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Automatic honeycombing detection based on watershed transform

Honeycombing is a common diffuse lung symptom in High-Resolution computed Tomography (HRCT), indicating the fibrosis of the lung. The purpose of this study was to develop an automatic scheme to detect honeycombing pattern accurately. The scans of 30 patients with diffuse lung disease were enrolled in the study. The lung region identified by threshold and morphological operations was pre-segmented by watershed transform to be divided into proper regions of interest (ROIs). Then texture features selected by recursive feature elimination algorithm were calculated within each ROI. Support vector machine (SVM) is used to generate rules with the training examples provided by experienced radiologists and knowledge-guided strategy was applied to reduce false positive regions. The proposed system achieved an accuracy of 92.8%, a sensitivity of 87.6% and a specification of 93.9%. The strategy is sufficiently accurate for objective and quantitative analysis of honeycombing in lung CT images.