Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A Locally Constrained Watershed Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An evolutionary approach to feature function generation in application to biomedical image patterns
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Neighborhood Exploring Detector: An EM-Based Signal Detector for Multiple Antenna Systems
IEEE Transactions on Signal Processing
Unsupervised segmentation based on robust estimation and color active contour models
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual learning by coevolutionary feature synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-Time Motion Segmentation of Sparse Feature Points at Any Speed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A diagnostic method for protein conformational diseases (PCD) from microscopy images is proposed when such conformational conflicts involve muscular intranuclear inclusions (INIs) indicative of oculopharyngeal muscular dystrophy (OPMD), one variety of PCD. The method combines two techniques: (1) the Histogram Region of Interest Fixed by Thresholds (HRIFT) is designed to capture the color information of INIs for basic feature extraction; (2) an automated feature synthesis, based on the HRIFT features, is designed to identify OPMD by means of Genetic Programming and the Expectation Maximization algorithm (GP-EM) for classification improvement. With variations in size, shape, and background structure, a total of 600 microscopic images are analyzed for the binary classes of healthy and sick conditions of OPMD. The integrated technique of the approach reveals a sensitivity of 0.9 and an area of 0.961 under the receiver operating characteristic (ROC) at a specificity of 0.95. Furthermore, significant improvements in classification accuracy and computational time are demonstrated by comparison with other methods.