Automatic abnormal region detection in lung CT images for visual retrieval
Henning Müller, Samuel Marquis, Gilles Cohen, Pierre‐Alexandre Poletti, Christian Lovis, Antoine Geissbühler · Swiss Medical Informatics · 2005
Image management, analysis, and retrieval are currently very active research fields mainly because of the large amount of visual data being produced in modern hospitals, and the lack of applications dealing with these data. Most often, the goal is to aid the diagnostic process. Unfortunately, only very few medical image retrieval systems are currently used in clinical routine. One application domain with a high potential for automatic image retrieval is the analysis and retrieval of lung CTs. A first user study in the United States (Purdue University) shows that these systems allow improving the diagnostic quality significantly. This article describes an approach to an aid for lung CT diagnostics. The analysis incorporates several steps and the goal is to automate the process as much as possible for easy integration into clinical processes. Thus, several automatic steps are proposed from a selection of the most characteristic slices, to an automatic segmentation of the lung tissue and a classification on the segmented area into visual observation classes. Feedback to the MD is given in the form of marked regions in the images that appear to be different from the norm of healthy tissue. We currently work on a small set of training images with marked and annotated regions but a larger set of images for the evaluation of our algorithm is in work. The article currently only contains a short quantitative evaluation. For most tasks we use existing open source software such as Weka, GIFT, and itk. This allows an easy reproduction of the search results and limits the need for costly redevelopments.