Detection of microcalcifications in mammograms using error of prediction and statistical measures

A two-stage method for detecting microcalcifications in mammograms is presented. In the first stage, the determination of the candidates for microcalcifications is performed. For this purpose, a 2-D linear prediction error filter is applied, and for those pixels where the prediction error is larger than a threshold, a statistical measure is calculated to determine whether they are candidates for microcalcifications or not. In the second stage, a feature vector is derived for each candidate, and after a classification step using a support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test: The Alberta Program for the Early Detection of Breast Cancer with 50- m resolution, and the results are evaluated using a freeresponse receiver operating characteristics curve. Two different analyses are performed: an individual microcalcification detection analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average, respectively. The best performance is characterized by a sensitivity of 0.89, a specificity of 0.99, and a positive predictive value of 0.79. In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77 false positives per image, and a value of 0.90 is achieved at 0.94 false positive per imag

Segmentation-based lossless compression of burn wound images

Color images may be encoded by using a gray-scale image compression technique on each of the three color planes. Such an approach, however, does not take advantage of the correlation existing between the color planes. In this paper, a new segmentation-based lossless compression method is proposed for color images. The method exploits the correlation existing among the three color planes by treating each pixel as a vector of three components, performing region growing and difference operations using the vectors, and applying a color coordinate transformation. The method performed better than the Joint Photographic Experts Group (JPEG) standard by an average of 3.40 bits/pixel with a database including four natural color images of scenery, four images of burn wounds, and four fractal images, and it outperformed the Joint Bi-Level Image experts Group (JBIG) standard by an average of 3.01 bits/pixel. When applied to a database of 20 burn wound images, the 24 bits/pixel images were efficiently compressed to 4.79 bits/pixel, then requiring 4.16 bits/pixel less than JPEG and 5.41 bits/pixel less than JBIG

The Effect of External Loads and Cyclic Loading on Normal Patellofemoral Joint Signals.

Pain over the anterior portion of the knee joint is a common clinical complaint. A condition known as 'chondromalacia patella' (softening of the cartilage under the patella), which frequently causes anterior knee pain is difficult to diagnose and monitor. Vibrations detected by a contact transducer over the patellofemoral joint may be useful in the assessment of chondromalacia patella. This paper utilised this technique known as vibroarthrography (VAG), to study two potential sources of variability of the normal patellofemoral joint signal. The effect of increased muscular force on the VAG signal was measured by externally loading the joint. The effect of load history (cyclic loading) on the VAG signal was determined by comparing signals before, during, and after application of weights under similar cyclic loading conditions. Results indicated that external loading of the patellofemoral joint caused only minor signal variation. Cyclical loading of the joint, on the other hand, was determined to be a major source of variability of the normal patellofemoral joint signal, which must be controlled in future VAG tests

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions

Using epidemic simulators for monitoring an ongoing epidemic

Prediction of infection trends, estimating the efficacy of contact tracing, testing or impact of influx of infected are of vital importance for administration during an ongoing epidemic. Most effective methods currently are empirical in nature and their relation to parameters of interest to administrators are not evident. We thus propose a modified SEIRD model that is capable of modeling effect of interventions and inward migrations on the progress of an epidemic. The tunable parameters of this model bear relevance to monitoring of an epidemic. This model was used to show that some of the commonly seen features of cumulative infections in real data can be explained by piecewise constant changes in interventions and population influx. We also show that the data of cumulative infections from twelve Indian states between mid March and mid April 2020 can be generated from the model by applying interventions according to a set of heuristic rules. Prediction for the next ten days based on this model, reproduced real data very well. In addition, our model also reproduced the time series of recoveries and deaths. Our work constitutes an important first step towards an effective dashboard for the monitoring of epidemic by the administration, especially in an Indian context

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

Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows

Pathological conditions of knee joints have been observed to cause changes in the characteristics of vibroarthrographic (VAG) signals. Several studies have proposed many parameters for the analysis and classification of VAG signals; however, no statistical modeling methods have been explored to analyze the distinctions in the probability density functions (PDFs) between normal and abnormal VAG signals. In the present work, models of PDFs were derived using the Parzen-window approach to represent the statistical characteristics of normal and abnormal VAG signals. The Kullback-Leibler distance was computed between the PDF of the signal to be classified and the PDF models for normal and abnormal VAG signals. Additional statistical measures, including the mean, standard deviation, coefficient of variation, skewness, kurtosis, and entropy, were also derived from the PDFs obtained. An overall classification accuracy of 77.53%, sensitivity of 71.05%, and specificity of 82.35% were obtained with a database of 89 VAG signals using a neural network with radial basis functions with the leave-one-out procedure for cross validation. The screening efficiency was derived to be 0.8322, in terms of the area under the receiver operating characteristics curve. (C) 2009 Elsevier Ltd. All rights reserved

An unbiased linear artificial neural network with normalized adaptive coefficients for filtering noisy ECG signals

The electrocardiogram (ECG) is the most commonly used signal for diagnostic purposes in medicine. The adaptive filtering technique is suited for filtering ECG signals, which are inherently nonstationary. In this paper, we propose a novel neural-network-based adaptive filter to eliminate high-frequency random noise in ECG signals. We make use of a linear artificial neural network (ANN) with delayed values of the ECG time series as the filter inputs. The ANN does not contain a bias in its summation unit, and the coefficients are normalized. During the learning process, the normalized coefficients are used in the steepest-descent algorithm in order to achieve efficient online filtering of noisy ECG signals

An algorithm for evaluating the performance of adaptive filters for the removal of artifacts in ECG signals

Filtering electrocardiogram (ECG) signals calls for a filter whose impulse response can be automatically adjusted according to the varying characteristics of the signal and artifacts. In order to eliminate effectively the artifacts in ECG signals, we propose the unbiased linear artificial neural network (ULANN) as a new type of adaptive filter. This paper compares the performance of the ULANN filter with the prevailing least-mean-squares (LMS) and recursive-least-squares (RLS) adaptive filters, for the removal of artifacts in noisy ECG signals. The measures of performance include the root-mean-squared error, a normalized correlation coefficient (NCC), and entropy. A template derived from each ECG signal is used as a reference to derive the measures. The NCC values for the ULANN, LMS, and RLS filter, averaged over 22 ECG signals, are 0.9956 +/- 0.0022, 0.9948 +/- 0.0020, and 0.9940 +/- 0.0026, respectively. The results indicate that the ULANN filter provides filtered signals with the highest waveshape fidelity among the three filters studied