A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs

Authors:
R. Sundararajan;H. Xu;P. Annangi;X. Tao;XiWen Sun;Ling Mao
Affiliations:
GE Global Research, Bangalore, India, Shanghai, China, Niskayuna;GE Global Research, Bangalore, India, Shanghai, China, Niskayuna;GE Global Research, Bangalore, India, Shanghai, China, Niskayuna;GE Global Research, Bangalore, India, Shanghai, China, Niskayuna;Shanghai Pulmonary Hospital, Shanghai, China;Shanghai Pulmonary Hospital, Shanghai, China
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 6
Cited 0

On the Incorporation of shape priors into geometric active contours

VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Computer-Aided Diagnosis for Pnemoconiosis Using Neural Network

CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
Detection of Clusters of Microcalcifications in Mammograms: A Multi Classifier Approach

CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Active contours without edges

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of detecting the presence of pneumoconiosis in a patient on the basis of evidence found in chest radiographs. Abnormalities pertaining to pneumoconiosis appear in the form of opacities of various sizes; the profusion of these opacities determines the stage of the disease. We present a multiresolution approach whereby we segment regions of interest (ROIs) from the X-Ray image at two levels - lung field and lung zone. We characterize each of these regions using a set of features and build support vector machine (SVM) classifiers that can predict whether or not the region contains any abnormalities. We combine these ROI-Ievel predictions with a second stage SVM in order to get a prediction for the entire chest. Experimental validation shows that this approach provides good results.