Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques
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Author/Creator ORCID
Date
2019-07-28
Type of Work
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Citation of Original Publication
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; Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques; BioMed Research International; https://doi.org/10.1155/2019/3843295;
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Subjects
Breast cancer
traditional mammography
ultrasonography
magnetic resonance imaging
radiation exposure
intravoxel incoherent motion
band expansion process
automatic target generation process
kernel constrained energy minimization
malignant tumors
cysts
Remote Sensing Signal and Image Processing Laboratory
traditional mammography
ultrasonography
magnetic resonance imaging
radiation exposure
intravoxel incoherent motion
band expansion process
automatic target generation process
kernel constrained energy minimization
malignant tumors
cysts
Remote Sensing Signal and Image Processing Laboratory
Abstract
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 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.