hi! i would like to have the matlab code for edge detection of blood vessel of angiogram images using classical image processing techniques like filtering first and then applying histogram equalization technique.
i am in my final year of graduation at NIT-Agartala and requires help for my project.
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Edge detection is an important technique in the area of image processing with wide applications in medical images. Angiography is an imaging technique used to locate the position of blood vessels in different parts of the human body. X-rays, computerized tomography (computed tomography) and magnetic resonance imaging (MR) are the medical imaging techniques used to visualize the blood vessels of the human body. Many methods of edge detection have been proposed for many years. Researchers use the classic edge detection process for different applications, but these methods are not effective for edge detection of angiogram images. Therefore, an improved edge detection algorithm is proposed to determine the fine edges of blood vessels. The proposed algorithm involves very simple steps and provides more precision.
Angiogram images are degraded by the lack of sharpness and noise of medical imaging systems. Edge detection is a difficult task in noisy images, since both noise and edges contain high-frequency components. Attempts have been made to reduce the noise that produces fuzzy and distorted edges. There are a variety of operators that are used in such images, so they can average enough data to discount the localized noisy pixels. The result is less precise in relation to the location of the detected edges. Very few edges imply a step change in intensity. Visual effects such as poor focus or refraction can result in objects with limits defined by a gradual change in intensity. The operator must be chosen to respond to such a gradual change in those cases. Therefore, there are problems of detection of false edges, missing true edges, edge location, high computation time and problems due to noise, etc. Therefore, the objective is to compare several edge detection techniques and analyze the performance of these techniques in different conditions. There are several ways to perform edge detection.