DocumentCode :
2393589
Title :
A novel opposition-based classifier for mass diagnosis in mammography images
Author :
Saki, Fatemeh ; Tahmasbi, Amir ; Shokouhi, Shahriar B.
Author_Institution :
Dept. of Electr. Eng., Iran Univ. of Sci. & Technol. (IUST), Tehran, Iran
fYear :
2010
fDate :
3-4 Nov. 2010
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, a novel opposition-based classifier has been developed which classifies breast masses into benign and malignant categories. An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has been utilized as the classifier. The objective is increasing the convergence rate of MLP learning rules as well as improving the mass diagnostic performance. The input ROI, which is a suspected part of mammogram, is segmented manually by expert radiologists and subjected to some preprocessing stages such as histogram equalization, translation and scaling. Then, a group of features which are appropriate descriptors of mass shape, margin and density have been extracted from the preprocessed ROIs. The proposed features include circularity, Zernike moments, contrast, average gray level, NRL derivatives and SP. The proposed classifier has been trained with both traditional BP and OWBP learning rules and the performance have been evaluated. The system which utilizes OWPB learning rule yields a significantly faster training time than BP algorithm while the Az of the resulting CADx system is 0.944.
Keywords :
diagnostic radiography; image classification; learning (artificial intelligence); mammography; medical image processing; multilayer perceptrons; MLP learning rule convergence; MLP network; NRL derivatives; OWBP classifier; Zernike moments; average gray level; benign breast mass; breast mass classification; breast mass density descriptor; breast mass margin descriptor; breast mass shape descriptor; circularity; contrast; malignant breast mass; mammography images; mass diagnosis; mass diagnostic performance; opposite weighted back propagation; opposition based classifier; Cancer; Computational modeling; Shape; Silicon; Computer aided diagnosis; mammography; multi layer Perceptron; opposition based learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering (ICBME), 2010 17th Iranian Conference of
Conference_Location :
Isfahan
Print_ISBN :
978-1-4244-7483-7
Type :
conf
DOI :
10.1109/ICBME.2010.5704940
Filename :
5704940
Link To Document :
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