Mammography

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Mammography is the process of using low-energy-X-rays (usually around 30 kVp) to examine the human breast and is used as a diagnostic and a screening tool. (Wikipedia.org)






Conferences related to Mammography

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2018 10th International Conference on Computational Intelligence and Communication Networks (CICN)

10th International Conference on Computational Intelligence and Communication Networks (CICN 2017) is organized to address various issues to prosper the creation of intelligent solutions in future. The aim is to bring together worldwide leading researchers, developers, practitioners and educators interested in advancing the state of the art in computational intelligence and communication Networks for exchanging knowledge that encompasses a broad range of disciplines among various distinct communities. It is expected that researchers will bring new prospect for collaboration across disciplines and gain idea facilitating novel breakthrough. The theme for this conference is Innovating and Inspiring the researchers to adopt the outcome for implementation.

  • 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)

    Soft computing, Fuzzy Logic and ANN • Sensors and networks (wireless ad hoc N/W, Vehicular N/W) • Acoustic and under water electronics/communication and sonar systems • Image, Signal and Speach Processing • Microwave IC, antennas and Wave Propagation • Modeling and simulation• Digital Design, VLSI and SOC • Data Mining, Big data, Ontology and Web Services • Parallel and distributed systems• Telecommunication and Mobile communication • Grid, Cloud, High Speed/Performance and Green Computing • RFIDs and applicatons• Embedded systems and Hardware Design/Implementation • Mobile Computing, Computational Intelligence • Power Electronics, Transmission and Power Systems • Computer Vision and Artificial Intelligence • Bio-informatics, Biometry and Medical Imaging• Information security,Network Security and Steganography • Remote sensing and GIS

  • 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)

    Soft computing, Fuzzy Logic and ANN ? Sensors and networks (wireless ad hoc N/W, Vehicular N/W) ? Acoustic and under water electronics/communication and sonar systems ? Signal and Speach Processing ? Microwave IC, antennas and Wave Propagation ? Modeling and simulation ? VLSI Design and SOC ? Data Mining, Ontology and Web Services ? Parallel and distributed systems ? Telecommunication and Mobile communication ? Grid, Cloud, High Speed/Performance and Green Computing ? RFIDs and applicatons ? Embedded systems and Hardware Design/Implementation ? Mobile Computing, Computational Intelligence

  • 2015 International Conference on Computational Intelligence and Communication Networks (CICN)

    Soft computing, Fuzzy Logic and ANN • Sensors and networks (wireless ad hoc N/W, Vehicular N/W) • Acoustic and under water electronics/communication and sonar systems • Signal and Speach Processing • Microwave IC, antennas and Wave Propagation • Modeling and simulation• VLSI Design and SOC • Data Mining, Ontology and Web Services • Parallel and distributed systems• Telecommunication and Mobile communication • Grid, Cloud, High Speed/Performance and Green Computing • RFIDs and applicatons• Embedded systems and Hardware Design/Implementation • Mobile Computing, Computational Intelligence

  • 2014 International Conference on Computational Intelligence and Communication Networks (CICN)

    The International Conference on Computational Intelligence and Communication Networks (CICN 2014) is organized to address various issues to prosper the creation of intelligent solutions in future. The aim is to bring together worldwide leading researchers, developers, practitioners and educators interested in advancing the state of the art in computational intelligence and communication Networks for exchanging knowledge that encompasses a broad range of disciplines among various distinct communities. It is expected that researchers will bring new prospect for collaboration across disciplines and gain idea facilitating novel breakthrough. The theme for this conference is Innovating and Inspiring the researchers to adopt the outcome for implementation. The conference will provide an exceptional platform to the researchers to meet and discuss the utmost solutions, scientific results and methods in solving intriguing problems with people that actively involved in these evergreen fields. T

  • 2013 5th International Conference on Computational Intelligence and Communication Networks (CICN)

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  • 2012 4th International Conference on Computational Intelligence and Communication Networks (CICN)

    The lnternational Conference CICN 2012 aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary to brainstorm for fruitful results and applications.

  • 2011 International Conference on Computational Intelligence and Communication Networks (CICN)

    The International Conference on Computational Intelligence and Communication Networks (CICN2011) is organized to address various issues to prosper the creation of intelligent solutions in future. The aim is to bring together worldwide leading researchers, developers, practitioners and educators interested in advancing the state of the art in computational intelligence and communication Networks for exchanging knowledge that encompasses a broad range of disciplines among various distinct communities.

