10 January 2014 Analysis of mammogram images based on texture features of curvelet sub-bands
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Proceedings Volume 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013); 906924 (2014) https://doi.org/10.1117/12.2054183
Event: Fifth International Conference on Graphic and Image Processing, 2013, Hong Kong, China
Abstract
Image texture analysis plays an important role in object detection and recognition in image processing. The texture analysis can be used for early detection of breast cancer by classifying the mammogram images into normal and abnormal classes. This study investigates breast cancer detection using texture features obtained from the grey level cooccurrence matrices (GLCM) of curvelet sub-band levels combined with texture feature obtained from the image itself. The GLCM were constructed for each sub-band of three curvelet decomposition levels. The obtained feature vector presented to the classifier to differentiate between normal and abnormal tissues. The proposed method is applied over 305 region of interest (ROI) cropped from MIAS dataset. The simple logistic classifier achieved 86.66% classification accuracy rate with sensitivity 76.53% and specificity 91.3%.
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Syed Jamal Safdar Gardezi, Syed Jamal Safdar Gardezi, Ibrahima Faye, Ibrahima Faye, Mohamed Meselhy Eltoukhy, Mohamed Meselhy Eltoukhy, } "Analysis of mammogram images based on texture features of curvelet sub-bands", Proc. SPIE 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013), 906924 (10 January 2014); doi: 10.1117/12.2054183; https://doi.org/10.1117/12.2054183
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