An accurate technique for breast tumor segmentation is a critical step for monitoring and quantifying breast cancer. The fully automated tumor segmentation in mammograms presents many challenges related to characteristics of an image. In this paper, a hybrid segmentation algorithm, which combines the watershed transform and level set techniques, is proposed. Since watershed segmentation is base...

Due to its broad impact in many image analysis applications, the problem of image segmentation has been widely studied. However, there still does not exist any automatic segmentation procedure able to deal accurately with any kind of image. Thus semi-automatic segmentation methods may be seen as an appropriate alternative to solve the segmentation problem. Among these methods, the marker-based ...

This paper deals with a novel semi-automatic segmentation method giving realistic results on head and neck tumors. To the classical “inside” and “outside”, we add a fuzzy area with a tumor probability between 0 and 1 which represents the area where even specialists do not have an unique solution. We describe here the technique and its results, then we will discuss about the marker number diminu...

In this work, we present a segmentation algorithm for color images that uses the watershed algorithm to segment either the two-dimensional (2-D) or the three-dimensional (3-D) color histogram of an image. For compliance with the way humans perceive color, this segmentation has to take place in a perceptually uniform color space like the Luv space. To avoid oversegmentation, the watershed algori...

A major drawback when using the watershed transformation as a segmentation tool comes from the over-segmentation of the image. Over-segmentation is produced by the great number of minima embedded in the image or in its gradient. A powerful technique has been designed to suppress over-segmentation by a primary selection of markers pointing out the regions or objects to be segmented in the image....

Automatic image interpretation is often achieved by first performing a segmentation of the image (i.e., gathering neighbouring pixels into homogeneous regions) and then applying a supervised region-based classification. In such a process, the quality of the segmentation step is of great importance in the final classified result. Nevertheless, whereas the classification step takes advantage from...

Ultrasound imaging or ultrasonography is an important diagnosis method in medical analysis. It is important to segment out cavities, di erent types of tissues and organs in the ultrasound image for e ective and correct diagnosis. Human experts are very good in segmenting out the required region. But humans lack e ciency when size of dataset increases. The need of high reproducibility and need o...

We propose a video region segmentation scheme combining spatio-temporal edges and watershed techniques. We consider the video sequence as a 3-D volume and compute color edges within this volume. These color edges form a vector field that is in turn used to obtain an edge function. This edge function is used as a topological surface for a watershed grouping stage. Considering the video as a 3-D ...

In this paper, an approach to the segmentation of microscopic color images is addressed, and applied to medical images. The approach combines a clustering method and a region growing method. Each color plane is segmented independently relying on a watershed based clustering of the plane histogram. The marginal segmentation maps intersect in a label concordance map. The latter map is simplified ...