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 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 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME)

It is our pleasure to invite you to participate in the 4st IEEE Middle East Conference on Biomedical Engineering (MECBME 2018), which will be hosted by IEEE Medicine and Biology Society (EMBS) Tunisia chapter and the IEEE Tunisia section, in collaboration with the REGIM-Lab. (University of Sfax, Tunisia).The conference will be held at the Ramada Plaza Hotel in Gammarth - Tunis (Tunisia) from March 28-30, 2018.MECBME 2018 is technically co-sponsored by IEEE Region 8 and IEEE Medicine and Biology Society (EMBS)

  • 2016 3rd Middle East Conference on Biomedical Engineering (MECBME)

    The 3rd Middle East Conference on Biomedical Engineering (MECBME’16) is an international forum for engineers, scientists, and researchers to present their state-of-the-art work in biomedical engineering. It also provides engineers with an opportunity to interact and share their experiences in industry and technology applications. The conference will run for two days. Submitted papers will be peer reviewed.

  • 2014 Middle East Conference on Biomedical Engineering (MECBME)

    The scope of the conference includes Biomedical Signal and Image Processing, Bioinstrumentation; Nanomedicine and Biosensors, Biomedical Circuits and Systems, Neuroengineering; Rehabilitation and Therapeutic Systems, Biophotonics, Bioinformatics, Biomechanics, Artificial Organs and Prosthesis, Biomaterials and Tissue Engineering, e-Health, and related areas.

  • 2011 1st Middle East Conference on Biomedical Engineering (MECBME)

    Biomedical Signal and Image Processing Bioinstrumentation; Nanomedicine and Biosensors Biomedical Circuits and Systems Neuroengineering; Rehabilitation and Therapeutic Systems Biomedical Imaging Biophotonics Biomedical System Modeling Bioinformatics Biomechanics; Artificial Organs and Prosthesis Biomaterials and Tissue Engineering Healthcare Information Systems and e-Health Clinical Engineering Nuclear Medicine Bioelectromagnetism Biomedical Education


2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

AQTR 2018 is intended to be an international forum for researchers in the field of automation, quality, testing and robotics. It will bring together equipment manufacturers, software developers and end-users to discuss the current trends and future directions of control and testing technologies and their industrial and social applications in the private and the public sectors. Active participation of students and graduate students is strongly encouraged.


2018 IEEE-NPSS Real Time Conference (RT)

Real time computing applications involving both hardware and software development in nuclear, particle, plasma and other related fields.


2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP)

The conference is a venue for the researchers to present their latest findings and discuss their ideas in the field of Machine Vision and Image Processing.

  • 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)

    all fields of machine vision and image processing and their applications

  • 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)

    MVIP 2013 aims to bring together researchers, scientists, engineers, and scholar students to exchange and share their experiences, ideas, and research results about all aspects of Machine Vision, Image Processing, and discuss the practical challenges encountered and the solutions adopted.MVIP 2013 is a single-track conference consisting high quality previously unpublished contributed papers. We cordially invite Professor/Researcher/Student to submit their research paper to MVIP 2013. Prospective authors are kindly invited to submit full text papers including results, tables, figures and references.

  • 2011 7th Iranian Conference on Machine Vision and Image Processing (MVIP)

    Machine Vision and Image Processing, Soft Computing in Machine Vision and Image Processing, Medical Image Processing, Watermarking, Steganography, Compression, Graphics, Remote Sensing, Pattern Recognition.

  • 2010 6th Iranian Conference on Machine Vision and Image Processing (MVIP)

    Medical Image Processing Industrial Applications of MV & IP Robot Vision MV & IP Algorithms Soft Computing in MV & IP MV & IP Hardware Pattern Recognition and Biometrics Watermarking, Steganography, and Compression 3D and Stereo Vision Computer Graphic and Animation Light, Color, and Theory of Imaging Color Image Processing Motion Pictures Analysis Image acquisition, Databases, and Retrieval Higher Level Processes and Image Analysis Remote Sensing


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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 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.


Geoscience and Remote Sensing, IEEE Transactions on

Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.


Image Processing, IEEE Transactions on

Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.


Information Technology in Biomedicine, IEEE Transactions on

Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.


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Most published Xplore authors for Mammography

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Xplore Articles related to Mammography

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Performance of vibro-acoustography in detecting microcalcifications in excised human breast tissue: a study of 74 tissue samples

A. Alizad; M. Fatemi; L. E. Wold; J. F. Greenleaf IEEE Transactions on Medical Imaging, 2004

X-ray mammography is the principal modality used today for detection of breast microcalcifications and breast lesions associated with breast cancer. X-ray mammography, however, is ionizing and its sensitivity is greatly reduced in dense breasts. Hence, alternative noninvasive and nonionizing breast imaging tools that can aid physicians to better diagnose early-stage breast lesions are of great interest. Vibro-acoustography is a novel ...


