A novel polar space random field model for the detection of glandular structures

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-ba...

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Main Authors: Fu, Hao, Qiu, Guoping, Shu, Jie, Ilyas, Mohammad
Format: Article
Published: Institute of Electrical and Electronics Engineers 2014
Subjects:
Online Access:https://eprints.nottingham.ac.uk/47528/
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author Fu, Hao
Qiu, Guoping
Shu, Jie
Ilyas, Mohammad
author_facet Fu, Hao
Qiu, Guoping
Shu, Jie
Ilyas, Mohammad
author_sort Fu, Hao
building Nottingham Research Data Repository
collection Online Access
description 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.
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spelling nottingham-475282020-04-29T15:02:35Z https://eprints.nottingham.ac.uk/47528/ A novel polar space random field model for the detection of glandular structures Fu, Hao Qiu, Guoping Shu, Jie Ilyas, Mohammad 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. Institute of Electrical and Electronics Engineers 2014-01-02 Article PeerReviewed Fu, Hao, Qiu, Guoping, Shu, Jie and Ilyas, Mohammad (2014) A novel polar space random field model for the detection of glandular structures. IEEE Transactions on Medical Imaging, 33 (3). pp. 764-776. ISSN 1558-254X Gland; polar space; random field http://ieeexplore.ieee.org/document/6697841/ doi:10.1109/tmi.2013.2296572 doi:10.1109/tmi.2013.2296572
spellingShingle Gland; polar space; random field
Fu, Hao
Qiu, Guoping
Shu, Jie
Ilyas, Mohammad
A novel polar space random field model for the detection of glandular structures
title A novel polar space random field model for the detection of glandular structures
title_full A novel polar space random field model for the detection of glandular structures
title_fullStr A novel polar space random field model for the detection of glandular structures
title_full_unstemmed A novel polar space random field model for the detection of glandular structures
title_short A novel polar space random field model for the detection of glandular structures
title_sort novel polar space random field model for the detection of glandular structures
topic Gland; polar space; random field
url https://eprints.nottingham.ac.uk/47528/
https://eprints.nottingham.ac.uk/47528/
https://eprints.nottingham.ac.uk/47528/