A region‐appearance‐based adaptive variational model for 3D liver segmentation. Issue 4 (7th March 2014)

• Record Type: Journal Article
• Authors: Peng, Jialin; Dong, Fangfang; Chen, Yunmei; Kong, Dexing
• Is Part Of: Medical physics. Volume 41:Issue 4(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-03-07
• Subjects: Computed tomography -- Segmentation -- Calculus of variations; computerised tomography -- edge detection -- image segmentation -- liver -- medical image processing -- variational techniques; liver segmentation -- variational model -- active contour; 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 -- Heart -- Medical image segmentation -- Computed tomography -- Data sets -- Anatomy -- Testing procedures -- Geoinformatics -- Three dimensional image processing; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4866837
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Liver segmentation from computed tomography images is a challenging task owing to pixel intensity overlapping, ambiguous edges, and complex backgrounds. The authors address this problem with a novel active surface scheme, which minimizes an energy functional combining both edge‐ and region‐based information. Methods: : In this semiautomatic method, the evolving surface is principally attracted to strong edges but is facilitated by the region‐based information where edge information is missing. As avoiding oversegmentation is the primary challenge, the authors take into account multiple features and appearance context information. Discriminative cues, such as multilayer consecutiveness and local organ deformation are also implicitly incorporated. Case‐specific intensity and appearance constraints are included to cope with the typically large appearance variations over multiple images. Spatially adaptive balancing weights are employed to handle the nonuniformity of image features. Results: : Comparisons and validations on difficult cases showed that the authors' model can effectively discriminate the liver from adhering background tissues. Boundaries weak in gradient or with no local evidence (e.g., small edge gaps or parts with similar intensity to the background) were delineated without additional user constraint. With an average surface distance of 0.9 mm and an average volume overlap of 93.9% on the MICCAI data set, the authors' model outperformed most state‐of‐the‐art methods. Validations on eight volumes with different initial conditions had segmentation score variances mostly less than unity. Conclusions: : The proposed model can efficiently delineate ambiguous liver edges from complex tissue backgrounds with reproducibility. Quantitative validations and comparative results demonstrate the accuracy and efficacy of the model.