Adaptive Edge Detection Technique Towards Features Extraction from Mammogram Images

Authors

  • Indra Kanta Maitra B.P.Poddar Institute of Management and Technology, Kolkata, WB, India
  • Sangita Bhattacharjee Dept. of Computer Sc. and Engg, University of Calcutta, Kolkata, WB, India
  • Debnath Bhattacharyya Bharati Vidyapeeth Deemed University College of Engineering, Pune, Maharashtra, India
  • Tai-Hoon Kim Sungshin Women's University, Dongseon-dong 3-ga, Seoul, Korea
  • Samir Kumar Bandyopadhyay Dept. of Computer Sc. and Engg, University of Calcutta, Kolkata, WB, India

DOI:

https://doi.org/10.6000/1929-2279.2016.05.02.2

Keywords:

Mammogram, CAD, Edge Detection, Full and Complete Binary Tree, Adaptive Threshold, Histogram, Average Bin Distance (ABD), Maximum Difference Threshold (MDT), Prominent Bins, t-Test.

Abstract

Cancer is one of the most dreaded diseases of modern world. Breast cancer is the second most type of cancer & the fifth most common cause of cancer related death so it’s a significant public health problem in the world especially for elderly females. Computer technology specifically computer aided diagnosis (CAD), relatively young interdisciplinary technology, has had a tremendous impact on medical diagnosis of cancer detection due to its accuracy and cost effectiveness. 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. The mammogram is a comparatively efficient and low cost diagnostic imaging technique for breast cancer detection. In this paper a robust mammogram enhancement and edge detection algorithm is proposed, using tree-based adaptive thresholding technique. The proposed technique has been compared with different classical edge-detection techniques yielding acceptable out come. The proposed edge-detection algorithm showing 0.07 p-values and 2.411 t-stat in one sample two tail t-test ( = 0.025). The edge is single pixeled and connected which is very significant for medical edge-detection.

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Published

2016-03-29

How to Cite

Indra Kanta Maitra, Sangita Bhattacharjee, Debnath Bhattacharyya, Tai-Hoon Kim, & Samir Kumar Bandyopadhyay. (2016). Adaptive Edge Detection Technique Towards Features Extraction from Mammogram Images. Journal of Cancer Research Updates, 5(2),  47–58. https://doi.org/10.6000/1929-2279.2016.05.02.2

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