Segmentation partitions an image into its constituent parts. It is essentially the pre-processing stage of image analysis and computer vision. In this work, T1 and T2 weighted brain magnetic resonance images are segmented using multilevel thresholding and bacterial foraging optimization (BFO) algorithm. The thresholds are obtained by maximizing the between class variance (multilevel Otsu method...

Content-based image retrieval (CBIR) is becoming an important field with the advance of multimedia and imaging technology everincreasingly. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. Content-based image retrieval can also be u...

The content-based retrieval of diffusion magnetic resonance (dMR) imaging data would enable a wide range of analyses on large databases with dMR images.This paper proposes a content-based retrieval framework for dMR images to explore the use of Diffusion Tensor Imaging (DTI) derived parameters. The propagation graph algorithm is proposed for the query-centric retrieval of dMR subjects and the f...

Magnetic Resonance Imaging is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Intelligent system is designed to diagnose brain tumor through MRI using image processing algorithms such as Particle ...

This paper presents two segmentation methods, based on Markov Random Fields (MRF), to quantify automatically brain tissue volumes from Magnetic Resonance (MR) images. Anatomically, a human brain is considered as composed of three main tissues: gray matter, white matter and cerebrospinal fluid (CSF). Due to the resolution limitation of the image acquisition system, a voxel in MR image may be com...

background : magnetic resonance imaging of the breast is becoming a useful adjunct to mammography and sonography for the detection of breast lesions. however, it is not yet accepted as a routine examination for all breast cancer patients due to the lack of data regarding whether breast magnetic resonance imaging impacts recurrence or survival. this trial examines the use of magnetic resonance i...

Brain magnetic resonance (MR) images have been widely used for analyzing brain diseases. However, due to the existence of noise and intensity inhomogeneity, many segmentation methods are hard to find accurate resuslts. This paper presents a novel variational framework for the registration, segmentation and bias estimation simultaneously. We first presented an improved segmentation model by usin...

During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this paper for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enh...

With the growth of 3D MRI, and the popularity of 4D functional MRI, there is a considerable need for advanced post-processing software to handle the extremely large volumes of data and to extract meaningful analyses. The primary analysis of such information is visualisation-the images are viewed by experts who can often judge immediately the significance of certain complex relationships. With v...

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