Feasibility study for SPECT mammography based on compact imagers rotating around breast vertical axis

R. Pani; A. Soluri; R. Scafe; R. Pellegrini; G. de Vincentis; M. N. Cinti; M. Betti; R. Inches; G. Garibaldi; F. Cusanno; M. Gambaccini; A. Fantini; A. Taibi; A. Olivo; S. Pani; L. Rigon; D. Bollini; N. Lanconelli; A. Del Guerra 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149), 2000

The detection limit of invasive carcinoma by standard prone scintimammography appears to be >=1 cm diameter. Since it is desirable to detect lesions at very earliest stages of growth (0.8 cm or less), the development of prototype scintimammographic systems with improved imaging performances is a primary goal. Here, the authors propose a dedicated high resolution breast imaging scanner for SPECT ...


The role of breast compression in scintimammography

R. Pani; R. Pellegrini; A. Solouri; I. N. Weinberg; G. De Vincentis; R. Scafe; M. N. Cinti; M. Betti; R. Inches; F. Scopinaro; F. Garibuldi; F. Cusanno; A. Del Guerra; I. Khalkhali 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149), 2000

A phantom model was used to study the effect of breast compression on signal- to-noise ratio (SNR) for a dedicated high-resolution gamma camera (Single Photon Emission Mammography, or `SPEM') and a conventional camera as typically employed in prone scintimammography. The phantom was designed to simulate the effects of lesion size and of scatter from nearby torso activity. The phantom studies ...


A wavelet based mammographic system

A. Laine; M. Lewis; F. Taylor Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on, 1994

Mammography's role in the detection of breast cancer at early stages is well known. Although more accurate than other existing techniques, mammography still only finds 80 to 90 percent of breast cancers. It has been suggested that mammograms, as normally viewed, display only about 3% of the total information detected. The general inability to detect small tumors and other salient ...


Modeling optimal characteristics of a-Si:H semiconductor detectors for X-ray detection

K. S. Diaz; A. Leyva; C. Cruz; F. J. Ramirez-Jimenez IEEE Transactions on Nuclear Science, 2005

The promising perspectives and important advantages of the amorphous silicon p-i-n semiconductor detectors for the direct detection of X-rays at room temperature make them suitable for medical applications. In this work, photons in the mammography energy range obtained from a molybdenum target X-ray tube, were transported in an a-Si:H diode using MCNP-4C code based on Monte Carlo method. The geometric ...


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Educational Resources on Mammography

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eLearning

Performance of vibro-acoustography in detecting microcalcifications in excised human breast tissue: a study of 74 tissue samples

A. Alizad; M. Fatemi; L. E. Wold; J. F. Greenleaf IEEE Transactions on Medical Imaging, 2004

X-ray mammography is the principal modality used today for detection of breast microcalcifications and breast lesions associated with breast cancer. X-ray mammography, however, is ionizing and its sensitivity is greatly reduced in dense breasts. Hence, alternative noninvasive and nonionizing breast imaging tools that can aid physicians to better diagnose early-stage breast lesions are of great interest. Vibro-acoustography is a novel ...


Feasibility study for SPECT mammography based on compact imagers rotating around breast vertical axis

R. Pani; A. Soluri; R. Scafe; R. Pellegrini; G. de Vincentis; M. N. Cinti; M. Betti; R. Inches; G. Garibaldi; F. Cusanno; M. Gambaccini; A. Fantini; A. Taibi; A. Olivo; S. Pani; L. Rigon; D. Bollini; N. Lanconelli; A. Del Guerra 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149), 2000

The detection limit of invasive carcinoma by standard prone scintimammography appears to be >=1 cm diameter. Since it is desirable to detect lesions at very earliest stages of growth (0.8 cm or less), the development of prototype scintimammographic systems with improved imaging performances is a primary goal. Here, the authors propose a dedicated high resolution breast imaging scanner for SPECT ...


The role of breast compression in scintimammography

R. Pani; R. Pellegrini; A. Solouri; I. N. Weinberg; G. De Vincentis; R. Scafe; M. N. Cinti; M. Betti; R. Inches; F. Scopinaro; F. Garibuldi; F. Cusanno; A. Del Guerra; I. Khalkhali 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149), 2000

A phantom model was used to study the effect of breast compression on signal- to-noise ratio (SNR) for a dedicated high-resolution gamma camera (Single Photon Emission Mammography, or `SPEM') and a conventional camera as typically employed in prone scintimammography. The phantom was designed to simulate the effects of lesion size and of scatter from nearby torso activity. The phantom studies ...


A wavelet based mammographic system

A. Laine; M. Lewis; F. Taylor Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on, 1994

Mammography's role in the detection of breast cancer at early stages is well known. Although more accurate than other existing techniques, mammography still only finds 80 to 90 percent of breast cancers. It has been suggested that mammograms, as normally viewed, display only about 3% of the total information detected. The general inability to detect small tumors and other salient ...


Modeling optimal characteristics of a-Si:H semiconductor detectors for X-ray detection

K. S. Diaz; A. Leyva; C. Cruz; F. J. Ramirez-Jimenez IEEE Transactions on Nuclear Science, 2005

The promising perspectives and important advantages of the amorphous silicon p-i-n semiconductor detectors for the direct detection of X-rays at room temperature make them suitable for medical applications. In this work, photons in the mammography energy range obtained from a molybdenum target X-ray tube, were transported in an a-Si:H diode using MCNP-4C code based on Monte Carlo method. The geometric ...


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IEEE.tv Videos

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IEEE-USA E-Books

  • 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

  • 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

  • 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

  • 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

  • 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.

  • 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

  • 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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