3 research outputs found
Discriminative Localized Sparse Representations for Breast Cancer Screening
Breast cancer is the most common cancer among women both in developed and
developing countries. Early detection and diagnosis of breast cancer may reduce
its mortality and improve the quality of life. Computer-aided detection (CADx)
and computer-aided diagnosis (CAD) techniques have shown promise for reducing
the burden of human expert reading and improve the accuracy and reproducibility
of results. Sparse analysis techniques have produced relevant results for
representing and recognizing imaging patterns. In this work we propose a method
for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA).
In this work we apply dictionary learning to our block based sparse analysis
method to classify breast lesions as benign or malignant. The performance of
our method in conjunction with LC-KSVD dictionary learning is evaluated using
10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results
indicate that the proposed sparse analyses may be a useful component for breast
cancer screening applications
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