Automated Segmentation of Breast Tumor Based on Statistical Feature Analysis of Dynamic Contrast Enhanced MRI and Diffusion MRI

• Main Authors: Ho, Chun-I, 何峻毅
• Other Authors: Ching, Yu-Tai
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/44964827979625959550

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 104 === Since the past 30 years, cancer has been ranked first among the top ten leading causes of death. Among all the cancers, breast cancer was ranked fifth for women in 1995 and became first in 2006. To date, the incidence rate of breast cancer is still increasing. Therefore, a cost-effective and early diagnostic medical imaging tool for breast cancer is highly needed. Since 1980, magnetic resonance imaging (MRI) has been considered as an important medical imaging modality for clinical applications. MRI could provide useful pathological information for assessing the preoperative evaluation, efficacy of the treatment and postoperative tracking. On clinical protocols, breast MRI is usually performed after administration of contrast agents into the blood stream for assessing the perfusion information in microenvironment. To investigate the dynamic changes of signal intensities for a given region-of-interest (ROI), fast T1-weighted imaging technique is typically utilized to retrieve continuous signal changes, which is known as dynamic contrast enhanced-MRI (DCE-MRI). Another MRI technique used for breast tumor diagnosis is diffusion MRI, which can produces a quantitative metric, namely, apparent diffusion coefficient (ADC). In tumor regions, complex tissue microstructure and microenvironment typically cause increased diffusion heterogeneity, that is, non-Gaussianity. Therefore, a recently developed diffusion MRI technique, diffusion kurtosis imaging (DKI), is used to assess diffusion non-Gaussianity of water molecules in breast tumor regions. In this study, assessment of diffusion non-Gaussianity by DKI will be taken into account for tumor segmentation, except for DCE-MRI and ADC measurement which are usually applied to differentiate the tumor lesion by radiologists. By incorporating more information into our segmentation framework, we aimed to increase robustness and accuracy for automatic breast tumor segmentation. Our algorithm first performs k-means clustering approach on DCE-MRI images to determine the initial regions for following segmentation. Secondly, the histogram analysis is performed on ADC and DKI images to determine tumor probabilities for identifying possible tumor ROI. In results, a total of 14 clinical cases have been examined by the developed approach, showing the similarity between automatically selected ROI and manually selected ROI is approximately 60%. In future, this proposed segmentation approach could be potentially helpful to facilitate the early diagnosis and evaluation of treatment effects on breast cancer.