04-06-2013, 01:58 PM
Edge Detection of Angiogram Images Using the Classical Image Processing Techniques
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Abstract
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 Preprocessing
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.
INTRODUCTION
Segmentation plays a vital role in the detection of blood
vessels in an angiogram image. It is a process of partitioning
an angiogram into several non-overlapping regions. Thus it is
used to extract the vascular and background regions. Based on
the partitioning results, surfaces of vasculatures can be
extracted, modelled, manipulated, measured and visualized.
Hence it is used to detect the various vascular diseases.
Therefore, developing reliable and robust image segmentation
methods for angiography has been the priority and by the
other research groups [4, 10, 11].
EXISITNG METHOD
CANNY EDGE DETECTOR
The Canny edge detection operator was developed in the
year 1986 by John.F.Canny. He used a multi-stage algorithm
to detect a wide range of edges in the images. The following
are the various stages of the Canny Edge Detection algorithm.
A. Noise Reduction
The Canny edge detector uses the first derivative of a
Gaussian as a filter. It filters the noise by convolving the raw
image with the Gaussian filter. By doing so, we obtain a
slightly blurred version of the original image but it is not
affected by a single noisy pixel to any significant degree.
Thus, it is used to remove the noise present on raw
unprocessed data.
Intensity Gradient
This algorithm uses four filters to detect the various edges in
the blurred image. Since an edge in the image may point in a
variety of directions, horizontal, vertical and diagonal edges
are to be detected. This is done by calculating the gradient of
the pixel relative to its neighborhood. A good approximation
of the first derivate is given by the two Sobel operators .Since
the derivatives enhance noise; the smoothing effect is a
particularly attractive feature of the Sobel operators. First
derivatives are implemented using the magnitude of the
gradient.
CONCLUSION
In this paper, the proposed algorithm detects the edges of
the blood vessel from the given angiogram image using the
classical image processing techniques. The edges segmented
are accurate and clear as compared to the canny edge detection
and the steps involved to obtain the edges of the blood vessel
are simple and easy to implement. The results provide that the
proposed algorithm is effective and efficient in detecting the
edges. The future work will focus on developing an algorithm
for detecting the blocks and types of disease in the angiogram
image in a simpler way, as of the existing methods. Thus a
more generalized algorithm will be addressed in the near
future.