Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques.
Breast 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...
| Publicado en: | BioMed Research International pp. 1 - 16 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/28/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137738521&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137738521 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/28/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 137738521 137738521 137738521 10.1155/2019/3843295 137738521 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques. aug: au: Chan, 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 affil: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan sug: subj: Breast Neoplasms Diagnosis Breast Neoplasms Classification Magnetic Resonance Imaging Methods Spectrum Analysis Methods Women's Health Human Female Mammography Ultrasonography Magnetic Resonance Imaging Contrast Media Adverse Effects Female ab: Breast 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 findings 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). After automatically finding 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. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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