ПОИСК СТРУКТУРНЫХ ОСОБЕННОСТЕЙ СТРОЕНИЯ ТОМОГРАФИЧЕСКИХ ИЗОБРАЖЕНИЙ НА ОСНОВЕ КОНЦЕПЦИИ АКТИВНЫХ АГЕНТОВ

Предлагается алгоритм поиска особенностей строения томографических изображений, основанный на концепции самоорганизующихся агентных систем. В реализованной мультиагентной системе поставленная задача решается коллективно за счет конкуренции автономных агентов двух типов. Работа мультиагентного алгоритма демонстрируется на примере задачи поиска закономерностей строения, связанных с туберкулезом легких. Эффективность предложенного алгоритма оценивается на достаточно большой базе данных трехмерных КТ-изображений, включающей томограммы грудной клетки 111 пациентов общим объемом около 10 000 слоев

A novel shape feature to classify microcalcifications

Clinical evident shows that the shape of mammographic calcification is an indicator of the pathology. Microcalcifications (MC) with rough shape are early signs of malignant breast cancer. This thesis proposed a shape metric to help radiologist in classifying regions of interest. Region growing and gradient vector flow algorithm are used to obtain the contour of MC to calculate the normalized distance signature. A three level wavelet decomposition with a Daubechies eight tap wavelet is used to provide a bandpass function and extract the desired shape feature of the MC. A comparison with previously used shape features such as compactness, moment, Fourier descriptors is provided. 58 malignant and 125 benign cases, totaling 368 individual MC, are tested by the proposed method and previously used shape features

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

A biologically inspired algorithm for microcalcification cluster detection

Abstract — The early detection of breast cancer greatly improves prognosis. One of the earliest signs of cancer is the formation of clusters of microcalcifications. We introduce a novel method for microcalcification detection based on a biologically inspired adaptive model of contrast detection. This model is used in conjunction with image filtering based on anisotropic diffusion and curvilinear structure removal using local energy and phase congruency. An important practical issue in automatic detection methods is the selection of parameters: we show that the parameter values for our algorithm can be estimated automatically from the image. This way, the method is made robust and essentially free of parameter tuning. We report results on mammograms from two databases and show that the detection performance can be improved by first including a normalisation scheme. Index Terms — X-ray mammography; microcalcification clusters; image normalization; anisotropic diffusion; human visual system. 2 I

Segmentation, Super-resolution and Fusion for Digital Mammogram Classification

Mammography is one of the most common and effective techniques used by radiologists for the early detection of breast cancer. Recently, computer-aided detection/diagnosis (CAD) has become a major research topic in medical imaging and has been widely applied in clinical situations. According to statics, early detection of cancer can reduce the mortality rates by 30% to 70%, therefore detection and diagnosis in the early stage are very important. CAD systems are designed primarily to assist radiologists in detecting and classifying abnormalities in medical scan images, but the main challenges hindering their wider deployment is the difficulty in achieving accuracy rates that help improve radiologists’ performance. The detection and diagnosis of breast cancer face two main issues: the accuracy of the CAD system, and the radiologists’ performance in reading and diagnosing mammograms. This thesis focused on the accuracy of CAD systems. In particular, we investigated two main steps of CAD systems; pre-processing (enhancement and segmentation), feature extraction and classification. Through this investigation, we make five main contributions to the field of automatic mammogram analysis. In automated mammogram analysis, image segmentation techniques are employed in breast boundary or region-of-interest (ROI) extraction. In most Medio-Lateral Oblique (MLO) views of mammograms, the pectoral muscle represents a predominant density region and it is important to detect and segment out this muscle region during pre-processing because it could be bias to the detection of breast cancer. An important reason for the breast border extraction is that it will limit the search-zone for abnormalities in the region of the breast without undue influence from the background of the mammogram. Therefore, we propose a new scheme for breast border extraction, artifact removal and removal of annotations, which are found in the background of mammograms. This was achieved using an local adaptive threshold that creates a binary mask for the images, followed by the use of morphological operations. Furthermore, an adaptive algorithm is proposed to detect and remove the pectoral muscle automatically. Feature extraction is another important step of any image-based pattern classification system. The performance of the corresponding classification depends very much on how well the extracted features represent the object of interest. We investigated a range of different texture feature sets such as Local Binary Pattern Histogram (LBPH), Histogram of Oriented Gradients (HOG) descriptor, and Gray Level Co-occurrence Matrix (GLCM). We propose the use of multi-scale features based on wavelet and local binary patterns for mammogram classification. We extract histograms of LBP codes from the original image as well as the wavelet sub-bands. Extracted features are combined into a single feature set. Experimental results show that our proposed method of combining LBPH features obtained from the original image and with LBPH features obtained from the wavelet domain increase the classification accuracy (sensitivity and specificity) when compared with LBPH extracted from the original image. The feature vector size could be large for some types of feature extraction schemes and they may contain redundant features that could have a negative effect on the performance of classification accuracy. Therefore, feature vector size reduction is needed to achieve higher accuracy as well as efficiency (processing and storage). We reduced the size of the features by applying principle component analysis (PCA) on the feature set and only chose a small number of eigen components to represent the features. Experimental results showed enhancement in the mammogram classification accuracy with a small set of features when compared with using original feature vector. Then we investigated and propose the use of the feature and decision fusion in mammogram classification. In feature-level fusion, two or more extracted feature sets of the same mammogram are concatenated into a single larger fused feature vector to represent the mammogram. Whereas in decision-level fusion, the results of individual classifiers based on distinct features extracted from the same mammogram are combined into a single decision. In this case the final decision is made by majority voting among the results of individual classifiers. Finally, we investigated the use of super resolution as a pre-processing step to enhance the mammograms prior to extracting features. From the preliminary experimental results we conclude that using enhanced mammograms have a positive effect on the performance of the system. Overall, our combination of proposals outperforms several existing schemes published in the literature

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models