Dissimilarity Representations in Lung Parenchyma Classification

Lauge Emil Borch Laurs Sørensen, Marleen de Bruijne

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

5 Citations (Scopus)

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% ($text{emph{p" border="0" class="imgtopleft"> = 0.046$).
Original languageEnglish
Title of host publicationMedical Imaging 2009 : Computer-Aided Diagnosis
EditorsNico Karssemeijer, Maryellen L. Giger
Number of pages12
PublisherSPIE - International Society for Optical Engineering
Publication date2009
DOIs
Publication statusPublished - 2009
EventSPIE Medical Imaging - Lake Buena Vista, United States
Duration: 7 Feb 200912 Feb 2009

Conference

ConferenceSPIE Medical Imaging
Country/TerritoryUnited States
CityLake Buena Vista
Period07/02/200912/02/2009
SeriesProceedings of SPIE
Number7260

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