Connecting Markov random fields and active contour models: application to gland segmentation and classification

Xu, Jun; Monaco, James; Sparks, Rachel; Madabhushi, Anant

doi:10.1117/1.jmi.4.2.021107

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…Figure 8b is an enlarged image of the region drawn in the box, A-B line segment passes through the pulmonary nodule, point A is the left boundary, and B is the right boundary. The X and Y coordinates of points A and B in the image are (20,19) and (20,33), respectively. Figure 8c is an intensity graph of pixels on the C-D line segment.…”

Section: Construction Of the Boundary Detection Term Based On Bayesiamentioning

confidence: 99%

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Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

et al. 2020

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Background: Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper. Methods: First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed. Results: To verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively. Conclusions: The experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging.

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Section: Construction Of the Boundary Detection Term Based On Bayesiamentioning

confidence: 99%

“…Making full use of the spatial structure information between pixels will help solve this problem. It is worth noting that the MRF has been widely used in other fields such as prostate glands [20] and brain MR image segmentation [21]. But it is still rarely used in the segmentation of pulmonary nodules.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

et al. 2020

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“…is the normalization factor. A clique, C, in an undirected graph G = (S, E) is a subset of the vertices, C ⊆ S, such that every two distinct nodes are adjacent [48].…”

Section: Medical Demandsmentioning

confidence: 99%

“…In [70], the preliminary work is extended. The authors In [72]- [74], a hierarchical conditional random field (HIECRF) model based gastric histopathology image segmentation method is proposed to localize abnormal (cancer) regions.…”

Section: B Other Applications Using Mrfsmentioning

confidence: 99%

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

Li,

et al. 2020

Preprint

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Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. Among these methods, random field models play an indispensable role in improving the analysis performance. In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. Firstly, we introduce the background of two random fields and pathology images. Secondly, we summarize the basic mathematical knowledge of MRFs and CRFs from modelling to optimization. Then, a thorough review of the recent research on the MRFs and CRFs of pathology images analysis is presented. Finally, we investigate the popular methodologies in the related works and discuss the method migration among CAD field.

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“…Both classical computer vision processing and deep learning based methods have been successfully used for image segmentation in the past. Segmentation of glandular structure can be achieved by color-gradient-based, morphologybased [11,12], or graph-based methods. Multiple convolutional neural network (CNN) architecture [13][14][15] based models have been proposed for Gland Segmentation in the Colon Histology Images, thanks to the GlaS challenge [16].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study

et al. 2021

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Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a feasibility study on a colitis model treated with Sulfasalazine, a drug used in therapeutic care of inflammatory bowel disease. We aimed to evaluate the colonic mucosa improvement associated with the recovery response of the crypts, a complex histologic structure reflecting tissue homeostasis and repair in response to inflammation. Our approach requires robust image segmentation of objects of interest from whole slide images, a composite low dimensional representation of the typical or novel morphological variants of the segmented objects, and exploration of image features of significance towards biology and treatment efficacy. Both interpretable features (eg. counts, area, distance and angle) as well as statistical texture features calculated using Gray Level Co-Occurance Matrices (GLCMs), are shown to have significance in analysis. Ultimately, this analytic framework of supervised image segmentation, unsupervised learning, and feature analysis can be generally applied to preclinical data. We hope our report will inspire more efforts to utilize deep learning in preclinical in vivo studies and ultimately make the field more innovative and efficient.

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Connecting Markov random fields and active contour models: application to gland segmentation and classification

Cited by 5 publications

References 42 publications

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study

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