  • 2010 International Conference on Computational Intelligence and Communication Networks (CICN)

    The International Conference on Computational Intelligence and Communication Networks (CICN2010) is organized to address various issues to prosper the creation of intelligent solutions in future. The aim is to bring together worldwide leading researchers, developers, practitioners and educators interested in advancing the state of the art in computational intelligence and communication Networks for exchanging knowledge that encompasses a broad range of disciplines among various distinct communities.


2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

This conference provides an idea-exchange and discussion platform for researchers andpractitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    This conference provides an idea-exchange and discussion platform for researchers andpractitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    This conference provides an idea-exchange and discussion platform for researchers and practitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    This conference provides an idea-exchange and discussion platform for researchers andpractitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    This conference provides an idea-exchange and discussion platform for researchers and practitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    This conference provides an idea-exchange and discussion platform for researchers and practitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    This conference provides an idea-exchange and discussion platform for researchers and practitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2011 International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    This conference provides an idea-exchange and discussion platform for researchers and practitioners interested in Intelligent Human-Machine and Cybernetics and other topics.

  • 2010 2nd International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

    The conference subjects include intelligent human machine systems, control theory and systems theory. Interdisciplinary research will be given a special emphasis. Sharing research and experiences and openly discussing problems will be strongly encouraged in all scientific sessions.


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


2018 25th IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP 2018, the 25th in the series that has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world.


2018 26th Signal Processing and Communications Applications Conference (SIU)

The general scope of the conference ranges from signal and image processing to telecommunication, and applications of signal processing methods in biomedical and communication problems.

  • 2017 25th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing and Communication Applications (SIU) conference is the most prominent scientific meeting on signal processing in Turkey bringing together researchers working in signal processing and communication fields. Topics include but are not limited to the areas of research listed in the keywords.

  • 2016 24th Signal Processing and Communication Application Conference (SIU)

    Signal Processing Theory, Statistical Signal Processing, Nonlinear Signal Processing, Adaptive Signal Processing, Array and Multichannel Signal Processing, Signal Processing for Sensor Networks, Time-Frequency Analysis, Speech / Voice Processing and Recognition, Computer Vision, Pattern Recognition, Machine Learning for Signal Processing, Human-Machine Interaction, Brain-Computer Interaction, Signal-Image Acquisition and Generation, image Processing, video Processing, Image Printing and Presentation, Image / Video / Audio browsing and retrieval, Image / Video / Audio Watermarking, Multimedia Signal Processing, Biomedical Signal Processing and Image Processing, Bioinformatics, Biometric Signal-Image Processing and Recognition, Signal Processing for Security and Defense, Signal and Image Processing for Remote Sensing, Signal Processing Hardware, Signal Processing Education, Radar Signal Processing, Communication Theory, Communication Networks, Wireless Communications

  • 2015 23th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing Theory Statistical Signal Processing Nonlinear Signal Processing Adaptive Signal Processing Array and Multichannel Signal Processing Signal Processing for Sensor Networks Time-Frequency Analysis Speech / Voice Processing and Recognition Computer Vision Pattern Recognition Machine Learning for Signal Processing Human-Machine Interaction Brain-Computer Interaction Signal-Image Acquisition and Generation image Processing video Processing Image Printing and Presentation Image / Video / Audio browsing and retrieval Image / Video / Audio Watermarking Multimedia Signal Processing Biomedical Signal Processing and Image Processing Bioinformatics Biometric Signal-Image Processing and Recognition Signal Processing for Security and Defense Signal and Image Processing for Remote Sensing Signal Processing Hardware Signal Processing Education Radar Signal Processing Communication Theory Communication Networks Wireless Communications

  • 2014 22nd Signal Processing and Communications Applications Conference (SIU)

    SIU will be held in Trabzon, Turkey at the Karadeniz Technical University Convention and Exhibition Centre on April 23, 2014. SIU is the largest and most comprehensive technical conference focused on signal processing and its applications in Turkey. Last year there were 500 hundred participants. The conference will feature renowned speakers, tutorials, and thematic workshops. Topics include but are not limited to: Signal Procesing, Image Processing, Communication, Computer Vision, Machine Learning, Biomedical Signal Processing,

  • 2013 21st Signal Processing and Communications Applications Conference (SIU)

    Conference will discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. Conference will also include invited lectures, tutorials and special sessions.

