You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
12 May 2004Multilevel emphysema diagnosis of HRCT lung images in an incremental framework
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.
The alert did not successfully save. Please try again later.
Mithun N. Prasad, Arcot Sowmya, "Multilevel emphysema diagnosis of HRCT lung images in an incremental framework," Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.533943