Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Color image fidelity metrics evaluated using image distortion maps
Signal Processing - Special issue on image and video quality metrics
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties
IEEE Transactions on Computers
Artificial Intelligence in Medicine
WeAidU-a decision support system for myocardial perfusion images using artificial neural networks
Artificial Intelligence in Medicine
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
A study of cloud classification with neural networks using spectral and textural features
IEEE Transactions on Neural Networks
Multiple feature sets based categorization of laryngeal images
Computer Methods and Programs in Biomedicine
Increasing the discrimination power of the co-occurrence matrix-based features
Pattern Recognition
Predictor output sensitivity and feature similarity-based feature selection
Fuzzy Sets and Systems
Automated speech analysis applied to laryngeal disease categorization
Computer Methods and Programs in Biomedicine
Using the patient's questionnaire data to screen laryngeal disorders
Computers in Biology and Medicine
Voice Pathology Classification by Using Features from High-Speed Videos
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders
Artificial Intelligence in Medicine
Classification of functional voice disorders based on phonovibrograms
Artificial Intelligence in Medicine
Machine Vision and Applications
Categorizing laryngeal images for decision support
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Selecting features from multiple feature sets for SVM committee-based screening of human larynx
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Automatic classification of lymphoma images with transform-based global features
IEEE Transactions on Information Technology in Biomedicine
An image feature approach for computer-aided detection of ischemic stroke
Computers in Biology and Medicine
Questionnaire- versus voice-based screening for laryngeal disorders
Expert Systems with Applications: An International Journal
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Objective: The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords. Methodology: The problem is treated as a pattern recognition task. To obtain a concise and informative representation of a vocal cord image, colour, texture, and geometrical features are used. The representation is further analyzed by a pattern classifier categorizing the image into healthy, diffuse, and nodular classes. Results: The approach developed was tested on 785 vocal cord images collected at the Department of Otolaryngology, Kaunas University of Medicine, Lithuania. A correct classification rate of over 87% was obtained when categorizing a set of unseen images into the aforementioned three classes. Conclusion: Bearing in mind the high similarity of the decision classes, the results obtained are rather encouraging and the developed tools could be very helpful for assuring objective analysis of the images of laryngeal diseases.