  • 2012 20th Signal Processing and Communications Applications Conference (SIU)

    Conference will discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. Conference will also include invited lectures, tutorials and special sessions.

  • 2011 19th Signal Processing and Communications Applications Conference (SIU)

    Conference will bring together academia and industry professionals as well as students and researchers to present and discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. The Conference will also include invited lectures, tutorials and special sessions.

  • 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU)

    S1.Theory of Signal-Processing S2.Statistical Signal-Processing S3.Multimedia Signal-Processing S4.Biomedical Signal-Processing S5.Sensor Networks S6.Multirate Signal-Processing S7.Pattern Recognition S8.Computer Vision S9.Adaptive Filters S10.Image/Video/Speech Browsing, Retrieval S11.Speech/Audio Coding S12.Speech Processing S13.Human-Machine Interfaces S14.Surveillance Signal Processing S15.Bioinformatics S16.Self-Learning S17.Signal-Processing Education S18.Signal-Processing Systems S1

  • 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU)

    The scope of the conference is to cover recent topics in theory and applications of Signal Processing and Communications.

  • 2008 IEEE 16th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing, Image Processing, Speech Processing, Pattern Recognition, Human Computer Interaction, Communication, Video and Speech indexing, Computer Vision, Biomedical Signal Processing

  • 2007 IEEE 15th Signal Processing and Communications Applications (SIU)

  • 2006 IEEE 14th Signal Processing and Communications Applications (SIU)

  • 2005 IEEE 13th Signal Processing and Communications Applications (SIU)

  • 2004 IEEE 12th Signal Processing and Communications Applications (SIU)


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Periodicals related to Mammography

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Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Computational Intelligence Magazine, IEEE

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.


Control Systems Technology, IEEE Transactions on

Serves as a compendium for papers on the technological advances in control engineering and as an archival publication which will bridge the gap between theory and practice. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the technology needed to implement control systems from analysis and design through simulation and hardware.


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Most published Xplore authors for Mammography

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Xplore Articles related to Mammography

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Preprocessing filters for mammogram images: A review

[{u'author_order': 1, u'affiliation': u'ECE Department, Vimal Jyothi Engineering College, Kannur, Kerala, India', u'full_name': u'Kshema'}, {u'author_order': 2, u'affiliation': u'ECE Department, Vimal Jyothi Engineering College, Kannur, Kerala, India', u'full_name': u'M. Jayesh George'}, {u'author_order': 3, u'affiliation': u'ECE Department, Vimal Jyothi Engineering College, Kannur, Kerala, India', u'full_name': u'D. Anto Sahaya Dhas'}] 2017 Conference on Emerging Devices and Smart Systems (ICEDSS), None

Breast cancer is a standout amongst the most widely recognized kind of growth among ladies that develops from breast tissue. Still the exact cause of the breast cancer remain unknown. Early detection and diagnosis is the best and most effective strategy to control the tumor progression. Mammography is the currently recommended imaging method for early determination and diagnosis of breast ...


Exploring Wavelets Subband Decomposition Toward a Computer Aided Detection of Microcalcification in Breast Cancer

[{u'author_order': 1, u'full_name': u'Nizar Ben Hamad'}, {u'author_order': 2, u'affiliation': u'SETIT, ISECS Sfax, TUNISIA, +216 74 674 364, khaled.taouil@isimsf.rnu.tn; Research Unit: Sciences and Technologies of Image and Telecommunications, Higher Institute of Biotechnology, Sfax-TUNISIA', u'full_name': u'Khaled Taouil'}] The 2nd International Conference on Distributed Frameworks for Multimedia Applications, None

2-D wavelet transform decomposition is widely used in computer aided detection of microcalcifications in mammograms. The aim of this work is to investigate the better type of wavelet and its optimal potential level of decomposition that gives us better detection. Our algorithm consists of four steps: First, dimension reduction is performed on the mammography images to delimitate the ROI (region ...


