Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A method for linking computed image features to histological semantics in neuropathology
Journal of Biomedical Informatics
Cytological image analysis with a genetic fuzzy finite state machine
Computer Methods and Programs in Biomedicine
Learning histopathological microscopy
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
A fuzzy logic based-method for prognostic decision making in breast and prostate cancers
IEEE Transactions on Information Technology in Biomedicine
On the Classification of Prostate Carcinoma With Methods from Spatial Statistics
IEEE Transactions on Information Technology in Biomedicine
On the selection and classification of independent features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
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Providing an improved technique which can assist pathologists in correctly classifying meningioma tumours with a significant accuracy is our main objective. The proposed technique, which is based on optimum texture measure combination, inspects the separability of the RGB colour channels and selects the channel which best segments the cell nuclei of the histopathological images. The morphological gradient was applied to extract the region of interest for each subtype and for elimination of possible noise (e.g. cracks) which might occur during biopsy preparation. Meningioma texture features are extracted by four different texture measures (two model-based and two statistical-based) and then corresponding features are fused together in different combinations after excluding highly correlated features, and a Bayesian classifier was used for meningioma subtype discrimination. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations in terms of quantitatively characterising the meningioma tissue, achieving an overall classification accuracy of 92.50%, improving from 83.75% which is the best accuracy achieved if the texture measures are used individually.