Automatic scale selection for medical image segmentation

The scale of interesting structures in medical images is space variant because of partial volume effects, spatial dependence of resolution in many imaging modalities, and differences in tissue properties. Existing segmentation methods either apply a single scale to the entire image or try fine-to-coarse/coarse-to-fine tracking of structures over multiple scales. While single scale approaches fail to fully recover the perceptually important structures, multi-scale methods have problems in providing reliable means to select proper scales and integrating information over multiple scales. A recent approach proposed by Elder and Zucker addresses the scale selection problem by computing a minimal reliable scale for each image pixel. The basic premise of this approach is that, while the scale of structures within an image vary spatially, the imaging system is fixed. Hence, sensor noise statistics can be calculated. Based on a model of edges to be detected, and operators to be used for detection, one can locally compute a unique minimal reliable scale at which the likelihood of error due to sensor noise is less than or equal to a predetermined threshold. In this paper, we improve the segmentation method based on the minimal reliable scale selection and evaluate its effectiveness with both simulated and actual medical data.