Ansatte – Københavns Universitet

Dissimilarity Representations in Lung Parenchyma Classification

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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% ($text{emph{p" border="0" class="imgtopleft"> = 0.046$).
OriginalsprogEngelsk
TitelMedical Imaging 2009 : Computer-Aided Diagnosis
RedaktørerNico Karssemeijer, Maryellen L. Giger
Antal sider12
ForlagSPIE - International Society for Optical Engineering
Publikationsdato2009
DOI
StatusUdgivet - 2009
BegivenhedSPIE Medical Imaging - Lake Buena Vista, USA
Varighed: 7 feb. 200912 feb. 2009

Konference

KonferenceSPIE Medical Imaging
LandUSA
ByLake Buena Vista
Periode07/02/200912/02/2009
NavnProceedings of SPIE
Nummer7260

ID: 8378290