Adaptive fast marching method for automatic liver segmentation from CT images. Issue 9 (30th August 2013)

• Record Type: Journal Article
• Authors: Song, Xiao; Cheng, Ming; Wang, Boliang; Huang, Shaohui; Huang, Xiaoyang; Yang, Jinzhu
• Is Part Of: Medical physics. Volume 40:Issue 9(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-08-30
• Subjects: Computed tomography -- Probability theory, stochastic processes, and statistics; computerised tomography -- diseases -- image segmentation -- kidney -- liver -- medical image processing -- statistics; liver segmentation -- computer‐aided -- adaptive fast marching method -- intensity statistics -- computed tomography; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Liver -- Medical imaging -- Computed tomography -- Medical image segmentation -- Cancer -- Medical image noise -- Kidneys -- Medical image contrast -- Computer aided diagnosis -- Anatomy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4819824
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Liver segmentation is a fundamental step in computer‐aided liver disease diagnosis and surgery planning. For the sake of high accuracy and efficiency, in this study, the authors present an automatic seed point selection method and an adaptive fast marching method (FMM) for liver segmentation. Methods: : The automatic seed point selection method is according to the structure and intensity characteristics of liver. The proposed adaptive FMM is self‐adaptive parameter adjustment. The arrival time parameter T in FMM is adjusted according to the intensity statistics of the possible liver region, which can be used to estimate the size of liver region on the corresponding computed tomography (CT) slices. The proposed algorithm consists of the following steps. First, a thresholding operation was applied to remove the ribs, spines, and kidneys, followed by a smooth filter for noise reduction and a nonlinear gray scale converter, which was used to enhance the contrast of the liver parenchyma. Second, the seed points located in the liver were selected automatically. Finally, using the processed image as a speed function, adaptive FMM was employed to generate the liver contour. Results: : Clinical validation has been performed on 30 abdominal CT data‐sets. The proposed algorithm achieved an overall true positive rate of 98.7%, false negative rate of 1.6%, false positive rate of 5.2%, and the DICE coefficient of 96.7%. It takes about 0.30s for a 512 × 512‐pixel slice. Conclusions: : The method has been applied successfully to achieve fast and accurate liver segmentation.