1. Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques
- Author
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Chein-I Chang, Clayton Chi-Chang Chen, Po-Wen Huang, Si-Wa Chan, Yu-Tzu Chang, Chin-Yao Lin, Ruey-Feng Chang, Jyh-Wen Chai, Yung-Chieh Chang, Yen-Chieh Ouyang, and Hsian-Min Chen
- Subjects
medicine.medical_specialty ,Article Subject ,Multispectral image ,Contrast Media ,lcsh:Medicine ,Breast Neoplasms ,General Biochemistry, Genetics and Molecular Biology ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Imaging, Three-Dimensional ,0302 clinical medicine ,Breast cancer ,Histogram ,Image Interpretation, Computer-Assisted ,medicine ,Humans ,Mammography ,Image resolution ,Intravoxel incoherent motion ,General Immunology and Microbiology ,medicine.diagnostic_test ,business.industry ,lcsh:R ,Hyperspectral imaging ,Magnetic resonance imaging ,General Medicine ,Image Enhancement ,medicine.disease ,Diffusion Magnetic Resonance Imaging ,030220 oncology & carcinogenesis ,Female ,Radiology ,business ,Research Article - 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 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.
- Published
- 2019