Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis

Authors:
Liyang Wei;Yongyi Yang;Robert M. Nishikawa
Affiliations:
Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3301 South Dearborn Street, Chicago, IL 60616, USA;Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3301 South Dearborn Street, Chicago, IL 60616, USA;Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
Venue:
Pattern Recognition
Year:
2009

Citing 6
Cited 10

Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Transductive Inference for Text Classification using Support Vector Machines

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Recognition, Fourth Edition

Pattern Recognition, Fourth Edition

Detection of Microcalcifications Using Coordinate Logic Filters and Artificial Neural Networks

IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Combination of nonlinear filters and ANN for detection of microcalcifications in digitized mammography

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Effective recognition of MCCs in mammograms using an improved neural classifier

Engineering Applications of Artificial Intelligence
Microcalcifications detection using PFCM and ANN

MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

Neurocomputing
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging

Knowledge-Based Systems
Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

Journal of Medical Systems
Regularization in retrieval-driven classification of clustered microcalcifications for breast cancer

Journal of Biomedical Imaging - Special issue on Advances in Computer-Aided Detection and Diagnosis
Rotation invariant fuzzy shape contexts based on eigenshapes and fourier transforms for efficient radiological image retrieval

ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
An Improved Decision Support System for Detection of Lesions in Mammograms Using Differential Evolution Optimized Wavelet Neural Network

Journal of Medical Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms was modeled after expert observers. In this work, we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adaptively incorporating local proximity information into a classifier, it can help to improve its classification accuracy, thereby leading to an improved ''second opinion'' to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.