Automatic intensity windowing of mammographic images based on a perceptual metric

[EN] Purpose: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. Methods: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at . Results: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. Conclusions: A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram. (C) 2017 American Association of Physicists in MedicineThis work has the support of IST S.L., University of Valencia (CPI15170), Consolider (CPAN13TR01), MINETUR (TSI1001012013019) and IFIC (Severo Ochoa Centre of Excellence SEV20140398). The authors would also like to thank C. Bellot M.D., M. Brouzet M.D., C. Calabuig M.D., J. Camps M.D., J. Coloma M.D., D. Erades M.D., Mr. V. Gutierrez, J. Herrero M.D., Dr. I. Maestre, Dr. A. Neco M.D., C. Ortola M.D., A. Rubio M.D., Dr. R. Sanchez, Dr. F. Sellers, A. Segura M.D., and the Spanish Cancer Association (AECC) for their effort, participation, counseling, and commitment in this research study. The authors report no conflicts of interest in conducting the research.Albiol Colomer, A.; Corbi, A.; Albiol Colomer, F. (2017). Automatic intensity windowing of mammographic images based on a perceptual metric. Medical Physics. 44(4):1369-1378. https://doi.org/10.1002/mp.12144S13691378444Maidment, A. D. A., Fahrig, R., & Yaffe, M. J. (1993). Dynamic range requirements in digital mammography. Medical Physics, 20(6), 1621-1633. doi:10.1118/1.596949Kimpe, T., & Tuytschaever, T. (2006). Increasing the Number of Gray Shades in Medical Display Systems—How Much is Enough? Journal of Digital Imaging, 20(4), 422-432. doi:10.1007/s10278-006-1052-3ACR, AAPM, and SIIM Practice parameter for determinants of image quality in digital mammography 2014Committee DS PS3.3 information object definitions 2015Pisano, E. D., Chandramouli, J., Hemminger, B. M., Glueck, D., Johnston, R. E., Muller, K., … Pizer, S. (1997). The effect of intensity windowing on the detection of simulated masses embedded in dense portions of digitized mammograms in a laboratory setting. Journal of Digital Imaging, 10(4), 174-182. doi:10.1007/bf03168840Börjesson, S., Håkansson, M., Båth, M., Kheddache, S., Svensson, S., Tingberg, A., … Månsson, L. G. (2005). A software tool for increased efficiency in observer performance studies in radiology. Radiation Protection Dosimetry, 114(1-3), 45-52. doi:10.1093/rpd/nch550Sahidan, S. I., Mashor, M. Y., Wahab, A. S. W., Salleh, Z., & Ja’afar, H. (s. f.). Local and Global Contrast Stretching For Color Contrast Enhancement on Ziehl-Neelsen Tissue Section Slide Images. 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, 583-586. doi:10.1007/978-3-540-69139-6_146Ganesan, K., Acharya, U. R., Chua, C. K., Min, L. C., Abraham, K. T., & Ng, K.-H. (2013). Computer-Aided Breast Cancer Detection Using Mammograms: A Review. IEEE Reviews in Biomedical Engineering, 6, 77-98. doi:10.1109/rbme.2012.2232289Papadopoulos, A., Fotiadis, D. I., & Costaridou, L. (2008). Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Computers in Biology and Medicine, 38(10), 1045-1055. doi:10.1016/j.compbiomed.2008.07.006Panetta, K., Yicong Zhou, Agaian, S., & Hongwei Jia. (2011). Nonlinear Unsharp Masking for Mammogram Enhancement. IEEE Transactions on Information Technology in Biomedicine, 15(6), 918-928. doi:10.1109/titb.2011.2164259Rogowska, J., Preston, K., & Sashin, D. (1988). Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs. IEEE Transactions on Biomedical Engineering, 35(10), 817-827. doi:10.1109/10.7288Ramponi, G. (1998). Rational unsharp masking technique. Journal of Electronic Imaging, 7(2), 333. doi:10.1117/1.482649Rangayyan, R. M., Liang Shen, Yiping Shen, Desautels, J. E. L., Bryant, H., Terry, T. J., … Rose, M. S. (1997). Improvement of sensitivity of breast cancer diagnosis with adaptive neighborhood contrast enhancement of mammograms. IEEE Transactions on Information Technology in Biomedicine, 1(3), 161-170. doi:10.1109/4233.654859Tang, J., Liu, X., & Sun, Q. (2009). A Direct Image Contrast Enhancement Algorithm in the Wavelet Domain for Screening Mammograms. IEEE Journal of Selected Topics in Signal Processing, 3(1), 74-80. doi:10.1109/jstsp.2008.2011108LINGURARU, M., MARIAS, K., ENGLISH, R., & BRADY, M. (2006). A biologically inspired algorithm for microcalcification cluster detection. Medical Image Analysis, 10(6), 850-862. doi:10.1016/j.media.2006.07.004Tsai, D.