A Novel Polar Space Random Field Model for the Detection of Glandular Structures

Abstract

Abstract-In this paper, we propose a novel method to detect glandular structures in microscopic images of human tissue. We first convert the image from Cartesian space to polar space and then introduce a novel random field model to locate the possible boundary of a gland. Next, we develop a visual feature-based support vector regressor to verify if the detected 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. Our approach can not only detect the existence of the gland, but also can accurately locate it with pixel accuracy. In the experiments, we treat the task of detecting glandular structures as object (gland) detection and segmentation problems respectively. The results indicate that our new technique outperforms state-of-the-art computer vision algorithms in respective fields.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

“…Linear discriminant analysis (LDA) was used to distinguish between benign and malignant glands. In a more recent paper ( Fu et al, 2014 ), a gland detection and segmentation approach named “GlandVision” was proposed. A random field model was employed to locate candidate gland boundaries in a polar coordinate-transformed image.…”

Identification of histological features to predict MUC2 expression in colon cancer tissues

“…15 Much progress has been made in the segmentation of glandular structure in a tissue image. 21,[24][25][26][27][28][29] A nuclear segmentation algorithm consisting of color deconvolution, k-means clustering, local adaptive thresholding, and cell separation was explored to extract cell nuclei features in a recent study, 3 and previous studies have used active contour models to segment nuclei. [30][31][32] It is important to replicate our results in the context of other nuclei segmentation approaches to study the robustness of the association.…”

Gland segmentation in colon histology images: The glas challenge contest

“…Recent papers [Wu et al, 2005a,b, Gunduz-Demir et al, 2010, Fu et al, 2014, Sirinukunwattana et al, 2015, Cohen et al, 2015 indicate the increasing interest in histology image analysis applied to intestinal gland segmentation. In this section, we review some of these methods.…”

“…The validation of this method was limited only to the dataset with healthy and benign cases. Fu et al [2014] introduced a segmentation algorithm based on polar coordinates. A neighborhood of each gland and a center chosen inside the gland were considered.…”