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Adaptive Edge Detection Method towards Features Extraction from Diverse Medical Imaging Technologies
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Author(s): Indra Kanta Maitra (B. P. Poddar Institute of Management Technology, India)and Samir Kumar Bandhyopadhyaay (University of Calcutta, India)
Copyright: 2017
Pages: 34
Source title:
Intelligent Multidimensional Data Clustering and Analysis
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India), Sourav De (Cooch Behar Government Engineering College, India), Indrajit Pan (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-5225-1776-4.ch007
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
The CAD is a relatively young interdisciplinary technology, has had a tremendous impact on medical diagnosis specifically cancer detection. The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm. To achieve accurate segmentation, an efficient edge-detection algorithm is essential. Medical images like USG, X-Ray, CT and MRI exhibit diverse image characteristics but are essentially collection of intensity variations from which specific abnormalities are needed to be isolated. In this chapter a robust medical image enhancement and edge detection algorithm is proposed, using tree-based adaptive thresholding technique. It has been compared with different classical edge-detection techniques using one sample two tail t-test to exam whether the null hypothesis can be supported. The proposed edge-detection algorithm showing 0.07 p-values and 2.411 t-stat where a = 0.025. Moreover the proposed edge is single pixeled and connected which is very significant for medical edge detection.
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