The segmented blood vessel in retinal images is an important indicator in medical treatment. In 2007, Ricci and Perfetti proposed a simple and efficient blood vessel segmentation method based on line operator. However, this scheme makes some false segmentation when it is used to process the pixels which are close to a thick blood vessel. To overcome the above problem, a novel retinal blood vessel segmentation method based on line operator and edge detector, also called (RBVSLE), was proposed. The RBVSLE enhances the contrast of the green-channel of the retinal images. Next, the RBVSLE creates the edge map, which is denoted as vessel growth seed map, by using a canny detector. Finally, the RBVSLE segments vessel pixel by line operator from vessel growth seed map. Experimental results confirm the accuracy rate of RBVSLE is approximately 94% which is significantly better than other methods.
This paper is intended to present a lossless image compression method based on multiple-tables arithmetic coding (MTAC) method to encode a gray-level imagef. First, the MTAC method employs a median edge detector (MED) to reduce the entropy rate off. The gray levels of two adjacent pixels in an image are usually similar. A base-switching transformation approach is then used to reduce the spatial redundancy of the image. The gray levels of some pixels in an image are more common than those of others. Finally, the arithmetic encoding method is applied to reduce the coding redundancy of the image. To promote high performance of the arithmetic encoding method, the MTAC method first classifies the data and then encodes each cluster of data using a distinct code table. The experimental results show that, in most cases, the MTAC method provides a higher efficiency in use of storage space than the lossless JPEG2000 does.
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