Abstract
Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and ‘vesselness values’ from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.
Highlights
Active contour models [1,2,3,4] have become very popular in the past few decades, and widely used in a wide range of problems including image segmentation and computer vision, which dynamically deforms object contours based on a predefined energy functional from image information and can, by minimizing this functional, yield smooth and closed boundary contours of the desired objects with sub-pixel accuracy [5,6,7]
To identify correctly and extract completely small vessels, active contour models have to alleviate these problems caused by intensity inhomogeneity by using more image information, which is generally obtained by different feature descriptors [27, 30] and characterizes the natures of certain objects
To objectively evaluate the segmentation performances of our model, the public and available image datasets (STructured Analysis of the Retina, STARE) [28] are used for segmentation experiments (S1 File), which can be available at http://www.ces.clemson.edu/~ahoover/stare/
Summary
Active contour models [1,2,3,4] have become very popular in the past few decades, and widely used in a wide range of problems including image segmentation and computer vision, which dynamically deforms object contours based on a predefined energy functional from image information and can, by minimizing this functional, yield smooth and closed boundary contours of the desired objects with sub-pixel accuracy [5,6,7]. When image intensities are severe inhomogeneous, c1 may be approximately equal to c2, causing the model to lose of the capability of identifying the foreground and background regions This model solely utilizes the global information of image to drive the motion of curve contour and ignores the local information [29] around a neighborhood of each pixel point
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