Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Contrast limited adaptive histogram equalization
Graphics gems IV
Floating search methods in feature selection
Pattern Recognition Letters
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Texture Classification Using Wavelet Decomposition with Markov Random Field Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
Computer-aided classification of zoom-endoscopical images using Fourier filters
IEEE Transactions on Information Technology in Biomedicine
Automated Marsh-like classification of celiac disease in children using local texture operators
Computers in Biology and Medicine
Classification of endoscopic images using delaunay triangulation-based edge features
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed to the wavelet domain using the pyramidal discrete wavelet transform. Then, Gaussian Markov random fields are used to extract features from the resulting wavelet coefficients. Finally, these features are used for a classification using the k-NN classifier and the Bayes classifier. To enhance the classification results feature subset selection is used to reduce the dimensionality of the features. Apart from that, directional neighborhoods for the Markov random fields are introduced. These are exploiting the orientation of the details within the wavelet detail subbands with the goal of further improving the classification performance. The experimental results show that an automated classification using the presented method is feasible.