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A Competitive Study of Graph Reduction Methods for Min S-T Cut Image Segmentation

Węgliński T., Fabijańska A., Goclawski J.

Image Processing & Communications

2014

Vol. 19, no. 2-3

97--105

When applied to the segmentation of 3D medical images, graph-cut segmentation algorithms require an extreme amount of memory and time resources in order to represent the image graph and to perform the necessary processing on the graph. These requirements actually exclude the graph-cut based approaches from their practical application. Hence, there is a need to develop the dedicated graph size reduction methods. In this paper, several techniques for the graph size reduction are proposed. These apply the idea of superpixels. In particular, two methods for superpixel creation are introduced. The results of applying the proposed methods to the segmentation of CT datasets using min-cut/max-flow algorithm are presented, compared and discussed.

Optimal blocs subdivision in the "Dividing-cubes" algorithm : application to the 3D medical imagery

Kebaili A., Boumghar F.

Machine Graphics and Vision

2000

Vol. 9, No. 1/2

281-288

In this article we present an optimisation of the execution time of the "dividing-cubes" alorithm. whose aim is to extract 3D surfaces from volumic images. These images are generally very voluminous and represents an pile up of many iso-surfaces, what require a considerable time to process them fpr extract the desired iso-surface. In first time it's matter of reducing the cover time of the 3D image by subdividing it into blocs of voxels, witch comes to visits a more reduce number of voxels. We propose in a second step, an optimization of the subdividing blocs parameters that allow us to have a bloc size the most suitable as possible, and permint to genetare a minimal execution time.Our propositions and formulations are made vaild by means of experiments carried out on processed images, and medical images.