Analysis and Classification of Crithidia Luciliae Fluorescent Images
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Data & Knowledge Engineering
Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis
IEEE Transactions on Information Technology in Biomedicine
A decision support system for Crithidia Luciliae image classification
Artificial Intelligence in Medicine
Mitotic HEp-2 cells recognition under class skew
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of antinuclear autoantibodies (ANA). IIF diagnosis requires both the estimation of the fluorescent intensity and the description of the staining pattern, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer-aided diagnosis (CAD) tools that can offer a support to physician decision. In this paper we first propose a strategy to reliably label the image data set by using the diagnoses performed by different physicians, and then we present a system to classify the fluorescent intensity. Such a system adopts a multiple expert system architecture (MES), based on the classifier selection paradigm. Two different selection rules are presented and, given the application domain, the convenience of using one of them is analyzed. Different sets of operating points are determined, making the recognition system suited to application in daily practice and in a wide spectrum of scenarios. The measured performance on an annotated database of IIF images shows a low overall miss rate (