Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images

By Yitian Zhao, Lavdie Rada, Ke Chen, Simon P. Harding, Yalin Zheng. IEEE Transactions on Medical Imaging, 2015

390 citations recorded by the source corpus.

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 L2 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., H1 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.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1a2ab88f595bf6128f527ff066fdcb3a5caaf4af

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In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Computer Methods and Programs in Biomedicine (neutral): “Local phase map and pixel intensity are used as region information in [84] .”
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018, background): “[10] proposed to solve an infinite active contour model by using hybrid region information.”
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores (2016, methodology): “[13] presented methods for retinal vessel segmentation by applying active contour models.”
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation (2018, background): “The second dataset, STARE, contains 20 color images, and each image has a size of 700×605 pixels [45, 46].”
Recurrent residual U-Net for medical image segmentation (2019, background): “The second dataset, STARE, contains 20 color images, and each image has a size of 700 × 605 pixels.(48,50) Owing to the small number of samples Fig.”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, background): “In general, most of these strategies are based on applying thresholding, vessel tracking techniques [16] or region-oriented approaches—such as region growing [17]–[19] or active contours [20], [21]—after vessel enhancement.”
Segmenting Retinal Blood Vessels With Deep Neural Networks (2016, background): “in [15] employ a sophisticated active contour model that involves not only pixel brightness, but also other features derived from an image.”
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, neutral): “[19] proposed to solve an infinite”