Edge detection of angiogram images using the classical image processing techniques

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). In medical image processing, blood vessels need to be extracted clearly and properly from a noisy background, drift image intensity, and low contrast pose. 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. For this purpose we need vessel enhancement and segmentation. Segmentation is a process of partitioning a given image into several non-overlapping regions. Edge detection is an important task and in the literature, complex algorithms have been modeled for the detection of the edges of the blood vessels. In this paper, the edges of the vessels in the angiogram image are detected using the proposed algorithm which is done using the classical image processing techniques. This involves the Pre-processing step, where the noise is removed using a simple filter and Histogram equalization technique, instead of the Canny edge Detector. The proposed algorithm is not complicated but accurate and involves very simple steps.