Measurement of Breast Density on Digital Mammograms

[{u'author_order': 1, u'affiliation': u'Faculty of Health Sciences, Okayama University Medical School, 5-1 Shikata-cho, 2-chome, Okayama, 700-8558, JAPAN. Phone: +81-86-235-6907, Fax: +81-86-222-3717, Email: goto@md.okayama-u.ac.jp', u'full_name': u'Sachiko Goto'}, {u'author_order': 2, u'affiliation': u'Faculty of Health Sciences, Okayama University Medical School, 5-1 Shikata-cho, 2-chome, Okayama, 700-8558, JAPAN. Phone: +81-86-235-6907, Fax: +81-86-222-3717', u'full_name': u'Yoshiharu Azuma'}, {u'author_order': 3, u'affiliation': u'Faculty of Health Sciences, Okayama University Medical School, 5-1 Shikata-cho, 2-chome, Okayama, 700-8558, JAPAN. Phone: +81-86-235-6907, Fax: +81-86-222-3717', u'full_name': u'Tetsuhiro Sumimoto'}, {u'author_order': 4, u'affiliation': u'Faculty of Health Sciences, Okayama University Medical School, 5-1 Shikata-cho, 2-chome, Okayama, 700-8558, JAPAN. Phone: +81-86-235-6907, Fax: +81-86-222-3717', u'full_name': u'Yoshihiro Takeda'}, {u'author_order': 5, u'affiliation': u'Graduate School of Health Sciences, Okayama University', u'full_name': u'Naoko Tsujita'}, {u'author_order': 6, u'affiliation': u'Central Division of Radiology, Okayama University Hospital', u'full_name': u'Shigefumi Kadohisa'}] 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings, None

We built a estimating system of individual breast density from digital mammograms by using breast tissue equivalent phantoms that are able to change the mixture ratio of adipose and glandular tissue and the thickness. The method was compared with a visual assessment of breast density by the radiologists as a gold standard. The clinical image data set that consisted of ...


Efficient restoration and enhancement of super-resolved X-ray images

[{u'author_order': 1, u'affiliation': u'Ricoh Innovations, Menlo Park, CA, USA', u'full_name': u'M. Dirk Robinson'}, {u'author_order': 2, u'affiliation': u'Dept. of Ophthalmology, Duke University Medical Center, Durham, NC, USA', u'full_name': u'Sina Farsiu'}, {u'author_order': 3, u'affiliation': u'Dept. of Radiology and Biomed. Engr., Duke University Medical Center, Durham, NC, USA', u'full_name': u'Joseph Y. Lo'}, {u'author_order': 4, u'affiliation': u'Dept. of Ophthalmology and Biomed. Engr., Duke University Medical Center, Durham, NC, USA', u'full_name': u'Cynthia A. Toth'}] 2008 15th IEEE International Conference on Image Processing, None

Our previous work demonstrates the ability to reconstruct a single higher resolution image from fusing a collection of multiple extremely low-dosage aliased X-ray images. While this computationally efficient method eliminates aliasing artifacts associated with undersampling, it does not address the problem of deblurring the reconstructed image. In this paper, we present a fast nonlinear deblurring algorithm, specifically designed to address ...


A New Mammogram Preprocessing Method for Computer-Aided Diagnosis Systems

[{u'author_order': 1, u'affiliation': u'Eng. Res. Lab., Hassan II Univ., Casablanca, Morocco', u'full_name': u'Ilhame Ait Lbachir'}, {u'author_order': 2, u'affiliation': u'Eng. Res. Lab., Hassan II Univ., Casablanca, Morocco', u'full_name': u'Rachida Es-Salhi'}, {u'author_order': 3, u'affiliation': u'Eng. Res. Lab., Hassan II Univ., Casablanca, Morocco', u'full_name': u'Imane Daoudi'}, {u'author_order': 4, u'affiliation': u'Eng. Res. Lab., Hassan II Univ., Casablanca, Morocco', u'full_name': u'Saadia Tallal'}] 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), None

Mammography is currently the most powerful technique for early detection of breast cancer. To better interpret mammogram images and assist radiologists in their decision, CAD systems have been proposed. This paper gives a comparative analysis of the existing preprocessing methods and proposes a technique for preprocessing mammography that will be implemented afterwards in a CAD system. The proposed preprocessing technique ...


