An improved K-means clustering method for cDNA microarray image segmentation

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...

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorit...

K - Means Clustering for Automatic Image Segmentation

An Improved Clustering-Based Approach for DNA Microarray Image Segmentation

DNA Microarrays are powerful techniques that are used to analyze the expression of DNA in organisms after performing experiments. One of the key issues in the experimental approaches that utilize microarrays is to extract quantitative information from the spots, which represent the genes in the experiments. In this process, separating the background from the foreground is a fundamental problem ...

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...