Connecting Markov random fields and active contour models: application to gland segmentation and classification-Reference-Cited by-同舟云学术

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

Published:2017-03-28 Issue:2 Volume:4 Page:021107
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:en
Short-container-title:J. Med. Imag

Author:

Xu Jun¹,Monaco James P.²,Sparks Rachel³,Madabhushi Anant⁴

Affiliation:

1. Nanjing University of Information Science and Technology, Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing, China

2. Inspirata, Tampa, Florida, United States

3. University College of London, Center for Medical Image Computing, London, United Kingdom

4. Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology Nuclear Medicine and imaging

Reference46 articles.

1. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

2. Snakes: Active contour models

3. An adaptive clustering algorithm for image segmentation

4. A multiscale random field model for Bayesian image segmentation

5. A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model

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1. Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study;PLOS ONE;2021-06-14

2. A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis;Archives of Computational Methods in Engineering;2021-04-27

3. Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement;Neurocomputing;2020-09

4. Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference;BioMedical Engineering OnLine;2020-06-17

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