background: brain tissue segmentation for delineation of 3d anatomical structures from magnetic resonance (mr) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (gm), white matter (wm) and cerebrospinal fluid (csf), but only if the obtained segmentation results are correct. due to image arti...

In multi-atlas based segmentation, a target image is segmented by registering multiple atlas images to this target image and propagating the corresponding atlas segmentations. These propagated segmentations are then combined into a single segmentation in a process called label fusion. Multi-atlas based segmentation is a segmentation method that allows fully automatic segmentation of image popul...

An image segmentation method for human gesture analysis is proposed deriving from the concept of opportunistic vision-based feature fusion. The method for human body part segmentation in image sequences is motivated by a layered and collaborative architecture for gesture analysis in multi-view camera networks. The layered structure aims to accommodate the diversity of gestures while collaborati...

Training a deep neural network often requires large amount of annotated data, which is scarce in the medical image analysis domain. In this work, we present simple yet effective technique for enhancing segmentation through information fusion. The proposed approach utilizes from different spatial scales and combines them learnable way. Experimental results on two benchmark datasets demonstrate t...

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contourbased atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of “single-most-similar” atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmen...

Information fusion has, in the form of multiple classifier systems, long been a successful tool in pattern recognition applications. It is also becoming increasingly popular in biomedical image analysis, for example in computer-aided diagnosis and in image segmentation. In this paper, we extend the principles of multiple classifier systems by considering information fusion of classifier inputs ...

Infrared and visible image fusion is an important precondition of realizing target perception for unmanned aerial vehicles UAVs , then UAV can perform various given missions. Information of texture and color in visible images are abundant, while target information in infrared images is more outstanding. The conventional fusion methods are mostly based on region segmentation; as a result, the fu...

In this study we examine the use of Multiscale Fourier Transforms (MFT) on the effect of image fusion and segmentation. The proposed method takes SPOT-PAN and SPOT-XS images to register using MFT technique to produce high quality fused image. The algorithm works iterativly from coarsest level of decomposition to the top level. This process applied to the all sub-bands in order to give best choi...

texture image analysis is one of the most important working realms of image processing in medical sciences and industry. up to present, different approaches have been proposed for segmentation of texture images. in this paper, we offered unsupervised texture image segmentation based on markov random field (mrf) model. first, we used gabor filter with different parameters’ (frequency, orientatio...