Share Email Print
cover

Proceedings Paper

Segmentation of MR images using multiple-feature vectors
Author(s): Orlean I. B. Cole; Mohammad Farhang Daemi
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Segmentation is an important step in the analysis of MR images (MRI). Considerable progress has been made in this area, and numerous reports on 3D segmentation, volume measurement and visualization have been published in recent years. The main purpose of our study is to investigate the power and use of fractal techniques in extraction of features from MR images of the human brain. These features which are supplemented by other features are used for segmentation, and ultimately for the extraction of a known pathology, in our case multiple- sclerosis (MS) lesions. We are particularly interested in the progress of the lesions and occurrence of new lesions which in a typical case are scattered within the image and are sometimes difficult to identify visually. We propose a technique for multi-channel segmentation of MR images using multiple feature vectors. The channels are proton density, T1-weighted and T2-weighted images containing multiple-sclerosis (MS) lesions at various stages of development. We first represent each image as a set of feature vectors which are estimated using fractal techniques, and supplemented by micro-texture features and features from the gray-level co-occurrence matrix (GLCM). These feature vectors are then used in a feature selection algorithm to reduce the dimension of the feature space. The next stage is segmentation and clustering. The selected feature vectors now form the input to the segmentation and clustering routines and are used as the initial clustering parameters. For this purpose, we have used the classical K-means as the initial clustering method. The clustered image is then passed into a probabilistic classifier to further classify and validate each region, taking into account the spatial properties of the image. Initially, segmentation results were obtained using the fractal dimension features alone. Subsequently, a combination of the fractal dimension features and the supplementary features mentioned above were also obtained and the results were compared with each other as well as with other standard techniques. A number of controlled experiments were carried out with the help of trained radiologists, which have indicated the validity of the preliminary results.

Paper Details

Date Published: 16 April 1996
PDF: 9 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237920
Show Author Affiliations
Orlean I. B. Cole, Univ. of Nottingham (United Kingdom)
Mohammad Farhang Daemi, Univ. of Nottingham (United Kingdom)


Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

© SPIE. Terms of Use
Back to Top