We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segme...

We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient, and identifies the main edges of an image. Several image processing problems (e.g. inpainting and segme...

BACKGROUND AND OBJECTIVE State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid region-based energy ...

In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms with aim to refine the disparity and depth map by using a stereo pair of images. This algorithm utilizes image filtering and modified SAD (Sum of Absolute Differences) stereo matching meth...

In this paper, the color image segmentation problem is addressed in supervised framework. In the supervised framework, we assume to have one original image from the class of images from which the given image is derived. In this framework, We have used Markov Random Field(MRF) to model the image label process and the MRF model parameters are estimated using the conditional pseudolikelihood crite...

Treatment planning for high precision radiotherapy of head and neck (H&N) cancer patients requires accurate delineation of many critical structures. Manual contouring is tedious and often suffers from large interand intra-rater variability. To reduce manual labor, we have previously developed a fully automated, atlas-based method for H&N CT image segmentation [1, 2]. In this work, we adapt the ...

RATIONALE AND OBJECTIVES The segmentation of airways from CT images is a critical first step for numerous virtual bronchoscopic (VB) applications. Automatic or semiautomatic methods are necessary, since manual segmentation is prohibitively time consuming. The methods must be robust and operate within a reasonable time frame to be useful for clinical VB use. The authors developed an integrated a...

This works deals with hybrid method for color image segmentation. The role of introducing the statistical features in clustering technique and that of data level and feature level fusion applied to color image segmentation are studied in this paper to obtain an optimal segmented image and to detect the fire in Forest images. The proposed segmentation approach is conceptually different and based...

Image thresholding has played an important role in image segmentation. This paper presents a hybrid approach for image segmentation based on the thresholding by fuzzy c-means (THFCM) algorithm for image segmentation. The goal of the proposed approach is to find a discerner cluster able to find an automatic threshold. The algorithm is formulated by applying the standard FCM clustering algorithm ...

detecting blood vessels is an important task in retinal image analysis.  the task is more challenging with the presence of bright and dark lesions in retinal images. here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. first, the negative impact of bright lesions is reduced by using k-means segmentation in a perceptive sp...