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Statistical measures of orientation of texture for the detection of architectural distortion in prior mammograms of interval-cancer

[+] Author Affiliations
Jayasree Chakraborty

University of Calgary, Department of Electrical and Computer Engineering, Schulich School of Engineering, Calgary, Alberta, Canada T2N 1N4

Indian Institute of Technology Kharagpur, Department of Electronics and Electrical Communication Engineering, West Bengal, India 721 302

Rangaraj M. Rangayyan

University of Calgary, Department of Electrical and Computer Engineering, Schulich School of Engineering, Calgary, Alberta, Canada T2N 1N4

University of Calgary, Department of Radiology, Calgary, Alberta, Canada T2N 1N4

Shantanu Banik

University of Calgary, Department of Electrical and Computer Engineering, Schulich School of Engineering, Calgary, Alberta, Canada T2N 1N4

Sudipta Mukhopadhyay

Indian Institute of Technology Kharagpur, Department of Electronics and Electrical Communication Engineering, West Bengal, India 721 302

J. E. Leo Desautels

University of Calgary, Department of Electrical and Computer Engineering, Schulich School of Engineering, Calgary, Alberta, Canada T2N 1N4

J. Electron. Imaging. 21(3), 033010 (Aug 22, 2012). doi:10.1117/1.JEI.21.3.033010
History: Received December 21, 2011; Revised May 31, 2012; Accepted June 20, 2012
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Abstract.  Architectural distortion is an important sign of early breast cancer. Due to its subtlety, it is often missed during screening. We propose a method to detect architectural distortion in prior mammograms of interval-cancer cases based on statistical measures of oriented patterns. Oriented patterns were analyzed in the present work because regions with architectural distortion contain a large number of tissue structures spread over a wide angular range. Two new types of cooccurrence matrices were derived to estimate the joint occurrence of the angles of oriented structures. Statistical features were computed from each of the angle cooccurrence matrices to discriminate sites of architectural distortion from falsely detected regions in normal parts of mammograms. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases with the application of Gabor filters and phase portrait analysis. For each ROI, Haralick’s 14 features were computed using the angle cooccurrence matrices. The best result obtained in terms of the area under the receiver operating characteristic (ROC) curve with the leave-one-patient-out method was 0.76; the free-response ROC curve indicated a sensitivity of 80% at 4.2 false positives per patient.

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Citation

Jayasree Chakraborty ; Rangaraj M. Rangayyan ; Shantanu Banik ; Sudipta Mukhopadhyay and J. E. Leo Desautels
"Statistical measures of orientation of texture for the detection of architectural distortion in prior mammograms of interval-cancer", J. Electron. Imaging. 21(3), 033010 (Aug 22, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.3.033010


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