2,577 research outputs found
Breast Cancer: Modelling and Detection
This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection
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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
Mammographic image restoration using maximum entropy deconvolution
An image restoration approach based on a Bayesian maximum entropy method
(MEM) has been applied to a radiological image deconvolution problem, that of
reduction of geometric blurring in magnification mammography. The aim of the
work is to demonstrate an improvement in image spatial resolution in realistic
noisy radiological images with no associated penalty in terms of reduction in
the signal-to-noise ratio perceived by the observer. Images of the TORMAM
mammographic image quality phantom were recorded using the standard
magnification settings of 1.8 magnification/fine focus and also at 1.8
magnification/broad focus and 3.0 magnification/fine focus; the latter two
arrangements would normally give rise to unacceptable geometric blurring.
Measured point-spread functions were used in conjunction with the MEM image
processing to de-blur these images. The results are presented as comparative
images of phantom test features and as observer scores for the raw and
processed images. Visualization of high resolution features and the total image
scores for the test phantom were improved by the application of the MEM
processing. It is argued that this successful demonstration of image
de-blurring in noisy radiological images offers the possibility of weakening
the link between focal spot size and geometric blurring in radiology, thus
opening up new approaches to system optimization.Comment: 18 pages, 10 figure
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Mammography remains the most prevalent imaging tool for early breast cancer
screening. The language used to describe abnormalities in mammographic reports
is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a
correct BI-RADS category to each examined mammogram is a strenuous and
challenging task for even experts. This paper proposes a new and effective
computer-aided diagnosis (CAD) system to classify mammographic masses into four
assessment categories in BI-RADS. The mass regions are first enhanced by means
of histogram equalization and then semiautomatically segmented based on the
region growing technique. A total of 130 handcrafted BI-RADS features are then
extrcated from the shape, margin, and density of each mass, together with the
mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a
modified feature selection method based on the genetic algorithm (GA) is
proposed to select the most clinically significant BI-RADS features. Finally, a
back-propagation neural network (BPN) is employed for classification, and its
accuracy is used as the fitness in GA. A set of 500 mammogram images from the
digital database of screening mammography (DDSM) is used for evaluation. Our
system achieves classification accuracy, positive predictive value, negative
predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%,
and 79.3%, respectively. To our best knowledge, this is the best current result
for BI-RADS classification of breast masses in mammography, which makes the
proposed system promising to support radiologists for deciding proper patient
management based on the automatically assigned BI-RADS categories
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