Early Detection of stenosis in Coronary Artery using Adaboost and ANN Classification
R.Reena Roy1, Maanasi.k2, Pavithra.R3, Rekha.M4
1R.Reena Roy,Assistant Professor, IT Department, Easwari Engineering college, Chennai, India.
2Maanasi.K, IT Department, Easwari Engineering College, Chennai, India
3Pavithra.R, IT Department, Easwari Engineering College, Chennai, India
4Rekha.M,, IT Department, Easwari Engineering College, Chennai, India
Manuscript received on October 17, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 4352-4356 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5023119119/2019©BEIESP | DOI: 10.35940/ijitee.A5023.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Coronary Artery Disease, CT Image, Image Segmentation, Stenosis, Texture Extraction
Scope of the Article: Classification