Blood Vessel Segmentation in Angiograms:

Abstract—Angiography is a widely used procedure for vessel observation in both clinical routine and medical research. Often for the subsequent analysis of the vasculature it is needed to measure the angiogram area covered by vessels and/or the vessel  length. For this purpose we need vessel enhancement and segmentation. In this paper, we evaluate the performance of a fuzzy inference system and morphology filters for blood vessel segmentation in a noise angiograms image.

Existing System

Edge detection is an essential task in computer vision. It covers a wide range of application, from segmentation to pattern  matching. It reduces the complexity of the image allowing more costly algorithms like object recognition, object matching , object registration , or surface reconstruction from stereo images to be used. Their detection is interesting for different goals. They can be used to measure parameters related to blood flow or to locate some patterns in relation to vessels in angiographic images. They can also be used as a first step before registration. Conventionally edge is detected according to some early brought forward algorithms like sobel algorithm, prewitt algorithm and Laplacian of Gaussian operator.

Disadvantages

But in theory they belong to the high pass filtering, which are not fit for noise medical image edge detection because noise and edge belong to the scope of high frequency.

In real world applications, medical images contain object boundaries and

object shadows and noise. Therefore, they may be difficult to distinguish the exact edge from noise or trivial geometric features.

Proposed system

we novel a fuzzy inference system and morphology filters for vessel edge detection or vessel segmentation. Figure1 depicts the applied process.

Advantages of Proposed System:

The fuzzy inference rules were defined in such a way that the FIS system output ("Edges") is high only for those pixels belonging to edges in the input image. A robustness to contrast and lighting variations were also in mind when these rules were established

In the mathematical morphology theory, images are treated as sets, and morphology transformations which derived from Minkowski addition and subtraction are defined to extract features in images.

As the performance of classic edge detectors degrades with noise, morphology edge detector has been studied.

Morphological operation is used to detect image edge, and at same time, denoise the image.

SYSTEM REQUIREMENT

Hardware Requirements

        Processor           :       Pentium III / IV

        Hard Disk           :       40 GB

        Ram                  :       256 MB

Monitor              :       15VGA Color

        Mouse               :       Ball / Optical

        Keyboard           :       102 Keys

Software Requirements

Operating System:      Windows XP professional

Front End           :       Microsoft Visual Studio .Net 2005

Language           :       Visual C#.Net,

Back End           :       Sql Server 2000 and above