Confidence-Based Segmentation of MR Imagery Using Region and Boundary Information with Nonlinear Scale-Space and Fast Marching Level Sets
Jerod J. Weinman, George Dean Bissias, Edward M. Riseman, Allen R. Hanson, Joseph Horowitz · 2003
Automatic segmentation of stroke lesions in magnetic resonance imagery is a difficult problem because anatomical knowledge is required for the most accurate decisions. Without such knowledge, classification rules seem inconsistent. We propose a hybrid boundary and region based segmentation model built upon nonlinear scalespace and geometric active contours that captures the various segmentation rules necessary to segment lesions. After a user selects a point within damaged tissue and another point within healthy tissue, the image is examined at several levels of detail. At each such scale, the lesion is segmented several times by varying a parameter that models the range of criteria for boundaries between healthy and damaged tissue. These segmentations are collected, and the relative frequency of tissue being labeled lesion is regarded as a measure of confidence in the classification of the tissue as damaged. Experiments compare volumes and segmentations of lesions given by physicians to those given by the automatic method. Performance upper bounds are established by matching automatic segmentation parameters (scale, threshold, and/or confidence) for