Computer Classification of Pneumoconiosis from Radiographs of Coal Workers

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Abstract
The accurate categorization of profusion of opacities in radiographs of coal workers is a significant medical problem. In this study, the feasibility of computer classification of profusion was investigated. Standard pattern recognition techniques were used except for the spatial moments which were computed as measurements of the texture patterns. A normal-abnormal classification was performed on 178 zonal samples and resulted in a training classification rate of 99% and a testing rate of 97%. A four category classification was also performed for the zonal samples with a correct classification rate of 84%. The zonal decisions were used to obtain overall film profusion. The results of this classification compared favorably with readings by radiologists. This study provides positive evidence for a quantitative approach to the classification of profusion. The significance of this study with respect to the understanding and measurement of lung pathology from radiographs is that an alternative or supplement to the presently used visual analysis is demonstrated.

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