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Distribution Fitting-based Pixel Labeling For Histology Image Segmentation
This paper presents a new pixel labeling algorithm for complex histology image segmentation. For each image pixel, a Gaussian mixture model is applied to estimate its neighborhood intensity distributions. With this local distribution fitting, a set of pixels having a full set of source classes (e.g. nuclei, stroma, connective tissue, and background) in their neighborhoods are identified as the seeds for pixel labeling. A seed pixel is labeled by measuring its intensity distance to each of its neighborhood distributions, and the one with the shortest distance is selected to label the seed. For non-seed pixels, we propose two different labeling schemes: global voting and local clustering. In global voting each seed classifies a non-seed pixel into one of the seed's local distributions, i.e., it casts one vote; the final label for the non-seed pixel is the class which gets the most votes, across all the seeds. In local clustering, each non-seed pixel is labeled by one of its own neighborhood distributions. Because the local distributions in a non-seed pixel neighborhood do not necessarily correspond to distinct source classes (i.e., two or more local distributions may be produced by the same source class), we first identify the 'true' source class of each local distribution by using the source classes of the seed pixels and a minimum distance criterion to determine the closest source class. The pixel can then be labeled as belonging to this class. With both labeling schemes, experiments on a set of uterine cervix histology images show encouraging performance of our algorithm when compared with traditional multithresholding and K-means clustering, as well as state-of-the-art mean shift clustering, multiphase active contours, and Markov random field-based algorithms.