Automatic Abdominal Fat Segmentation in MagneticResonance Images Using Spatial Clustering Algorithm

• Main Authors: Jing-Yi Sun, 孫靜儀
• Other Authors: Jay Wu
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/20744550511596639289

碩士 === 中臺科技大學 === 放射科學研究所 === 99 === Metabolic syndrome, such as hypertension, arteriosclerosis, and type-two diabetes, are related with the amount of abdominal fat. Thus, calculation of the abdominal fat provides an important index for preventing metabolic diseases. This research was to measure abdominal adipose tissues for T2-weighted magnetic resonance imaging (MRI) by applying an accurate unsupervised method. The proposed automatic procedures were divided into the following steps. First process was the image inhomogeneity correction using the Modified Fuzzy C-means (MFCM). Second step was to creation the body masks using Canny edge algorithm. Third, the adipose tissue and non-adipose tissue was detected by k-means cluster algorithm. Final process was the abdomen fat segmentation by the body mask and the non-adipose tissue mask. The simple linear regression and Blot-Altmen plot were used to analyze and compare the consistency and correlation between the manual and automatic segmentation methods. The Pearson correlation coefficients for SAT and VAT between manual and automatic segmentations were 0.999 (p < 0.05) and 0.997 (p < 0.05), respectively. The Blot-Altmen plot showed that manual and automatic segmentations were consistent for SAT and VAT. The percent error for SAT and VAT were -1.7% ~ 2.1% and -4.2% ~ 5.2%, respectively. The proposed algorithm can obtain the abdominal fat distribution efficiently and can lower the error of manual segmentation. Therefore, this approach could be used as an effective tool for quantitative research. Furthermore it could be used to prevent the metabolic related diseases.