Histogram partition and interval thresholding for volumetric breast tissue segmentation

It is possible to automatically decompose a volume into subvolumes based on histogram partition and interval thresholding. In practice, a histogram may assume unimodal or multimodal distributions. In this paper, we implement an automatic volumetric segmentation scheme by partitioning a histogram into intervals followed by interval thresholding. Based on its distribution shape, the histogram is partitioned by either a valley-seeking algorithm (for multimodal) or a five-subinterval algorithm (for unimodal). Applied to volumetric breast analysis, this technique decomposes a breast volume into five subvolumes corresponding to five intensity subintervals: lower (air bubble), low (fat), middle (normal tissue, or parenchyma), high (glandular duct), higher (calcification), in the order of X-ray attenuation value. With the assumption that each subvolume resulting from interval thresholding corresponds to a tissue type, the spatial structure of each breast tissue type can be individually visualized and analyzed in a subvolume in an ample space (as big as the whole volume) in the absence of other tissue types. We demonstrate this histogram-partitioned interval thresholding segmentation method with one breast phantom and one breast surgical specimen that are volumetrically reconstructed by cone-beam tomography.