Multiresolution Feature Extraction and Selection for Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Object recognition via local patch labelling
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Computer-aided detection of pulmonary pathology in pediatric chest radiographs
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Computer-Assisted Diagnosis of Tuberculosis: A First Order Statistical Approach to Chest Radiograph
Journal of Medical Systems
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In this paper classification on dissimilarity representations is applied to medical imaging data with the task of discrimination between normal images and images with signs of disease. We show that dissimilarity-based classification is a beneficial approach in dealing with weakly labeled data, i.e. when the location of disease in an image is unknown and therefore local feature-based classifiers cannot be trained. A modification to the standard dissimilarity-based approach is proposed that makes a dissimilarity measure multi-valued, hence, able to retain more information. A multi-valued dissimilarity between an image and a prototype becomes an image representation vector in classification. Several classification outputs with respect to different prototypes are further integrated into a final image decision. Both standard and proposed methods are evaluated on data sets of chest radiographs with textural abnormalities and compared to several feature-based region classification approaches applied to the same data. On a tuberculosis data set the multi-valued dissimilarity-based classification performs as well as the best region classification method applied to the fully labeled data, with an area under the receiver operating characteristic (ROC) curve (A"z) of 0.82. The standard dissimilarity-based classification yields A"z=0.80. On a data set with interstitial abnormalities both dissimilarity-based approaches achieve A"z=0.98 which is closely behind the best region classification method.