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Educational Resources on Mammography

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IEEE.tv Videos

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IEEE-USA E-Books

  • Learning a New View of a Database: With an Application in Mammography

    This chapter contains sections titled: Introduction, View Learning for Mammography, Naive View Learning Framework, Initial Experiments, Intergrated View Learning Framework, Further Experiments and Results, Related Work, Conclusions and Future Work, Acknowledgments, References

  • Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer

    The identification and interpretation of the signs of breast cancer in mammographic images from screening programs can be very difficult due to the subtle and diversified appearance of breast disease. This book presents new image processing and pattern recognition techniques for computer-aided detection and diagnosis of breast cancer in its various forms. The main goals are: (1) the identification of bilateral asymmetry as an early sign of breast disease which is not detectable by other existing approaches; and (2) the detection and classification of masses and regions of architectural distortion, as benign lesions or malignant tumors, in a unified framework that does not require accurate extraction of the contours of the lesions. The innovative aspects of the work include the design and validation of landmarking algorithms, automatic Tabar masking procedures, and various feature descriptors for quantification of similarity and for contour independent classification of mammograp ic lesions. Characterization of breast tissue patterns is achieved by means of multidirectional Gabor filters. For the classification tasks, pattern recognition strategies, including Fisher linear discriminant analysis, Bayesian classifiers, support vector machines, and neural networks are applied using automatic selection of features and cross-validation techniques. Computer-aided detection of bilateral asymmetry resulted in accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively. Computer-aided diagnosis of automatically detected lesions provided sensitivity of detection of malignant tumors in the range of [0.70, 0.81] at a range of falsely detected tumors of [0.82, 3.47] per image. The techniques presented in this work are effective in detecting and characterizing various mammographic signs of breast disease.

  • Content-based Retrieval of Medical Images:Landmarking, Indexing, and Relevance Feedback

    Content-based image retrieval (CBIR) is the process of retrieval of images from a database that are similar to a query image, using measures derived from the images themselves, rather than relying on accompanying text or annotation. To achieve CBIR, the contents of the images need to be characterized by quantitative features; the features of the query image are compared with the features of each image in the database and images having high similarity with respect to the query image are retrieved and displayed. CBIR of medical images is a useful tool and could provide radiologists with assistance in the form of a display of relevant past cases. One of the challenging aspects of CBIR is to extract features from the images to represent their visual, diagnostic, or application-specific information content. In this book, methods are presented for preprocessing, segmentation, landmarking, feature extraction, and indexing of mammograms for CBIR. The preprocessing steps include anisotropic di fusion and the Wiener filter to remove noise and perform image enhancement. Techniques are described for segmentation of the breast and fibroglandular disk, including maximum entropy, a moment-preserving method, and Otsu's method. Image processing techniques are described for automatic detection of the nipple and the edge of the pectoral muscle via analysis in the Radon domain. By using the nipple and the pectoral muscle as landmarks, mammograms are divided into their internal, external, upper, and lower parts for further analysis. Methods are presented for feature extraction using texture analysis, shape analysis, granulometric analysis, moments, and statistical measures. The CBIR system presented provides options for retrieval using the Kohonen self- organizing map and the k-nearest-neighbor method. Methods are described for inclusion of expert knowledge to reduce the semantic gap in CBIR, including the query point movement method for relevance feedback (RFb). Analysis of performanc is described in terms of precision, recall, and relevance-weighted precision of retrieval. Results of application to a clinical database of mammograms are presented, including the input of expert radiologists into the CBIR and RFb processes. Models are presented for integration of CBIR and computer-aided diagnosis (CAD) with a picture archival and communication system (PACS) for efficient workflow in a hospital. Table of Contents: Introduction to Content-based Image Retrieval / Mammography and CAD of Breast Cancer / Segmentation and Landmarking of Mammograms / Feature Extraction and Indexing of Mammograms / Content-based Retrieval of Mammograms / Integration of CBIR and CAD into Radiological Workflow

