ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Using human perceptual categories for content-based retrieval from a medical image database
Computer Vision and Image Understanding
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With the increasing ability of imaging technologies to provide true volumetric image data, there is an opportunity to fill the need to advance the field of computer-aided texture classification using 3D feature information. We present a method allowing for characterising lung regions of interstitial lung diseases. We compared the inter-observer variation between experts and computer classification results; and analysed the expert's labelling error and computer classification error. The 3D adaptive multiple feature method was in agreement with an expert in 92%, whereas agreement between two experts was 67%. There was no significant classification difference for different selections of VOI sizes for 15 × 15, 21 × 21, and 31 × 31. We demonstrated that 3D texture features can successfully differentiate parenchymal pathologies associated with both emphysema and interstitial lung diseases as well as mixed patterns. The system may assist primary readers to quantify extent of lung disease, and as such could assist in monitoring of treatment effects.