Abstract
Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the medical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics (LARFS) was proposed. By combining segmentation algorithm principles for traditional region growing (RG) and robust feature statistics (RFS), the location and neighborhood image information of input seed point can be comprehensively analyzed by LARFS. Results show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And because the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.
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Zhuo, Z., Zhai, W., Li, X. et al. Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics. Sci. China Inf. Sci. 57, 1–12 (2014). https://doi.org/10.1007/s11432-014-5095-7
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DOI: https://doi.org/10.1007/s11432-014-5095-7