Neural networks for pattern recognition
Neural networks for pattern recognition
From image analysis to computer vision: an annotated bibliography, 1955-1979
Computer Vision and Image Understanding
Histogram ratio features for color texture classification
Pattern Recognition Letters
Suppression of sampling moire in color printing by spline-based least-squares prefiltering
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Image Processing - Principles and Applications
Image Processing - Principles and Applications
Finding and identifying unknown commercials using repeated video sequence detection
Computer Vision and Image Understanding
Classification of hematologic malignancies using texton signatures
Pattern Analysis & Applications
WND-CHARM: Multi-purpose image classification using compound image transforms
Pattern Recognition Letters
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric identification using knee X-rays
International Journal of Biometrics
Journal of Signal Processing Systems
Computer Methods and Programs in Biomedicine
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
IEEE Transactions on Information Technology in Biomedicine
Spatial Modeling and Classification of Corneal Shape
IEEE Transactions on Information Technology in Biomedicine
Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Image database analysis of Hodgkin lymphoma
Computational Biology and Chemistry
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We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content. A unique two-stage approach was employed in this study. At the outer level, raw pixels were transformed with a set of transforms into spectral planes. Simple (Fourier, Chebyshev, and wavelets) and compound transforms (Chebyshev of Fourier and wavelets of Fourier) were computed. Raw pixels and spectral planes were then routed to the second stage (the inner level). At the inner level, the set of multipurpose global features was computed on each spectral plane by the same feature bank. All computed features were fused into a single feature vector. The specimens were stained with hematoxylin (H) and eosin (E) stains. Several color spaces were used: RGB, gray, CIE-L*a*b*, and also the specific stain-attributed H&E space, and experiments on image classification were carried out for these sets. The best signal (98%-99% on earlier unseen images) was found for the HE, H, and E channels of the H&E data set.