Computer-Aided Diagnosis for Pnemoconiosis Using Neural Network
CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
New Image Processing Models for Opacity Image Analysis in Chest Radiographs
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Am-fm image models
Analyzing Image Structure by Multidimensional Frequency Modulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
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This paper presents a computer-aided diagnostic (CAD) system for analyzing chest radiographs based on the International Labor Organization (ILO) standards. We introduce an amplitude-modulation frequency-modulation (AM-FM) based methodology by which a computer-based system will extract AM-FM features and detect those with suspected interstitial lung diseases. For classification, we use Partial Least Squares (PLS) using a low number of extracted factors (making the system robust). We consider several different AM-FM classifiers based on extracting features from individual scales as well as a final classifier that combines results from the individuals scales. We validate our methodology on 11 standard images graded according to the ILO standard. For several scales, as well as for the combined classifier that uses information from all scales, we get excellent classification results (area under the receiver operator characteristics curve equal to 1.0) using a limited number of latent PLS factors.