Universal and stable medical image generation for tissue segmentation \newline(The unistable method)

Abstract

Segmentation of medical images has been one of the most important research areas because of its impact in modeling and diagnosing the structure and the functions of various organs. The lack of unique solution for the segmentation problem of medical images is caused by the wide range of selections among different medical imaging modalities and clustering methods where each setting has its own estimates for solving this problem. The unistable method is a novel method that generates enhanced images with high contrast, which can reduce boundary overlap between different tissues. This is accomplished by fusion of different clustering maps, which are generated from selected medical images using some clustering methods. The improved unistable images are somehow universal, where all estimates of different regular segmentation settings are considered in the solution, and they are relatively stable, because results of their segmentation are proved to be relatively independent of clustering method.

Medical image enhancement based on volumetric tissue segmentation fusion (Uni-stable 3D method)

“…Uni-stable method [17] has been originally developed for generating high contrast 2D images based on DTI scans, where the Uni-stable method has been proved to be universal, where all estimates of different regular segmentation settings are considered in the solution, and they are relatively stable, because results of their segmentation are proved to be relatively independent of the applied clustering method (Fig. 1).…”

“…It was reported that there is no unique solution for the segmentation problem, because of the effect of the used imaging modality on the segmentation process; different results are obtained by changing clustering method and/or the selected numbers of clusters [5,7,8,15,16]. One solution in a former research [17] has been developed to produce Uni-stable images where segmentation results are relatively stable regarding the change of the clustering method. However, this was tested for the 2D images and need to be improved to cover the 3D images as well.…”

A marching algorithm for isosurface extraction from face-centered cubic lattices

“…Applications dealing with the isosurfaces extracted from volumetric data arise mostly in the medicine and geometry-processing domains. For the former, one may visualize and further assess a medical condition using the intensity values that are specific to the organs [14][15][16]. For the latter, we see shape deformation [17,18] and reconstruction [19][20][21][22] applications running on signed distance fields, as well as sampling of functions [8,23] and Boolean operations in constructive solid geometry [24,25].…”

Modeling of realistic heart electrical excitation based on DTI scans and modi ed reaction diffusion equation

“…Low-intensity zones represent fatty tissue, medium-intensity zones represent myocardium tissue, and high-intensity zones represent Purkinje tissue ( Figure 1). The produced DV map can be segmented with any clustering algorithm [29], such as K-means, and extra details are removed manually ( Figure 2). …”