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Wiselin Jiji, G.
- Multiscale Approach for Multiple Sclerosis Lesion in Multichannel MRI
Authors
1 Department of Computer Science and Engineering, Dr. Sivanthi Adithanar College of Engineering, Tiruchendur-628215, IN
2 Computer Science and Engineering, Dr. Sivanthi Adithanar College of Engineering, Tiruchendur-628215, IN
Source
Digital Image Processing, Vol 3, No 10 (2011), Pagination: 600-605Abstract
The multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility by automatically detecting multiple sclerosis (MS) lesions in 3-D multichannel magnetic resonance (MR) images. Our method uses segmentation to obtain a hierarchical decomposition of a multichannel, anisotropic MR scans. It then produces a rich set of features describing the segments in terms of intensity, shape, location, neighborhood relations, and anatomical context. These features are used in the Machine Learning;from this we can get the fully automatic, efficient results.Keywords
Brain Imaging, MRI, Multiple Sclerosis, Segmentation.- Identification of Brain Tumor Using Texture Segmentation
Authors
1 Department of CSE, Government College of Engineering, Tirunelveli, IN
2 Department of CSE, Dr. Sivanthi Adithanar College of Engineering, Thiruchendur, IN
Source
Digital Image Processing, Vol 2, No 9 (2010), Pagination: 307-312Abstract
Texture segmentation plays a vital role in many medical imaging applications. Texture segmentation is the process of partitioning an image into regions based on their texture. Here we present an unsupervised segmentation, which means that the algorithm does not require any knowledge of texture type present nor, the number of textures in the image to be segmented. The basic idea of the proposed method is to use the newly improved multi-resolution Gabor filter for feature extraction along with k-means clustering algorithm to group the related textures (segmenting the regions). This method is applied to segment a brain tumor images to identify the infected area.
Keywords
Segmentation, K-Means Cluster, Improved Multi-Resolution Gabor Filter.- Segmentation of Heart Blood Vessels
Authors
1 Department of CSE, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur-628 215, TamilNadu, IN
Source
Digital Image Processing, Vol 2, No 9 (2010), Pagination: 330-336Abstract
The coronary arteries are essential for the proper functioning of the human heart. It is difficult to segment the volume datasets separately from blood-filled cavities of the heart. Main reason for this difficulty is the lack of sufficient spatial resolution and partial volume effects. In this paper, a method is presented to mark the coronary arteries by traversal. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel–filling phase. Then segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width morphological filters. Therefore an algorithm is proposed for segmenting blood vessels from MRI as the computer-aided analysis.