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Cross-Domain Brain CT Image Smart Segmentation via Shared Hidden Space Transfer FCM Clustering

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Published:21 June 2020Publication History
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Abstract

Clustering is an important issue in brain medical image segmentation. Original medical images used for clinical diagnosis are often insufficient for clustering in the current domain. As there are sufficient medical images in the related domains, transfer clustering can improve the clustering performance of the current domain by transferring knowledge across the related domains. In this article, we propose a novel shared hidden space transfer fuzzy c-means (FCM) clustering called SHST-FCM for cross-domain brain computed tomography (CT) image segmentation. SHST-FCM projects both the data samples of the source domain and target domain into the shared hidden space, such that the distributions of the two domains are as close as possible. In the learned shared subspace, the data samples of the source domain serve as the auxiliary knowledge to aid the clustering process in the target domain. Extensive experiments on brain CT medical image datasets indicate the effectiveness of the proposed method.

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        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 2s
          Special Issue on Smart Communications and Networking for Future Video Surveillance and Special Section on Extended MMSYS-NOSSDAV 2019 Best Papers
          April 2020
          291 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3407689
          Issue’s Table of Contents

          Copyright © 2020 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 21 June 2020
          • Revised: 1 August 2019
          • Accepted: 1 August 2019
          • Received: 1 May 2019
          Published in tomm Volume 16, Issue 2s

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