Dissimilarity representations in lung parenchyma classification

Lauge Sørensen; Marleen de Bruijne

doi:10.1117/12.812505

27 February 2009 Dissimilarity representations in lung parenchyma classification

Lauge Sørensen, Marleen de Bruijne

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72602Z (2009) https://doi.org/10.1117/12.812505
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of $92.9%$, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% ($text{emph{p" border="0" class="imgtopleft"> = 0.046$).

Citation Download Citation

Lauge Sørensen and Marleen de Bruijne "Dissimilarity representations in lung parenchyma classification", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602Z (27 February 2009); https://doi.org/10.1117/12.812505

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available