BACKGROUND AND PURPOSE WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in...

This paper compares different algorithms for the segmentation of skin lesions in dermoscopic images. The basic segmentation algorithms compared are Thresholding techniques (Global and Adaptive), Region based techniques (K-means, Fuzzy C means, Expectation Maximization and Statistical Region Merging), Contour models (Active Contour Model and Chan Vese Model) and Spectral Clustering. Accuracy, se...

In this paper, five clustering techniques (k-means, ISODATA, merging, splitting and mean shift techniques) used for colour image segmentation are presented. Two heuristic evaluation methods (cluster validity measure VM and quality function Q) are applied. We show that evaluation functions VM and Q can be very helpful in search of best segmentation results. The best results came from k-means, me...

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...

We propose an evaluation process for segmentation which is made up of three different levels. It enables us to carry out the time consuming steps only for those segmentation methods for which a successful segmentation is foreseeable. In the first level the developer of a segmentation method does a coarse analysis of the usefulness of the individual segmentation methods by means of visual assess...

Color image segmentation Entropy k-means algorithm W-k-means algorithm a b s t r a c t In this paper, a weight selection procedure in the W-k-means algorithm is proposed based on the statistical variation viewpoint. This approach can solve the W-k-means algorithm's problem that the clustering quality is greatly affected by the initial value of weight. After the statistics of data, the weights o...

Brain MRI image segmentation is one of the most important applications of image segmentation technique, and is an important part of clinical diagnostic tools. Segmented image can help physicians to identify tumor tissues in brain, and monitor effectiveness of chemotherapy treatments. However, manual segmentation of muscle regions is not only inaccurate, but also time consuming. In this work, In...

The scope of the well-known k-means algorithm has been broadly extended with some recent results: first, the kmeans++ initialization method gives some approximation guarantees; second, the Bregman k-means algorithm generalizes the classical algorithm to the large family of Bregman divergences. The Bregman seeding framework combines approximation guarantees with Bregman divergences. We present h...