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In this paper, we propose a novel method for detecting glandular structures in microscopic images of human tissue. We first transform the image from Cartesian space to polar space and introduce a novel random field model with an efficient inference strategy that uses two simple chain graphs to approximate a circular graph to infer possible boundary of a gland. We then develop a visual feature based support vector regressor (SVR) to verify if the inferred contour corresponds to a true gland. And finally, we combine the outputs of the random field and the regressor to form the GlandVision algorithm for the detection of glandular structures. In the experiments, we treat the task of detecting glandular structures as object (gland) proposal, detection and segmentation problems respectively and show that our new technique outperforms state of the art computer vision algorithms developed for generic objects.

GlandVision: A Novel Polar Space Random Field Model for Glandular Biological Structure Detection