Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques

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dc.contributor.authorChan, Si-Wa
dc.contributor.authorChang, Yung-Chieh
dc.contributor.authorHuang, Po-Wen
dc.contributor.authorOuyang, Yen-Chieh
dc.contributor.authorChang, Yu-Tzu
dc.contributor.authorChang, Ruey-Feng
dc.contributor.authorChai, Jyh-Wen
dc.contributor.authorChen, Clayton Chi-Chang
dc.contributor.authorChen, Hsian-Min
dc.contributor.authorChang, Chein-I
dc.contributor.authorLin, Chin-Yao
dc.date.accessioned2019-10-23T14:33:24Z
dc.date.available2019-10-23T14:33:24Z
dc.date.issued2019-07-28
dc.description.abstractBreast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent fndings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). Afer automatically fnding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. Te experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can efectively diferentiate between these three breast tissue types.en_US
dc.description.urihttps://www.hindawi.com/journals/bmri/2019/3843295/en_US
dc.format.extent16 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2hndo-hmrv
dc.identifier.citationChan, Si-Wa; Chang, Yung-Chieh; Huang, Po-Wen; Ouyang, Yen-Chieh; Chang, Yu-Tzu; Chang, Ruey-Feng; Chai, Jyh-Wen; Chen, Clayton Chi-Chang; Chen, Hsian-Min; Chang, Chein-I.; Lin, Chin-Yao; Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques; BioMed Research International; https://doi.org/10.1155/2019/3843295;en_US
dc.identifier.urihttps://doi.org/10.1155/2019/3843295
dc.identifier.urihttp://hdl.handle.net/11603/15955
dc.language.isoen_USen_US
dc.publisherHindawien_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBreast canceren_US
dc.subjecttraditional mammographyen_US
dc.subjectultrasonographyen_US
dc.subjectmagnetic resonance imagingen_US
dc.subjectradiation exposureen_US
dc.subjectintravoxel incoherent motionen_US
dc.subjectband expansion processen_US
dc.subjectautomatic target generation processen_US
dc.subjectkernel constrained energy minimizationen_US
dc.subjectmalignant tumorsen_US
dc.subjectcystsen_US
dc.subjectRemote Sensing Signal and Image Processing Laboratoryen_US
dc.titleBreast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniquesen_US
dc.typeTexten_US

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