  • Fractal Analysis of Breast Masses in Mammograms

    Fractal analysis is useful in digital image processing for the characterization of shape roughness and gray-scale texture or complexity. Breast masses present shape and gray-scale characteristics in mammograms that vary between benign masses and malignant tumors. This book demonstrates the use of fractal analysis to classify breast masses as benign masses or malignant tumors based on the irregularity exhibited in their contours and the gray-scale variability exhibited in their mammographic images. A few different approaches are described to estimate the fractal dimension (FD) of the contour of a mass, including the ruler method, box-counting method, and the power spectral analysis (PSA) method. Procedures are also described for the estimation of the FD of the gray-scale image of a mass using the blanket method and the PSA method. To facilitate comparative analysis of FD as a feature for pattern classification of breast masses, several other shape features and texture measures are desc ibed in the book. The shape features described include compactness, spiculation index, fractional concavity, and Fourier factor. The texture measures described are statistical measures derived from the gray-level cooccurrence matrix of the given image. Texture measures reveal properties about the spatial distribution of the gray levels in the given image; therefore, the performance of texture measures may be dependent on the resolution of the image. For this reason, an analysis of the effect of spatial resolution or pixel size on texture measures in the classification of breast masses is presented in the book. The results demonstrated in the book indicate that fractal analysis is more suitable for characterization of the shape than the gray-level variations of breast masses, with area under the receiver operating characteristics of up to 0.93 with a dataset of 111 mammographic images of masses. The methods and results presented in the book are useful for computer-aided diagnosis of br ast cancer. Table of Contents: Computer-Aided Diagnosis of Breast Cancer / Detection and Analysis ofnewline Breast Masses / Datasets of Images of Breast Masses / Methods for Fractal Analysis / Pattern Classification / Results of Classification of Breast Masses / Concluding Remarks

  • Computer-aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer

    Architectural distortion is an important and early sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. Screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. This book presents image processing and pattern recognition techniques to detect architectural distortion in prior mammograms of interval-cancer cases. The methods are based upon Gabor filters, phase portrait analysis, procedures for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase-portrait analysis, 4,224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammo rams of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' texture energy measures, and Haralick's 14 texture features were computed. The areas under the receiver operating characteristic (ROC) curves obtained using the features selected by stepwise logistic regression and the leave-one-image-out method are 0.77 with the Bayesian classifier, 0.76 with Fisher linear discriminant analysis, and 0.79 with a neural network classifier. Free-response ROC analysis indicated sensitivities of 0.80 and 0.90 at 5.7 and 8.8 false positives (FPs) per image, respectively, with the Bayesian classifier and the leave-one-image-out method. The present study has demonstrated the ability to detect early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on the average, for interval-cancer cases, with a sensitivity of 0.8 at 5.7 FP/image. The presented computer-aided detection techniques, dedicated to accurate detection and lo alization of architectural distortion, could lead to efficient detection of early and subtle signs of breast cancer at pre-mass-formation stages. Table of Contents: Introduction / Detection of Early Signs of Breast Cancer / Detection and Analysis of Oriented Patterns / Detection of Potential Sites of Architectural Distortion / Experimental Set Up and Datasets / Feature Selection and Pattern Classification / Analysis of Oriented Patterns Related to Architectural Distortion / Detection of Architectural Distortion in Prior Mammograms / Concluding Remarks

  • Analysis of Oriented Texture:With application to the Detection of Architectural Distortion in Mammograms

    The presence of oriented features in images often conveys important information about the scene or the objects contained; the analysis of oriented patterns is an important task in the general framework of image understanding. As in many other applications of computer vision, the general framework for the understanding of oriented features in images can be divided into low- and high-level analysis. In the context of the study of oriented features, low- level analysis includes the detection of oriented features in images; a measure of the local magnitude and orientation of oriented features over the entire region of analysis in the image is called the orientation field. High- level analysis relates to the discovery of patterns in the orientation field, usually by associating the structure perceived in the orientation field with a geometrical model. This book presents an analysis of several important methods for the detection of oriented features in images, and a discussion of the phase po trait method for high-level analysis of orientation fields. In order to illustrate the concepts developed throughout the book, an application is presented of the phase portrait method to computer-aided detection of architectural distortion in mammograms. Table of Contents: Detection of Oriented Features in Images / Analysis of Oriented Patterns Using Phase Portraits / Optimization Techniques / Detection of Sites of Architectural Distortion in Mammograms

  • Medical Imaging Modalities: XRay Imaging

    This chapter contains sections titled: * X-Ray Imaging * X-Ray Generation * X-Ray 2-D Projection Imaging * X-Ray Mammography * X-Ray CT * Spiral X-Ray CT * Contrast Agent, Spatial Resolution, and SNR * Exercises * References



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