Knowledge-Driven Automated Detection of Pleural Plaques and Thickening in High Resolution CT of the Lung

• Published in: Information Processing in Medical Imaging Vol. 19; pp. 270 - 285
• Main Authors: Rudrapatna, Mamatha, Mai, Van, Sowmya, Arcot, Wilson, Peter
• Format: Book Chapter Journal Article
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005
• Series: Lecture Notes in Computer Science
• Subjects: Algorithms; Artificial Intelligence; Decision Tree Learner; High Resolution Computer Tomography; Humans; Information Storage and Retrieval - methods; Intercostal Muscle; Lung Diseases - diagnostic imaging; Malignant Pleural Mesothelioma; Pattern Recognition, Automated - methods; Pleural Diseases - diagnostic imaging; Pleural Plaque; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity; Tomography, X-Ray Computed - methods
• ISBN: 9783540265450; 3540265457
• ISSN: 0302-9743; 1011-2499; 1611-3349
• DOI: 10.1007/11505730_23

Consistent efforts are being made to build Computer-Aided Detection and Diagnosis systems for radiological images. Such systems depend on automated detection of various disease patterns, which are then combined together to obtain differential diagnosis. For diffuse lung diseases, over 12 disease patterns are of interest in High Resolution Computed Tomography (HRCT) scans of the lung. In this paper, we present an automated detection method for two such patterns, namely Pleural Plaque and Diffuse Pleural Thickening. These are characteristic features of asbestos-related benign pleural disease. The attributes used for detection are derived from anatomical knowledge and the heuristics normally used by radiologists, and are computed automatically for each scan. A probabilistic model built on the attributes using naïve Bayes classifier is applied to recognise the features in new scans, and preliminary results are presented. The technique is tested on 140 images from 13 studies and validated by an experienced radiologist.