A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Thresholding. Image thresholding technique is <s> This paper presents efficient methods for automatic detection and extraction of blood vessels and optic disc (OD) both of which are two prominent anatomical structures in ocular fundus images. The blood vessel extraction algorithm is composed of four steps, i.e., matched filtering, local entropy-based thresholding, length filtering, and vascular intersection detection. The OD identification involves the efficient active contour (snake) method. Specifically, we use intensity variation and local contrast to initialize the snake model. Simulation results on a set of retinal images verify the effectiveness of the proposed methods. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Thresholding. Image thresholding technique is <s> In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of interest. In this sense, our approach is regarded as knowledge-guided adaptive thresholding, in contrast to most algorithms known from the literature. We apply our general framework to detect vessels in retinal images. An experimental evaluation demonstrates superior performance over global thresholding and a vessel detection method recently reported in the literature. Due to its simplicity and general nature, our novel approach is expected to be applicable to a variety of other applications. <s> BIB002
basically an image segmentation technique that is applied to alter a grey-scale image into the binary image to differentiate the objective point of interest. Thresholding is most suitable for analyzing the images with high contrast. Thresholding is an important technique that is widely used for the segmentation of fundus retinal images to analyze the microvasculature. Thresholding is used in retinal image processing only to highlight the features of interest and avoid the features that are not important for analyzing the image. The authors of BIB001 used entropy-based thresholding as subtechnique to keep the spatial structure of vascular tree segments in their retinal image analysis algorithm to detect and extract the blood vessels. A knowledge-guided adaptive local thresholding algorithm based on the verification-based multithresholding probing scheme was proposed in BIB002 to detect the blood vessel in fundus retinal image.