Efficient Active Contour and K-Means Algorithms in Image Segmentation

Segmentation of Liver in Low-Contrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms

In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a ‘Gaussian Mixture’ model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a ...

Segmentation of Photospheric Solar Images by Using c-Means,k-Means, and FCM Algorithms

An efficient Self-Organizing Active Contour model for image segmentation

Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, based on the evolution of an active contour. Among ACMs, supervised ACMs are able to exploit the information extracted from supervised examples to guide the contour evolution. However, their applicability is limited by the accuracy of the probability models they use. As a consequence, ...

segmentation of photospheric solar images by using c-means,k-means, and fcm algorithms

Directional geodesic active contour for image segmentation

By incorporating the image gradient directional information into the geodesic active contour model, we propose a novel active contour model called directional geodesic active contour, which has the advantage of selectively detecting the image edges with different gradient directions. The experiment results show the high performance of the proposed active contour in image segmentation, especiall...