An improvedK-means clustering algorithm for fish image segmentation

An Adaptive K-means Clustering Algorithm for Breast Image Segmentation

Breast cancer is one of the major causes of death among women. Small clusters of micro calcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. Early detection performed on X-ray mammography is the key to improve breast cancer diagnosis. In order to increase radiologist’s diagnostic performance, several computer-aided diagnosis (CAD) schemes ha...

An Improved K-means Clustering Algorithm for Image Segmentation

Image segmentation is a primary step in many computer vision applications, whose purpose is to extract information from the images to allow the discrimination among different objects of interest. This task usually involves the partitioning of the image into a number of clusters, such that the data in each cluster share similar features. This work describes a new clustering algorithm for providi...

GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

In this paper, a fast and practical GPU-based implementation of Fuzzy C-Means (FCM) clustering algorithm for image segmentation is proposed. First, an extensive analysis is conducted to study the dependency among the image pixels in the algorithm for parallelization. The proposed GPU-based FCM has been tested on digital brain simulated dataset to segment white matter(WM), gray matter(GM) and ce...

An Adaptive Clustering Algorithm For Image Segmentation

The problem of segmenting images of objects with smooth surfaces is considered. The algorithm we present is a generalization of the ,K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image. Spatial constraints are included by the use of a Gibbs random field model. Local intensity variations are accounted for in a n iterative procedu...

K - Means Clustering for Automatic Image Segmentation