451 research outputs found
A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure
The use of an automatic system for the analysis of mammographic images has
proven to be very useful to radiologists in the investigation of breast cancer,
especially in the framework of mammographic-screening programs. A breast
neoplasia is often marked by the presence of microcalcification clusters and
massive lesions in the mammogram: hence the need for tools able to recognize
such lesions at an early stage. In the framework of the GPCALMA (GRID Platform
for Computer Assisted Library for MAmmography) project, the co-working of
italian physicists and radiologists built a large distributed database of
digitized mammographic images (about 5500 images corresponding to 1650
patients) and developed a CAD (Computer Aided Detection) system, able to make
an automatic search of massive lesions and microcalcification clusters. The CAD
is implemented in the GPCALMA integrated station, which can be used also for
digitization, as archive and to perform statistical analyses. Some GPCALMA
integrated stations have already been implemented and are currently on clinical
trial in some italian hospitals. The emerging GRID technology can been used to
connect the GPCALMA integrated stations operating in different medical centers.
The GRID approach will support an effective tele- and co-working between
radiologists, cancer specialists and epidemiology experts by allowing remote
image analysis and interactive online diagnosis.Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th
IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 200
An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network
In this paper we present an efficient computer aided mass classification
method in digitized mammograms using Artificial Neural Network (ANN), which
performs benign-malignant classification on region of interest (ROI) that
contains mass. One of the major mammographic characteristics for mass
classification is texture. ANN exploits this important factor to classify the
mass into benign or malignant. The statistical textural features used in
characterizing the masses are mean, standard deviation, entropy, skewness,
kurtosis and uniformity. The main aim of the method is to increase the
effectiveness and efficiency of the classification process in an objective
manner to reduce the numbers of false-positive of malignancies. Three layers
artificial neural network (ANN) with seven features was proposed for
classifying the marked regions into benign and malignant and 90.91% sensitivity
and 83.87% specificity is achieved that is very much promising compare to the
radiologist's sensitivity 75%.Comment: 13 pages, 10 figure
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Elicitation and representation of expert knowledge for computer aided diagnosis in mammography
To study how professional radiologists describe, interpret and make decisions about micro-calcifications in mammograms. The purpose was to develop a model of the radiologists' decision making for use in CADMIUM II, a computerized aid for mammogram interpretation that combines symbolic reasoning with image processing
IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition
Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions
COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM
Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%.
The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant
An Unsupervised Method for Suspicious Regions Detection in Mammogram Images
Over the past years many researchers proposed biomedical imaging methods for computer-aided detection
and classification of suspicious regions in mammograms. Mammogram interpretation is performed by
radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings
labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method
to automatically detect suspicious regions in mammogram images. The method consists mainly of two
steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background
region from the breast profile region. In greater detail, gray levels mapping transform and histogram
specifications are used to enhance the visual representation of mammogram details. Then, local keypoints
and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are
filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and
confidence interval are very encouraging
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