Comparative Study of Techniques for Edge Detection of Angiogram Images using Classical Image Processing

In medical image processing, blood vessels need to be extracted clearly and properly from a noisy background, drift image intensity, and low contrast pose. The Blood vessels of the human body can be visualized using many medical imaging methods such as X-ray, Computed Tomography (CT), and Magnetic Resonance (MR). Angiography is a procedure widely used for the observation of the blood vessels in medical research, where the angiogram area covered by vessels and/or the vessel length is required. Vessel enhancement and segmentation is an effective technique used in angiogram. Segmentation is a process of partitioning a given image into several non-overlapping regions. Edge detection is an important task and in this process and complex algorithms have been modeled for the detection of the edges of the blood vessels. This paper discusses a comparative study of digital image-processing algorithm for detecting the edges of the vessels in the angiogram images.