The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Comparative Study of Texture Features for the Discrimination of Gastric Polyps in Endoscopic Video
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Pattern Analysis & Applications
Improving Pit---Pattern Classification of Endoscopy Images by a Combination of Experts
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Endomicroscopic image retrieval and classification using invariant visual features
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Computer-aided classification of zoom-endoscopical images using Fourier filters
IEEE Transactions on Information Technology in Biomedicine
A system for colorectal tumor classification in magnifying endoscopic NBI images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Introducing space and time in local feature-based endomicroscopic image retrieval
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
Hi-index | 0.00 |
In this work we propose a method to extract shape-based features from endoscopic images for an automated classification of colonic polyps. This method is based on the density of pits as used in the pit pattern classification scheme which is commonly used for the classification of colonic polyps. For the detection of pits we employ a noise-robust variant of the LBP operator. To be able to be robust against local texture variations we extend this operator by an adaptive thresholding. Based on the detected pit candidates we compute a Delaunay triangulation and use the edge lengths of the resulting triangles to construct histograms. These are then used in conjunction with the k-NN classifier to classify images. We show that, compared to a previously developed method, we are not only able to almost always get higher classification results in our application scenario, but that the proposed method is also able to significantly outperform the previously developed method in terms of the computational demand.