Problems and approaches for tissue segmentation in 3-D MR imaging

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Abstract
The paper describes applications problems and approaches for image segmentation in magnetic resonance imaging. The methods which are proposed work on 3D-datasets with the goal of isolating tissue volumes. Unlike 2D-techniques which operate on multispectral image data in 3D-image segmentation only one image for one anatomical slice is available lacking essential information for tissue discrimination. Three different approaches for the task of volume segmentation are presented. The first is based on the detection of edge structures dividing the original images into anatomically relevant object regions. A 3D-region merging algorithm is applied to extract those regions which belong to the object to be segmented from the region dataset. The second method consists of a polynomial classification of imagepixels into several user-defined tissue classes. Local texture properties are used as discrimination features. The third approach region classification may be regarded as a combination between edge detection and pixel classification. On the basis of a presegmentation of the dataset into object regions a classification process tries to group the regions into different object classes making use of various region features. The latter strategy has yielded the best results and highest reliability for 3D-image segmentation. Further improvements towards minimization of user-interaction are proposed. 1.

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