Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 34, Issue 9, Pages 1797-1807Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2015.2409024
Keywords
Active contour; fundus; infinite perimeter; local phase; segmentation; vessel
Categories
Funding
- Wellcome Trust [092668/Z/10/Z]
- Engineering and Physical Sciences Research Council [EP/K036939/1] Funding Source: researchfish
- EPSRC [EP/K036939/1] Funding Source: UKRI
- Wellcome Trust [092668/Z/10/Z] Funding Source: Wellcome Trust
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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 gamma-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.
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