-Y., Lee, Y., & Matsuyama, E. (2007). Information Entropy Measure for Evaluation of Image Quality. Journal of Digital Imaging, 21(3), 338-347. doi:10.1007/s10278-007-9044-5Sheikh, H. R., & Bovik, A. C. (2006). Image information and visual quality. IEEE Transactions on Image Processing, 15(2), 430-444. doi:10.1109/tip.2005.859378Tourassi, G. D., Vargas-Voracek, R., Catarious, D. M., & Floyd, C. E. (2003). Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information. Medical Physics, 30(8), 2123-2130. doi:10.1118/1.1589494Tourassi, G. D., Harrawood, B., Singh, S., Lo, J. Y., & Floyd, C. E. (2006). Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms. Medical Physics, 34(1), 140-150. doi:10.1118/1.2401667Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.819861Choi LK Goodall T Bovik AC Perceptual Image Enhancement. Encyclopedia of Image ProcessingFogel, I., & Sagi, D. (1989). Gabor filters as texture discriminator. Biological Cybernetics, 61(2). doi:10.1007/bf00204594Jain, A. K., Ratha, N. K., & Lakshmanan, S. (1997). Object detection using gabor filters. Pattern Recognition, 30(2), 295-309. doi:10.1016/s0031-3203(96)00068-4Vazquez-Fernandez, E., Dacal-Nieto, A., Martin, F., & Torres-Guijarro, S. (2010). Entropy of Gabor Filtering for Image Quality Assessment. Image Analysis and Recognition, 52-61. doi:10.1007/978-3-642-13772-3_6Rangayyan, R. M., Ayres, F. J., & Leo Desautels, J. E. (2007). A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute, 344(3-4), 312-348. doi:10.1016/j.jfranklin.2006.09.003Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., … Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045-1057. doi:10.1007/s10278-013-9622-7Task Group 18 Imaging Informatics Subcommittee Assessment of display performance for medical imaging systems 2005A. C. of Radiology Committee Bi-rads atlas 5th edition 2014Hochberg, Y., & Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine, 9(7), 811-818. doi:10.1002/sim.4780090710Keselman, H. J., & Keselman, J. C. (1984). The analysis of repeated measures designs in medical research. Statistics in Medicine, 3(2), 185-195. doi:10.1002/sim.4780030211Mauchly, J. W. (1940). Significance Test for Sphericity of a Normal $n$-Variate Distribution. The Annals of Mathematical Statistics, 11(2), 204-209. doi:10.1214/aoms/1177731915Samei, E., Badano, A., Chakraborty, D., Compton, K., Cornelius, C., Corrigan, K., … Willis, C. E. (2005). Assessment of display performance for medical imaging systems: Executive summary of AAPM TG18 report. Medical Physics, 32(4), 1205-1225. doi:10.1118/1.1861159Haghighat, M., Zonouz, S., & Abdel-Mottaleb, M. (2015). CloudID: Trustworthy cloud-based and cross-enterprise biometric identification. Expert Systems with Applications, 42(21), 7905-7916. doi:10.1016/j.eswa.2015.06.02

Optimized complex power quality classifier using one vs. rest support vector machine

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

Hybrid Features of Mask Generated with Gabor Filter for Texture Analysis and Sobel Operator for Image Regions Segmentation Using K-Means Technique

To make the image easily represented for more analysis and processing the segmentation procedure is required, where the image is portioned into its formed regions using some segmentation techniques based on features extraction. In this paper, a proposed procedure for finding the regions that formed the image is achieved based on hybrid features in two different components of different two colors spaces L*a*b* and RGB segmented by the k-means method. The hybrid features which comprise the mask segmentation are a combination of texture image characterization extracted by the Gabor filter and gradient image intensity by the Sobel operator after image quality enhancement by applying wiener filter noise reduction and contrast enhancement using Contrast limited adaptive equalization (CLAHE). Some statistical metrics are used for evaluating the performance of the proposed work stages