Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques
Journal
BioMed Research International
Journal Volume
2019
Date Issued
2019
Author(s)
Chan, S.-W.
Chang, Y.-C.
Huang, P.-W.
Ouyang, Y.-C.
Chang, Y.-T.
Chai, J.-W.
Chen, C.C.-C.
Chen, H.-M.
Chang, C.-I.
Lin, C.-Y.
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. © 2019 Si-Wa Chan et al.
SDGs
Other Subjects
Article; breast cancer; breast cyst; breast tissue; decision tree; diffusion weighted imaging; histogram; image analysis; image processing; intravoxel incoherent motion magnetic resonance imagin histogram; nuclear magnetic resonance imaging; quantitative analysis; radiological parameters; tumor classification; breast tumor; computer assisted diagnosis; diagnostic imaging; female; human; image enhancement; mammography; pathology; procedures; three-dimensional imaging; contrast medium; Breast Neoplasms; Contrast Media; Diffusion Magnetic Resonance Imaging; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Mammography
Type
journal article