View-collaborative fuzzy soft subspace clustering for automatic medical image segmentation

With the rapid development of medical imaging methodologies, such as magnetic resonance (MR) and positron emission tomography (PET)/MR, various types of MR images, which are acquired using inconsistent MR pulse sequences on the same patient, have been applied in medical-image-based diagnoses. A feat...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 79; no. 13-14; pp. 9523 - 9542
Main Authors: Zhao, Kaifa, Jiang, Yizhang, Xia, Kaijian, Zhou, Leyuan, Chen, Yangyang, Xu, Ke, Qian, Pengjiang
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2020
Springer Nature B.V
Subjects:
Algorithms
Clustering
Collaboration
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Feature extraction
Feature maps
Image acquisition
Image segmentation
Machine learning
Magnetic resonance imaging
Medical imaging
Multimedia Information Systems
Positron emission
Special Purpose and Application-Based Systems
Subspaces
Tomography
Collaborative learning
Multiview clustering
Soft subspace clustering
Medical image segmentation
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:With the rapid development of medical imaging methodologies, such as magnetic resonance (MR) and positron emission tomography (PET)/MR, various types of MR images, which are acquired using inconsistent MR pulse sequences on the same patient, have been applied in medical-image-based diagnoses. A feature map extracted from an MR image describes the patient’s condition from one perspective. By effectively using all feature maps from various MR images, it is possible to completely describe the intrinsic characteristics of the patient’s condition to facilitate a diagnosis. Facing such a scenario, classic machine learning algorithms typically stack these feature maps for unified processing and do not explore the importance of each feature within a single feature map or the relationships among feature maps. To address these challenges, both multiview and subspace learning scenarios are considered in this study, and the multiview collaboration-based fuzzy soft subspace clustering (MVC-FSSC) algorithm is proposed. The MVC-FSSC algorithm not only strives to exploit the agreement of decisions across all views via collaborative learning but also strives to utilize the soft subspace-based weighting mechanism to automatically evaluate the contribution of each dimensional feature to each estimated cluster within a single view. Our experimental results indicate that the proposed MVC-FSSC algorithm can effectively explore the collaborative relations among all views and the importance of features in their respective views. Additionally, our MVC-FSSC method has substantial advantages over traditional clustering algorithms in MR image segmentation. Applying the MVC-FSSC algorithm to five patients’ MR images, the average mean absolute prediction deviation (MAPD) is 98.62 ± 8.34, which is significantly better than the score of 131.90 ± 16.03 that was obtained using the collaborative fuzzy k-means (CO-FKM) algorithm and the score of 128.87 ± 11.32 that was obtained using the quadratic weights and Gini-Simpson diversity-based fuzzy clustering (QWGSD-FC) algorithm.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-07974-7