This paper introduces a watershed-based stochastic segmentation methodology. The approach is based on using M realizations of N random markers to build a probability density function (pdf) of contours which is then segmented by volumic watershed for de ning the R most signi cant regions. It over-performs the standard watershed algorithms when the aim is to segment complex images into a few regi...

The use of watersheds in image segmentation relies mostly on a good estimation of image gradients. However, background noise tends to produce spurious gradients, causing over-segmentation and degrading the result of the watershed transform. Also, low-contrast edges generate small magnitude gradients, causing distinct regions to be erroneously merged. In this paper, a new technique is presented ...

Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds ...

A general framework for automatic segmentation of diatom images is presented. This segmentation is a critical first step in contour-based methods for automatic identification of diatoms by computerized image analysis. We review existing results, adapt popular segmentation methods to this difficult problem, and finally develop a method that substantially improves existing results. This method is...

Precise liver segmentation in abdominal MRI images is one of the most important steps for the computer-aided diagnosis of liver pathology. The first and essential step for diagnosis is automatic liver segmentation, and this process remains challenging. Extensive research has examined liver segmentation; however, it is challenging to distinguish which algorithm produces more precise segmentation...

Segmentation is an important process in the field of medical imaging, as it can provide detailed information of an image. In this work, segmentation of pulmonary lobe is carried out which is useful for the clinical interpretation of CT images, to access the early presence and the characterization of several lung diseases. This segmentation process is challenging for severely diseased lung or lu...

In this paper we present a new implementation of a rainfalling watershed segmentation algorithm. Our previous algorithm was a one-run algorithm. All the steps needed to compute a complete watershed segmentation were done in one run over the input data. In our new algorithm we tried another approach. We separated the watershed algorithm in several low-complexity relabeling steps that can be perf...

A road segmentation technique is proposed based on a mathematical morphology tool called watershed transform. Starting from a coarse marker of the road to be segmented, we modify the gradient image of the scene and compute the watershed transform of this image. The result provides a fair segmentation of the road. The marker itself is produced by using the watershed transform, and a simplified r...

Fernand MeyerCentre de Morphologie MathématiqueMines-ParisTech () Watersheds, waterfalls, on edge or node weighted graphs 2012 February 29 1 / 201 arXi Introduction The watershed transform is one of the major image segmentation tools [4], used in the community of mathematical morphology and beyond. If the watershed is a successful concept, there is another side of the coin: a number of definiti...

Image processing is a computational method for various reconstructing process used in the images. Segmentation refers to the procedure of splitting a digital image into many segments. Medical imaging is the system and development used to create imagery of the human body for clinical purposes or medical science. Image gradient is a function which is used to take information from images. A waters...