the true outline of the target feature and it generates spurious edge caused by noise that normally stop the evolution of the surface to be extracted.

In this paper, enhanced Geometric active contour was formulated by using Kernel Principal Component Analysis(KPCA) with the existing Geometric active contour segmentation model and performance evaluation of the formulated model was carried out.

Keyword: Geometric active contour, Segmentation, Medical image, Kernel Principal Component Analysis.