Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Abstract

Blood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and SparseCoding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details.

BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

“…In addition to comparison with convolution neural network methods, it is also compared with traditional methods. Vessel Enhancement via Multi-dictionary and Sparse Coding (VE-MSC) method proposed by Chen et al [45] obtains a representation dictionary and an enhancement dictionary by extracting patches in the original blood vessel images and label images. The representation dictionary is used to obtain the sparse coefficients, and then the vascular enhancement image is reconstructed by the sparse coefficients and the enhancement dictionary.…”

“…Figure 17 shows the experimental results comparison on the DRIVE and STARE datasets using Chen at al. [45] and our method. As can be seen from the Fig.…”

“…17, Chen at al. [45] ignores the diameter scale information of the blood vessels, making the originally fine blood vessels become thicker after being enhanced. In addition, there are lesions and optic disc information showed in experimental results, indicating that the method is susceptible to noise such as the background of the lesion.…”

A Novel Framework for Shock Filter Using Partial Differential Equations

“…Image enhancement is a method for improving the quality or sharpening certain details of an image and is widely used in many fields such as fingerprint recognition [1], medical image processing [2][3][4], remote sense image processing [5], and underwater image processing [6]. There is an enormous amount of different approaches to performing image enhancing, which can broadly be divided into domain transform [2,5,7,8], histogram equalization [3,9], and feature-oriented filtering [10][11][12][13].…”

“…There is an enormous amount of different approaches to performing image enhancing, which can broadly be divided into domain transform [2,5,7,8], histogram equalization [3,9], and feature-oriented filtering [10][11][12][13]. As a feature-oriented filtering approach, shock filters are easily implemented and are effective in edge enhancing in image enhancing tasks.…”