Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

Handle URI:
http://hdl.handle.net/10754/563113
Title:
Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification
Authors:
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Automated classification of tissue types of Region of Interest (ROI) in medical images has been an important application in Computer-Aided Diagnosis (CAD). Recently, bag-of-feature methods which treat each ROI as a set of local features have shown their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting the inner relationship between the visual words and their weights. To overcome this problem, we develop a novel algorithm, Joint-ViVo, which learns the vocabulary and visual word weights jointly. A unified objective function based on large margin is defined for learning of both visual vocabulary and visual word weights, and optimized alternately in the iterative algorithm. We test our algorithm on three tissue classification tasks: classifying breast tissue density in mammograms, classifying lung tissue in High-Resolution Computed Tomography (HRCT) images, and identifying brain tissue type in Magnetic Resonance Imaging (MRI). The results show that Joint-ViVo outperforms the state-of-art methods on tissue classification problems. © 2013 Elsevier Ltd.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computational Bioscience Research Center (CBRC); Computer Science Program; Structural and Functional Bioinformatics Group
Publisher:
Elsevier BV
Journal:
Pattern Recognition
Issue Date:
Dec-2013
DOI:
10.1016/j.patcog.2013.05.001
Type:
Article
ISSN:
00313203
Sponsors:
The study was supported by grants from National Key Laboratory for Novel Software Technology, China (Grant no. KFKT2012B17), 2011 Qatar Annual Research Forum Award (Grant no. ARF2011), and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Appears in Collections:
Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorBensmail, Halimaen
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-08-03T11:36:04Zen
dc.date.available2015-08-03T11:36:04Zen
dc.date.issued2013-12en
dc.identifier.issn00313203en
dc.identifier.doi10.1016/j.patcog.2013.05.001en
dc.identifier.urihttp://hdl.handle.net/10754/563113en
dc.description.abstractAutomated classification of tissue types of Region of Interest (ROI) in medical images has been an important application in Computer-Aided Diagnosis (CAD). Recently, bag-of-feature methods which treat each ROI as a set of local features have shown their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting the inner relationship between the visual words and their weights. To overcome this problem, we develop a novel algorithm, Joint-ViVo, which learns the vocabulary and visual word weights jointly. A unified objective function based on large margin is defined for learning of both visual vocabulary and visual word weights, and optimized alternately in the iterative algorithm. We test our algorithm on three tissue classification tasks: classifying breast tissue density in mammograms, classifying lung tissue in High-Resolution Computed Tomography (HRCT) images, and identifying brain tissue type in Magnetic Resonance Imaging (MRI). The results show that Joint-ViVo outperforms the state-of-art methods on tissue classification problems. © 2013 Elsevier Ltd.en
dc.description.sponsorshipThe study was supported by grants from National Key Laboratory for Novel Software Technology, China (Grant no. KFKT2012B17), 2011 Qatar Annual Research Forum Award (Grant no. ARF2011), and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.en
dc.publisherElsevier BVen
dc.subjectBag-of-featuresen
dc.subjectTissue classificationen
dc.subjectVisual vocabularyen
dc.subjectVisual word weightingen
dc.titleJoint learning and weighting of visual vocabulary for bag-of-feature based tissue classificationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentStructural and Functional Bioinformatics Groupen
dc.identifier.journalPattern Recognitionen
dc.contributor.institutionQatar Computing Research Institute, Doha 5825, Qataren
kaust.authorWang, Jim Jing-Yanen
kaust.authorGao, Xinen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.