The nature of statistical learning theory
The nature of statistical learning theory
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Classification of hematologic malignancies using texton signatures
Pattern Analysis & Applications
Pattern Analysis & Applications
Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis
IEEE Transactions on Information Technology in Biomedicine
Mining knowledge for HEp-2 cell image classification
Artificial Intelligence in Medicine
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Objective: Systemic lupus erythematosus is a connective tissue disease affecting multiple organ systems and characterised by a chronic inflammatory process. It is considered a very serious sickness, further to be classified as an invalidating chronic disease. The recommended method for its detection is the indirect immunofluorescence (IIF) based on Crithidia Luciliae (CL) substrate. Hoverer, IIF is affected by several issues limiting tests reliability and reproducibility. Hence, an evident medical demand is the development of computer-aided diagnosis tools that can offer a support to physician decision. Methods: In this paper we propose a system that classifies CL wells integrating information extracted from different images. It is based on three main decision phases. Two steps, named as threshold-based classification and single cells recognition, are applied for image classification. They minimise false negative and false positive classifications, respectively. Feature extraction and selection have been carried out to determine a compact set of descriptors to distinguish between positive and negative cells. The third step applies majority voting rule at well recognition level, enabling us to recover possible errors provided by previous phases. Results: The system performance have been evaluated on an annotated database of IIF CL wells, composed of 63 wells for a total of 342 images and 1487 cells. Accuracy, sensitivity and specificity of image recognition step are 99.4%, 98.6% and 99.6%, respectively. At level of well recognition, accuracy, sensitivity and specificity are 98.4%, 93.3% and 100.0%, respectively. The system has been also validated in a daily routine fashion on 48 consecutive analyses of hospital outpatients and inpatients. The results show very good performance for well recognition (100% of accuracy, sensitivity and specificity), due to the integration of cells and images information. Conclusions: The described recognition system can be applied in daily routine in order to improve the reliability, standardisation and reproducibility of CL readings in IIF.