Adaptive Edge Detection Technique Towards Features Extraction from Mammogram Images
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.
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.
Stewart, et al. World Cancer Report, Lyon, France: IARC Press 2003.
Ferlay, et al. GLOBOCAN 2002: cancer incidence mortality and prevalence worldwide, IARC Cancer Base, Lyon France: IARC Press, 2004.
Ojala, et al. Accurate Segmentation of the Breast Region from Digitized Mammograms. Computerized Medical Imaging and Graphics 2001; 25(1): 47-59. http://dx.doi.org/10.1016/S0895-6111(00)00036-7
Chandrasekhar, et al. A simple method for automatically locating the nipple on mammograms. The Institute of Electrical and Electronics Engineers Transactions on Medical Imaging 1997; 16(5): 483-494. http://dx.doi.org/10.1109/42.640738
Gonzalez, et al. Digital Image Processing, 2nd edition, Prentice Hall, Upper Saddle River, NJ, 2002.
Rosin, et al. Evaluation of global image thresholding for change detection. Pattern Recognit Lett 2003; 24: 2345-56. http://dx.doi.org/10.1016/S0167-8655(03)00060-6
Roberts. Machine Perception of 3-D Solids, Optical and Electro-optical Information Processing, MIT Press, 1965.
Prewitt. Object Enhancement and Extraction in Picture processing and Psychopictorics, Academic Press, 1970.
Marr and Hildrith. Theory of Edge Detection. Proc Royal Society of London 1980; B207: 187-217.
Canny. A Computational Approach to Edge Detection. IEEE Trans Pattern Analysis and Machine Intelligence 1986; 8: 679-714.
Frei and Chen. Fast Boundary Detection: A Generalization and New Algorithm. IEEE Trans Computers 1977; C-26(10): 988-998. http://dx.doi.org/10.1109/TC.1977.1674733
Zhang, et al. Image Edge Detection Using Hidden Markov Chain Model Based on the Non-decimated Wavelet. International Journal of Signal Processing and Image Processing and Pattern 2009; 2(1): 109-118.
Jassim. Semi-Optimal Edge Detector based on Simple Standard Deviation with Adjusted Thresholding. International Journal of Computer Applications (0975 – 8887) 2013; 68(2): 43-48.
Woods, et al. A Sobel Edge Detection Algorithm Based System for Analyzing and Classifying Image Based Spam. Journal of Emerging Trends in Computing and Information Sciences 2012; 3(4): 506-511.
Maitra, et al. Automated Digital Mammogram Segmentation for Detection of Abnormal Masses Using Binary Homogeneity Enhancement Algorithm. IJCSE (0976-5166) 2011; 2(3): 415-427.