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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Vydané v:	Multimedia tools and applications Ročník 79; číslo 13-14; s. 9523 - 9542
Hlavní autori:	Zhao, Kaifa, Jiang, Yizhang, Xia, Kaijian, Zhou, Leyuan, Chen, Yangyang, Xu, Ke, Qian, Pengjiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2020 Springer Nature B.V
Predmet:	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
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Abstract	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.
AbstractList	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. 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.
Author	Xu, Ke Zhou, Leyuan Xia, Kaijian Zhao, Kaifa Chen, Yangyang Qian, Pengjiang Jiang, Yizhang
Author_xml	– sequence: 1 givenname: Kaifa surname: Zhao fullname: Zhao, Kaifa organization: School of Digital Media, Jiangnan University – sequence: 2 givenname: Yizhang surname: Jiang fullname: Jiang, Yizhang organization: School of Digital Media, Jiangnan University – sequence: 3 givenname: Kaijian surname: Xia fullname: Xia, Kaijian organization: Changshu No.1 People’s Hospital – sequence: 4 givenname: Leyuan surname: Zhou fullname: Zhou, Leyuan organization: Affiliated Hospital, Jiangnan University – sequence: 5 givenname: Yangyang surname: Chen fullname: Chen, Yangyang organization: School of Digital Media, Jiangnan University – sequence: 6 givenname: Ke surname: Xu fullname: Xu, Ke organization: School of Digital Media, Jiangnan University – sequence: 7 givenname: Pengjiang surname: Qian fullname: Qian, Pengjiang email: qianpjiang@jiangnan.edu.cn organization: School of Digital Media, Jiangnan University
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Cites_doi	10.1007/s10618-012-0258-x 10.1016/j.patcog.2009.09.010 10.1016/j.artmed.2018.07.001 10.1016/0098-3004(84)90020-7 10.3390/a8020234 10.1007/s10618-006-0060-8 10.1109/TCYB.2014.2334595 10.1016/j.patcog.2015.08.009 10.1142/S0219720012500035 10.1093/bioinformatics/btl185 10.1145/304181.304188 10.1007/s00500-015-1756-8 10.1145/1007730.1007731 10.1118/1.4926756 10.1002/jmri.24775 10.1016/S0893-6080(01)00108-3 10.1088/0031-9155/56/10/013 10.1007/BF00227423 10.1109/TSMCB.2011.2161607 10.1109/TMI.2018.2791721 10.3115/1273073.1273144 10.1109/ICDM.2012.43 10.1166/jmihi.2019.2749 10.1007/978-0-387-09823-4_14 10.1109/ICDM.2009.138
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References	Li, Mukaidono (CR16) 1995; 4 Qian, Sun, Jiang (CR23) 2016; 50 Miyamoto, Umayahara (CR20) 1998; 2 Gao, Maggs (CR12) 2005; 1 CR19 Xiaopeng, Shihe, Hui, Wen (CR31) 2014; 6 Deng, Choi, Chung, Wang (CR9) 2010; 43 CR34 CR10 CR32 Zaidi, Ojha, Morich (CR33) 2011; 56 Parsons, Haque, Liu (CR22) 2004; 6 Chitsaz, Jahromi (CR6) 2016; 20 Liang, Qian, Su (CR17) 2018; 90 Hooijmans, Dzyubachyk, Nehrke (CR13) 2015; 42 Su, Hu, Stehning (CR27) 2015; 42 Aggarwal, Wolf, Yu (CR1) 1999; 28 Liu, Mohammed, Carter (CR18) 2006; 22 Wang, Liu, Xiong (CR30) 2015; 8 Domeniconi, Gunopulos, Ma (CR11) 2007; 14 Aggarwal, Yu (CR2) 2000; 29 CR7 Hotho, Maedche, Staab (CR14) 2002; 16 CR28 CR24 Sim, Gopalkrishnan, Zimek, Cong (CR26) 2013; 26 Wang, Li, Zuluaga (CR29) 2018; 37 Roth, Shen, Oda (CR25) 2018; 36 Batuwita, Palade (CR3) 2012; 10 Bezdek, Ehrlich, Full (CR4) 1984; 10 Jiang, Chung, Wang (CR15) 2015; 45 Cao, Wu (CR5) 2002; 15 Nie, Xu, Li (CR21) 2012; 42 De Soete (CR8) 1986; 20 KH Su (7974_CR27) 2015; 42 G Wang (7974_CR29) 2018; 37 S Miyamoto (7974_CR20) 1998; 2 P Qian (7974_CR23) 2016; 50 F