Open AccessEarly Detection of stenosis in Coronary Artery using Adaboost and ANN Classification
Author(s) -
Roudi Nazarinia Roy,
K Maanasi,
R. Pavithra,
M Rekha.
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a5023.119119
Subject(s) - preprocessor , artificial intelligence , stenosis , segmentation , adaboost , medicine , computer vision , radiology , artery , computer science , pattern recognition (psychology) , cardiology , classifier (uml)
In recent years, due to the prevailing challenges in cardiovascular diseases in human , early detection of severity of stenosis has become essential. In this paper, an image processing method for detecting and localizing the regions in the coronary artery for segmentation is proposed.. This method works with a set of CT reconstructed images and gives much precision in detecting stenosis and helps clinical physicians for better diagnostic decision making process. CT is the widely used imaging technique to assess these kind of artery diseases. Ada Boost algorithm and colour based segmentation are used to exactly find out the regions in the artery. This method is applied on the reconstructed CT image of the heart along with preprocessing techniques for the detection of stenosis and center lines of the segmented arteries are extracted using texture extraction method. The CT images data set are collected from Sri Chakra scan centre, Chennai.