Abstract
Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L(2) Lebesgue measure of the γ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., H(1) Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.
Highlights
B LOOD vessels can be conceptualized anatomically as an intricate network, or tree-like structure, of hollow tubes of different sizes and compositions includingManuscript received December 11, 2014; revised February 17, 2015; accepted February 25, 2015
We propose a novel extension of the infinite perimeter active contour model so that the newly proposed model is able to take into account different types of image information
The so called Local Morphology Fitting (LMF) model proposed by Sun et al [16] merely modifies the data terms and adds a level set ZHAO et al.: AUTOMATED VESSEL SEGMENTATION USING INFINITE PERIMETER ACTIVE CONTOUR MODEL WITH HYBRID REGION INFORMATION
Summary
B LOOD vessels can be conceptualized anatomically as an intricate network, or tree-like structure (or vasculature), of hollow tubes of different sizes and compositions including. The lower requirement on the data and training makes unsupervised segmentation methods more applicable to a wider range of imaging modalities This category encapsulates most vessel segmentation techniques in the literature, such as [6], [10], [15], [24]–[28], and our model as described in this paper. A new infinite perimeter active contour model [34] has shown convincing performance in the detection of small oscillatory structures This feature of the model implies good performance expectations with vessel segmentation problems.
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