Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision remains a highly challenging task due to existence inherent magnification and distortion in images well presence lesions with similar density n...

We propose a framework that can conveniently capture both the causal and non-causal relationships among random variables. The framework is formulated based on the hybrid probabilistic graphical model. It allows to model heterogeneous relationships using both the directed causal links and the undirected non-causal links. We apply this framework to image segmentation and develop a multiscale hybr...

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), ...

An hybrid segmentation method for color images is presented in this work. It combines 2D histogram clustering to produce segmentation maps fused together providing an initial unsupervised clustering of the dominant colors of the image. Region information is then used and a novel technique is introduced to simplify the Region Adjacency Graph by merging candidate regions until the stabilization o...

Medical Image segmentation is to partition the image into a set of regions that are visually obvious and consistent with respect to some properties such as gray level, texture or color. Brain tumor classification is an imperative and difficult task in cancer radiotherapy. The objective of this research is to examine the use of pattern classification methods for distinguishing different types of...

This paper presents a novel histogram thresholding – fuzzy C-means hybrid (HTFCM) approach that could find different application in pattern recognition as well as in computer vision, particularly in color image segmentation. The proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image. Then, the Fuzzy C-means (FCM) algorithm is uti...

An hybrid segmentation method for color images is presented in this work. It combines 2D histogram clustering to produce segmentation maps fused together providing an initial unsupervised clustering of the dominant colors of the image. Region information is then used and a novel technique is introduced to simplify the Region Adjacency Graph by merging candidate regions until the stabilization o...

In this paper, we propose a new hybrid model for active contour image segmentation, which is able to segment non-uniform noisy images efficiently. The model is a combination between the classical active contour based on the image gradient and the mean curvature moving technique. The efficiency is achieved by de-noising the image using log-Gabor filter then using a hybrid model to segment the no...