Graph-Cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior
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摘要Femur segmentation from computed tomography (CT) images is a fundamental problem in femur-related computer-assisted diagnosis and surgical planning/navigation. In this study, an automatic approach for the segmentation of proximal femur from CT images that incorporates the statistical shape prior into the graph-cut framework (SP-GC) is proposed. The proposed segmentation framework includes two major processes, namely training and segmentation. In the training stage, a training set of three-dimensional CT images from a group of normal subjects were segmented semi-automatically. The shape prior was generated by active shape modeling that included mean shape and shape variance information. In the segmentation stage, two shape terms originated from the training stage were included in the GC energy function. The minimization of the energy function was achieved using a max-flow/min-cut algorithm. The performance of the proposed segmentation method was evaluated by testing on 60 CT datasets from bilateral femurs of 30 normal subjects. Qualitative and quantitative analyses of the segmentation results of the proposed method were performed and the results were compared with two widely used methods, namely the active shape model (ASM) and traditional GC, with results from manual delineation used as the ground truth. The mean dice similarity coefficient of the proposed SP-GC was 0.9600, which is higher than those of ASM and GC (0.8769 and 0.9358, respectively). The mean normalized error rate of the SP-GC results was 10 and 6 % lower than those of ASM and GC, respectively. In terms of the average surface distance measurement, the value for SP-GC was 0.885 mm, compared with 2.148 and 1.154 mm for ASM and GC, respectively. In comparison with ASM, SP-GC has superior performance given a small training set (e.g., n = 12). With increasing number of training samples, the segmentation accuracy of ASM saturated, but that of SP-GC slightly increased.
著者Huang JB, Griffith JF, Wang DF, Shi L
期刊名稱Journal of Medical and Biological Engineering
出版年份2015
月份10
日期1
卷號35
期次5
出版社SPRINGER HEIDELBERG
頁次594 - 607
國際標準期刊號1609-0985
電子國際標準期刊號2199-4757
語言英式英語
關鍵詞Active shape model; Computed tomography; Graph-cut algorithm; Proximal femur; Shape prior
Web of Science 學科類別Engineering; Engineering, Biomedical

上次更新時間 2020-22-10 於 01:50