A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An edge-based description of color images
CVGIP: Graphical Models and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Saliency of Watershed Contours and Hierarchical Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Color Image Filtering Based on Center-Weighted Vector Directional Filters
Multidimensional Systems and Signal Processing
Selection weighted vector directional filters
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Fast detection and impulsive noise removal in color images
Real-Time Imaging - Special issue on multi-dimensional image processing
Color Image Processing
Supervised parametric and non-parametric classification of chromosome images
Pattern Recognition
Directional processing of color images: theory and experimental results
IEEE Transactions on Image Processing
Regularization of optic flow estimates by means of weighted vector median filtering
IEEE Transactions on Image Processing
Partition-based vector filtering technique for suppression of noise in digital color images
IEEE Transactions on Image Processing
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Multichannel chromosome image acquisition is used for cancer diagnosis and research on genetic disorders. This type of imaging, apart from aiding the cytogeneticist in several ways, facilitates the visual detection of chromosome abnormalities. However, chromosome misclassification errors result from different factors, such as uneven hybridization, spectral overlap among fluors, and biochemical noise. In this paper, we enhance the chromosome classification accuracy by making use of a region Bayes classifier that increases the classification accuracy when compared to the already developed pixel-by-pixel classifier and by incorporating the vector median filtering approach for filtering of the image. The method is evaluated using a publicly available database that contains 183 six-channel chromosome sets of images. The overall improvement on the chromosome classification accuracy is 9.99%, compared to the pixel-by-pixel classifier without filtering. This improvement in the chromosome classification accuracy would allow subtle deoxyribonucleic acid abnormalities to be identified easily. The efficiency of the method might further improve by using features extracted from each region and a more sophisticated classifier.