Liang (7974_CR17) 2018; 90 R Batuwita (7974_CR3) 2012; 10 A Hotho (7974_CR14) 2002; 16 F Nie (7974_CR21) 2012; 42 L Parsons (7974_CR22) 2004; 6 Y Jiang (7974_CR15) 2015; 45 Z Deng (7974_CR9) 2010; 43 7974_CR24 E Chitsaz (7974_CR6) 2016; 20 MT Hooijmans (7974_CR13) 2015; 42 7974_CR28 J Liu (7974_CR18) 2006; 22 Y Gao (7974_CR12) 2005; 1 C Domeniconi (7974_CR11) 2007; 14 W Xiaopeng (7974_CR31) 2014; 6 G De Soete (7974_CR8) 1986; 20 CC Aggarwal (7974_CR2) 2000; 29 7974_CR10 7974_CR32 Y Cao (7974_CR5) 2002; 15 JC Bezdek (7974_CR4) 1984; 10 7974_CR7 RP Li (7974_CR16) 1995; 4 K Sim (7974_CR26) 2013; 26 H Zaidi (7974_CR33) 2011; 56 7974_CR34 7974_CR19 G Wang (7974_CR30) 2015; 8 CC Aggarwal (7974_CR1) 1999; 28 HR Roth (7974_CR25) 2018; 36
References_xml	– volume: 26 start-page: 332 issue: 2 year: 2013 end-page: 397 ident: CR26 article-title: A survey on enhanced subspace clustering publication-title: Data Min Knowl Disc doi: 10.1007/s10618-012-0258-x – volume: 2 start-page: 1394 year: 1998 end-page: 1399 ident: CR20 article-title: Fuzzy clustering by quadratic regularization publication-title: IEEE World Congress Comput Intell – volume: 43 start-page: 767 issue: 3 year: 2010 end-page: 781 ident: CR9 article-title: Enhanced soft subspace clustering integrating within-cluster and between-cluster information publication-title: Pattern Recogn doi: 10.1016/j.patcog.2009.09.010 – ident: CR10 – volume: 90 start-page: 34 year: 2018 end-page: 41 ident: CR17 article-title: Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: an intelligent, multi-level fusion approach publication-title: Artif Intell Med doi: 10.1016/j.artmed.2018.07.001 – volume: 10 start-page: 191 issue: 2 year: 1984 end-page: 203 ident: CR4 article-title: FCM: the fuzzy c -means clustering algorithm publication-title: Comput Geosci doi: 10.1016/0098-3004(84)90020-7 – volume: 8 start-page: 234 issue: 2 year: 2015 end-page: 247 ident: CR30 article-title: An optimization clustering algorithm based on texture feature fusion for color image segmentation publication-title: Algorithms doi: 10.3390/a8020234 – volume: 16 start-page: 48 issue: 4 year: 2002 end-page: 54 ident: CR14 article-title: Ontology-based text document clustering publication-title: KI – volume: 4 start-page: 2227 year: 1995 end-page: 2232 ident: CR16 article-title: A maximum-entropy approach to fuzzy clustering publication-title: IEEE Int Conf Fuzzy Syst – volume: 14 start-page: 63 issue: 1 year: 2007 end-page: 97 ident: CR11 article-title: Locally adaptive metrics for clustering high dimensional data publication-title: Data Min Knowl Disc doi: 10.1007/s10618-006-0060-8 – volume: 45 start-page: 688 issue: 4 year: 2015 end-page: 701 ident: CR15 article-title: Collaborative fuzzy clustering from multiple weighted views publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2014.2334595 – volume: 1 start-page: 468 year: 2005 end-page: 473 ident: CR12 article-title: Feature-level fusion in personal identification publication-title: Comput Soc Conf Comput Vis Pattern Recognit – volume: 50 start-page: 155 year: 2016 end-page: 177 ident: CR23 article-title: Cross-domain, soft-partition clustering with diversity measure and knowledge reference publication-title: Pattern Recogn doi: 10.1016/j.patcog.2015.08.009 – volume: 10 start-page: 1250003 issue: 04 year: 2012 ident: CR3 article-title: Adjusted geometric-mean: a novel performance measure for imbalanced bioinformatics datasets learning publication-title: J Bioinforma Comput Biol doi: 10.1142/S0219720012500035 – ident: CR19 – volume: 22 start-page: 1971 issue: 16 year: 2006 end-page: 1978 ident: CR18 article-title: Distance-based clustering of CGH data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl185 – volume: 29 start-page: 70 issue: 2 year: 2000 end-page: 81 ident: CR2 article-title: Finding generalized projected clusters in high dimensional spaces publication-title: ACM – ident: CR32 – ident: CR34 – volume: 28 start-page: 61 issue: 2 year: 1999 end-page: 72 ident: CR1 article-title: Fast algorithms for projected clustering publication-title: ACM SIGMOD Rec doi: 10.1145/304181.304188 – volume: 20 start-page: 4463 issue: 11 year: 2016 end-page: 4472 ident: CR6 article-title: A novel soft subspace clustering algorithm with noise detection for high dimensional datasets publication-title: Soft Comput doi: 10.1007/s00500-015-1756-8 – volume: 6 start-page: 90 issue: 1 year: 2004 end-page: 105 ident: CR22 article-title: Subspace clustering for high dimensional data: a review publication-title: ACM Sigkdd Explor Newsl doi: 10.1145/1007730.1007731 – volume: 42 start-page: 4974 issue: 8 year: 2015 end-page: 4986 ident: CR27 article-title: Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering publication-title: Med Phys doi: 10.1118/1.4926756 – ident: CR7 – volume: 42 start-page: 217 issue: 1 year: 2015 end-page: 223 ident: CR13 article-title: Fast multistation water/fat imaging at 3T using DREAM-based RF shimming publication-title: J Magn Reson Imaging doi: 10.1002/jmri.24775 – volume: 6 start-page: 2008 issue: 7 year: 2014 end-page: 2014 ident: CR31 article-title: The design of medical image transfer function using multi-feature fusion and improved k-means clustering publication-title: J Chem Pharm Res – volume: 15 start-page: 105 issue: 1 year: 2002 end-page: 120 ident: CR5 article-title: Projective ART for clustering data sets in high dimensional spaces publication-title: Neural Netw doi: 10.1016/S0893-6080(01)00108-3 – ident: CR28 – ident: CR24 – volume: 36 start-page: 63 issue: 2 year: 2018 end-page: 71 ident: CR25 article-title: Deep learning and its application to medical image segmentation publication-title: Med Imaging Technol – volume: 56 start-page: 3091 issue: 10 year: 2011 ident: CR33 article-title: Design and performance evaluation of a whole-body ingenuity TF PET–MRI system publication-title: Phys Med Biol doi: 10.1088/0031-9155/56/10/013 – volume: 20 start-page: 169 issue: 2-3 year: 1986 end-page: 180 ident: CR8 article-title: Optimal variable weighting for ultrametric and additive tree clustering publication-title: Qual Quant doi: 10.1007/BF00227423 – volume: 42 start-page: 17 issue: 1 year: 2012 end-page: 27 ident: CR21 article-title: Initialization independent clustering with actively self-training method publication-title: IEEE Trans Syst Man Cybern Part B (Cybernetics) doi: 10.1109/TSMCB.2011.2161607 – volume: 37 start-page: 1562 issue: 7 year: 2018 end-page: 1573 ident: CR29 article-title: Interactive medical image segmentation using deep learning with image-specific fine tuning publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2018.2791721 – volume: 20 start-page: 4463 issue: 11 year: 2016 ident: 7974_CR6 publication-title: Soft Comput doi: 10.1007/s00500-015-1756-8 – volume: 50 start-page: 155 year: 2016 ident: 7974_CR23 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2015.08.009 – volume: 20 start-page: 169 issue: 2-3 year: 1986 ident: 7974_CR8 publication-title: Qual Quant doi: 10.1007/BF00227423 – volume: 42 