Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network
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초록
Mammogram images are sensitive in nature and even a minor change in the environment affects the qualityof the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In thispaper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet basedadaptive sigmoid function. The cascade feed-forward back propagation technique has been used for trainingand testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process theimages directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function andthen the suspicious regions were selected to extract the features. A combination of texture features and graylevelco-occurrence matrix features were extracted and used for training and testing purposes. The system wastrained with 150 images, while a total 100 mammogram images were used for testing. A classificationaccuracy of more than 95% was obtained with our proposed method.
Abstract
Mammogram images are sensitive in nature and even a minor change in the environment affects the qualityof the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In thispaper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet basedadaptive sigmoid function. The cascade feed-forward back propagation technique has been used for trainingand testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process theimages directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function andthen the suspicious regions were selected to extract the features. A combination of texture features and graylevelco-occurrence matrix features were extracted and used for training and testing purposes. The system wastrained with 150 images, while a total 100 mammogram images were used for testing. A classificationaccuracy of more than 95% was obtained with our proposed method.
참고문헌 (37)
- "Cancer facts & figures 2013" (2013)[URL]
- H. D. Cheng, "Computer-aided detection and classification of microcalcifications in mammograms : a survey", Pattern Recognition, 36(12), pp.2967~2991 (2003)
- P. Skaane, "Interobserver variation in the interpretation of breast imaging : comparison of mammography, ultrasonography, and both combined in the interpretation of palpable noncalcified breast masses", Acta Radiologica, 38(4), pp.497~502 (1997)
- M. Kallergi, "Image segmentation in digital mammography : comparison of local thresholding and region growing algorithms", Computerized Medical Imaging and Graphics, 16(5), pp.323~331 (1992)
- Z. Lu, "Contourlet based mammographic image enhancement", Proceedings of SPIE 6534: Fifth International Conference on Photonics and Imaging in Biology and Medicine, pp.65340M~65340M (2007), International Society for Optics and Photonics
- A. Laine, "Wavelets for contrast enhancement of digital mammography", IEEE Engineering in Medicine and Biology Magazine, 14(5), pp.536~550 (1995)
- A. Laine, "Mammographic feature enhancement by multiscale analysis", IEEE Transactions on Medical Imaging, 13(4), pp.725~774 (1994)
- A. Mencattini, "Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing", IEEE Transactions on Instrumentation and Measurement, 57(7), pp.1422~1430 (2008)
- P. Sakellaropoulos, "A wavelet-based spatially adaptive method for mammographic contrast enhancement", Physics in Medicine and Biology, 48(6), pp.787~803 (2003)
- P. Heinlein, "Integrated wavelets for enhancement of microcalcifications in digital mammography", IEEE Transactions on Medical Imaging, 22(3), pp.402~413 (2003)
- S. Skiadopoulos, "Digital Mammography", pp.549~556 (2006), Springer
- C. M. Chang, "Coherence of multiscale features for enhancement of digital mammograms", IEEE Transactions on Information Technology in Biomedicine, 3(1), pp.32~46 (1999)
- G. Derado, "Wavelet image interpolation (WII): a wavelet-based approach to enhancement of digital mammography images", Proceedings of the 3rd Symposium on Bioinformatics Research and Applications (ISBRA), pp.203~214 (2007)
- F. Y. Lure, "Multiresolution unsharp masking technique for mammogram image enhancement", Proceedings of SPIE 2710: Medical Imaging 1996, pp.830~839 (1996), International Society for Optics and Photonics
- J. Scharcanski, "Denoising and enhancing digital mammographic images for visual screening", Computerized Medical Imaging and Graphics, 30(4), pp.243~254 (2006)
- J. Tang, "A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms", IEEE Journal of Selected Topics in Signal Processing, 3(1), pp.74~80 (2009)
- H. D. Cheng, "A novel fuzzy logic approach to mammogram contrast enhancement", Information Sciences, 148(1), pp.167~184 (2002)
- F. Sahba, "Contrast enhancement of mammography images using a fuzzy approach", Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS2008), pp.2201~2204 (2008)
- I. Stephanakis, "Enhancement of medical images using a fuzzy model for segment dependent local equalization", Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (ISPA2003), pp.970~975 (2003)
- J. Jiang, "Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement", Computerized Medical Imaging and Graphics, 29(1), pp.83~90 (2005)
- S. Aghagolzadeh, "Transform image enhancement", Optical Engineering, 31(3), pp.614~626 (1992)
- N. Petrick, "An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection", IEEE Transactions on Medical Imaging, 15(1), pp.59~67 (1996)
- W. Qian, "Tree-structured nonlinear filters in digital mammography", IEEE Transactions on Medical Imaging, 13(1), pp.25~36 (1994)
- Y. Zhou, "Human visual system based mammogram enhancement and analysis", Proceedings of 2010 2nd International Conference on Image Processing Theory Tools and Applications (IPTA), pp.229~234 (2010)
- A. P. Dhawan, "Enhancement of mammographic features by optimal adaptive neighborhood image processing", IEEE Transactions on Medical Imaging, 5(1), pp.8~15 (1986)
- V. H. Guis, "Adaptive neighborhood contrast enhancement in mammographic phantom images", Optical Engineering, 42(2), pp.357~366 (2003)
- G. Ramponi, "Nonlinear unsharp masking methods for image contrast enhancement", Journal of Electronic Imaging, 5(3), pp.353~366 (1996)
- Z. Wu, "Digital mammography image enhancement using improved unsharp masking approach", Proceedings 2010 3rd International Congress on Image and Signal Processing (CISP), pp.668~672 (2010)
- R. J. Ferrari, "Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets", IEEE Transactions on Medical Imaging, 20(9), pp.953~964 (2010)
- R. M. Rangayyan, "Analysis of bilateral asymmetry in mammograms using directional, morphological, and density features", Journal of Electronic Imaging, 16(1) (2007)
- F. F. Yin, "Computerized detection of masses in digital mammograms : automated alignment of breast images and its effect on bilateral‐subtraction technique", Medical Physics, 21(3), pp.445~452 (1994)
- N. Petrick, "Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms", Medical Physics, 26(8), pp.1642~1654 (1999)
- R. M. Rangayyan, "Measures of acutance and shape for classification of breast tumors", IEEE Transactions on Medical Imaging, 16(6), pp.799~810 (1997)
- B. Sahiner, "Classification of mass and normal breast tissue : a convolution neural network classifier with spatial domain and texture images", IEEE Transactions on Medical Imaging, 15(5), pp.598~610 (1996)
- S. Kumar, "Soft Computing Techniques in Engineering Applications", pp.59~74 (2014), Springer
- S. Lal, "Efficient algorithm for contrast enhancement of natural images", International Arab Journal of Information Technology, 11(1), pp.95~102 (2014)
- K. Panetta, "Nonlinear unsharp masking for mammogram enhancement", IEEE Transactions on Information Technology in Biomedicine, 15(6), pp.918~928 (2011)