Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spa...

La segmentation consiste à partitionner l’image en régions disjointes avec des couleurs homogènes. Les méthodes de segmentation d’images couleur peuvent être divisées en deux familles, selon qu’elles analysent la distribution des couleurs des pixels dans le plan image ou dans un espace couleur. La première partie de cet exposé ne décrira pas toutes les méthodes de segmentation existantes, mais ...

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Di4erent fusion methods have been proposed in literature, including multiresolution analysis. This paper...

This study presents a fabric level set method for contour extraction in medical images using novel coarse-to-fine level set scheme. Medical image segmentation is an atomic challenge for many researchers. The challenges are arisen due to the poor image contrast and artifacts that result in diffuse organ/tissue boundaries. Medical images are fused by using Brovey transform fusion to increase the ...

‎this paper presents an application of partial differential equations(pdes) for the segmentation of abdominal and thoracic aortic in cta datasets. an important challenge in reliably detecting aortic is the need to overcome problems associated with intensity inhomogeneities. level sets are part of an important class of methods that utilize partial differential equations (pdes) and have been exte...

Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...

Region merging methods consist of improving an initial segmentation by merging some pairs of neighboring regions. In a graph, merging two regions is not straightforward. The perfect fusion graphs defined in a previous paper verify all the properties requested by region merging algorithms. In this paper, we present a theorem which states that, in any dimension, the perfect fusion grids introduce...

In the sight of difficulty in segmentation of armor plate surface defects, visual attention mechanism is applied to segment defects images of steel strip surface, the key step of visual attention mechanism-fusion method of saliency images is improved, thinking of the contribution of salient area size, number and distribution to the comprehensive salient image, gray level consistency and GLCM’s ...

In multi-atlas based image segmentation, multiple atlases with label maps are propagated to the query image, and fused into the segmentation result. Voting rule is commonly used classifier fusion method to produce the consensus map. Local weighted voting (LWV) is another method which combines the propagated atlases weighted by local image similarity. When LWV is used, we found that the segmenta...