start-page: 4974 issue: 8 year: 2015 ident: 7974_CR27 publication-title: Med Phys doi: 10.1118/1.4926756 – ident: 7974_CR32 – volume: 14 start-page: 63 issue: 1 year: 2007 ident: 7974_CR11 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-006-0060-8 – ident: 7974_CR19 doi: 10.3115/1273073.1273144 – volume: 37 start-page: 1562 issue: 7 year: 2018 ident: 7974_CR29 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2018.2791721 – volume: 10 start-page: 191 issue: 2 year: 1984 ident: 7974_CR4 publication-title: Comput Geosci doi: 10.1016/0098-3004(84)90020-7 – volume: 22 start-page: 1971 issue: 16 year: 2006 ident: 7974_CR18 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl185 – volume: 2 start-page: 1394 year: 1998 ident: 7974_CR20 publication-title: IEEE World Congress Comput Intell – volume: 45 start-page: 688 issue: 4 year: 2015 ident: 7974_CR15 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2014.2334595 – volume: 36 start-page: 63 issue: 2 year: 2018 ident: 7974_CR25 publication-title: Med Imaging Technol – volume: 90 start-page: 34 year: 2018 ident: 7974_CR17 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2018.07.001 – volume: 42 start-page: 17 issue: 1 year: 2012 ident: 7974_CR21 publication-title: IEEE Trans Syst Man Cybern Part B (Cybernetics) doi: 10.1109/TSMCB.2011.2161607 – volume: 56 start-page: 3091 issue: 10 year: 2011 ident: 7974_CR33 publication-title: Phys Med Biol doi: 10.1088/0031-9155/56/10/013 – volume: 28 start-page: 61 issue: 2 year: 1999 ident: 7974_CR1 publication-title: ACM SIGMOD Rec doi: 10.1145/304181.304188 – volume: 15 start-page: 105 issue: 1 year: 2002 ident: 7974_CR5 publication-title: Neural Netw doi: 10.1016/S0893-6080(01)00108-3 – volume: 16 start-page: 48 issue: 4 year: 2002 ident: 7974_CR14 publication-title: KI – volume: 43 start-page: 767 issue: 3 year: 2010 ident: 7974_CR9 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2009.09.010 – ident: 7974_CR28 doi: 10.1109/ICDM.2012.43 – volume: 8 start-page: 234 issue: 2 year: 2015 ident: 7974_CR30 publication-title: Algorithms doi: 10.3390/a8020234 – volume: 6 start-page: 90 issue: 1 year: 2004 ident: 7974_CR22 publication-title: ACM Sigkdd Explor Newsl doi: 10.1145/1007730.1007731 – volume: 10 start-page: 1250003 issue: 04 year: 2012 ident: 7974_CR3 publication-title: J Bioinforma Comput Biol doi: 10.1142/S0219720012500035 – volume: 4 start-page: 2227 year: 1995 ident: 7974_CR16 publication-title: IEEE Int Conf Fuzzy Syst – volume: 29 start-page: 70 issue: 2 year: 2000 ident: 7974_CR2 publication-title: ACM – ident: 7974_CR10 – ident: 7974_CR34 doi: 10.1166/jmihi.2019.2749 – ident: 7974_CR24 doi: 10.1007/978-0-387-09823-4_14 – volume: 42 start-page: 217 issue: 1 year: 2015 ident: 7974_CR13 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.24775 – volume: 6 start-page: 2008 issue: 7 year: 2014 ident: 7974_CR31 publication-title: J Chem Pharm Res – volume: 1 start-page: 468 year: 2005 ident: 7974_CR12 publication-title: Comput Soc Conf Comput Vis Pattern Recognit – volume: 26 start-page: 332 issue: 2 year: 2013 ident: 7974_CR26 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-012-0258-x – ident: 7974_CR7 doi: 10.1109/ICDM.2009.138
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SubjectTerms	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
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Title	View-collaborative fuzzy soft subspace clustering for automatic medical image segmentation
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