Affiliation:
1. B. P. Poddar Institute of Management Technology, India
2. University of Calcutta, India
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 α = 0.025. Moreover the proposed edge is single pixeled and connected which is very significant for medical edge detection.
Reference43 articles.
1. A completely automated CAD system for mass detection in a large mammographic database
2. Bick, U., & Doi, K. (2000). Computer aided diagnosis tutorial on computer aided-diagnosis. CARS 2000, Hyatt Regency, San Francisco, CA.
3. Superior performances of the neural network on the masses lesions classification through morphological lesion differences.;U.Bottigli;International Journal of Biomedical Science,2006
4. A Computational Approach to Edge Detection
5. Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network