NDLI: Detection of Narrowed Coronary Arteries in X-ray Angiographic Images using Contour processing of Segmented Heart Vessels based on Hessian Vesselness Filter and Wavelet based Image Fusion

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Detection of Narrowed Coronary Arteries in X-ray Angiographic Images using Contour processing of Segmented Heart Vessels based on Hessian Vesselness Filter and Wavelet based Image Fusion

Content Provider	Semantic Scholar
Author	Seyed, Mostafa Mirhassani Elham, Ghasemi
Copyright Year	2011
Abstract	In this paper a method for Detection of narrowed coronary arteries in X-ray Angiographic images is addressed. For this purpose firstly coronary arteries are segmented base on some probability masks which indicates the membership function of being a vessel for each pixel in the image. In order to build the membership functions whole of angiographic frames in the sequence are utilized. To this aim, angiographic sequence is filtered by the Hessian based vesselness filter to increase discrimination between the vascular structures and the background. Next, a 2D discrete wavelet transform is employed for fusion of the angiographic frames and their filtered versions. In the next step, the heart vessels are detected by applying the fused image with a couple of constant values as threshold coefficients. The coefficients are employed to adjust the effect of threshold. As a consequence, two images containing the detected vessels are yielded. After that, in order to mask out the redundant particles in the result morphological operations are employed. In order to find narrowed vessels their contours are obtained and the thickness of vessels is measured. A predefined threshold is employed to find the abrupt decrease in the vessel thickness. Experiments demonstrate the efficiency of the proposed method in order to detect the narrowed heart vessels in X-ray angiographic frames.
Starting Page	27
Ending Page	33
Page Count	7
File Format	PDF HTM / HTML
Volume Number	36
Alternate Webpage(s)	https://www.ijcaonline.org/archives/volume36/number9/4520-6416?format=pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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