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12 May 2004 Multilevel emphysema diagnosis of HRCT lung images in an incremental framework
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Emphysema is a common chronic respiratory disorder characterized by the destruction of lung tissue. Labelling lung images containing Emphysema is a tedious and time consuming process and detection using fewer labelled examples would be very useful. We have recently developed an automated texture-based system capable of achieving varying levels of Emphysema detection in High Resolution Computed Tomography (HRCT) images using co-training [1]. Co-training is a semi-supervised technique used to improve classifiers trained with very few labelled examples using a large pool of unseen examples. In this paper, we show how we can use examples labelled by experts within the same system but in an incremental manner. We show that through the use of two views in our system, one can detect the most informative examples and through the use of labelled examples, one can provide class labels to those examples. When the two views disagree about the class label of an example, we feed the example together with the correct class label provided by the expert’s marking to the system in order to improve its performance. Results show that when images labelled by experts are incorporated into the system at early iterations, the performance of the system compared to the earlier system improves. The results were also compared against "density mask", a standard approach used for Emphysema detection in medical image analysis. In addition, radiologists have verified the results and concluded that the classifiers built at different iterations can be used for different levels of Emphysema diagnosis.
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Mithun N. Prasad and Arcot Sowmya "Multilevel emphysema diagnosis of HRCT lung images in an incremental framework", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004);

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