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

The convergence of a one-step smoothing Newton method for P0-NCP based on a new smoothing NCP-function

AbstractThe nonlinear complementarity problem (denoted by NCP(F)) can be reformulated as the solution of a nonsmooth system of equations. By introducing a new smoothing NCP-function, the problem is approximated by a family of parameterized smooth equations. A one-step smoothing Newton method is proposed for solving the nonlinear complementarity problem with P0-function (P0-NCP) based on the new smoothing NCP-function. The proposed algorithm solves only one linear system of equations and performs only one line search per iteration. Without requiring strict complementarity assumption at the P0-NCP solution, the proposed algorithm is proved to be convergent globally and superlinearly under suitable assumptions. Furthermore, the algorithm has local quadratic convergence under mild conditions

Cost-Efficient Data Backup for Data Center Networks against {\epsilon}-Time Early Warning Disaster

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with $\varepsilon$ early warning time. Given geo-distributed DCNs and such a $\varepsilon$-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the $\varepsilon$ early warning time constraint, which is significant because it is an emergency data protection scheme against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup

Molecular Systematics of Eastern Cottonwood Using AFLP and RAPD Markers.

Eastern cottonwood (Populus deltoides Bartr.) is the fastest growing tree in southeastern United States with great potential as a biomass source. DNA-based molecular marker techniques are playing increasingly important roles in elucidating genetic diversity within species. Amplified fragment length polymorphism (AFLP) and random amplified polymorphic DNA (RAPD) markers were employed to study genetic relationships among 57 clones from subregion Lower Mississippi river, West Central, and West Gulf. A total of 101 polymorphic RAPD markers were amplified from 14 primers. Six AFLP primer pairs resulted in a total of 457 polymorphic markers. Both RAPD and AFLP markers were able to uniquely identify all clones, indicating that extensive genetic diversity existed among the clones and demonstrating their efficiency as fingerprinting tools. To understand population structure in eastern cottonwood, leaf samples from 202 trees involving 12 natural populations from subregion East Central, East Gulf, and South Atlantic along the species\u27 geographic regions were collected. All identified polymorphic markers, including 492 AFLP markers and 104 RAPD markers were included in the analysis. The within-population genetic diversity was estimated to be 0.2543 from AFLP data and 0.2619 from RAPD data, suggesting there is significant genetic variation within populations. The coefficient of gene differentiation among populations (FST) was estimated to be 0.0663 and 0.0536 for AFLP and RAPID respectively (P \u3c 0.001), suggesting population subdivision in eastern cottonwood. The correlation between AFLP and RAPID data matrices based on Nei\u27s standard genetic distance as measured by Pearson product moment correlation was 0.4251 (P = 0.027). Phylogenetic trees were constructed by UPGMA and Neighbor joining method. From AFLP data, populations from East Gulf were always grouped together in both trees and this was further supported by bootstrap test of significance of the trees. The UPGMA tree from RAPD suggested populations from East Central and East Gulf are close to populations within the same subregion, whereas the Neighbor-joining tree supported populations from East Central are grouped together. In addition, the variances associated with the population parameters from AFLP analysis were significant lower than that from RAPD analysis, suggesting AFLP analysis is a more reliable tool than RAPD analysis for population study