Statistical and Structural Wavelet Packet Features for Pit Pattern Classification in Zoom-Endoscopic Colon Images
نویسنده
چکیده
We discuss features extracted from a wavelet packet decomposition for image classification. Statistical features computed from wavelet packet coefficients are compared to structural features which are derived from an image dependent wavelet packet decomposition subband structure. Primary application area is the classification of pit pattern structures in zoom-endoscopic colon imagery, while results are also compared to the outcome of a classical texture classification application. Key–Words: image classification, texture classification, wavelet packets, pit pattern, colon zoom-endoscopy
منابع مشابه
One-against-one Classification for Zoom-endoscopy Images
In this paper, we present a novel approach for the classification of zoom-endoscopy images based on the pit-pattern classification scheme. Our feature generation step is based on the computation of a set of statistical features in the wavelet-domain. In the classification step, we employ a one-against-one approach using 1-Nearest Neighbor classifiers together with sequential forward feature sel...
متن کاملSecond-Order Statistical Texture Representation of Asphalt Pavement Distress Images Based on Local Binary Pattern in Spatial and Wavelet Domain
Assessment of pavement distresses is one of the important parts of pavement management systems to adopt the most effective road maintenance strategy. In the last decade, extensive studies have been done to develop automated systems for pavement distress processing based on machine vision techniques. One of the most important structural components of computer vision is the feature extraction met...
متن کاملComputer-assisted pit-pattern classification in different wavelet domains for supporting dignity assessment of colonic polyps
In this paper, we show that zoom-endoscopy images can be well classified according to the pit-pattern classification scheme by using texture-analysis methods in different wavelet domains. We base our approach on three different variants of the wavelet transform and propose that the color-channels of the RGB and LAB color model are an important source for computing image features with high discr...
متن کاملImproving Pit-Pattern Classification of Endoscopy Images by a Combination of Experts
The diagnosis of colorectal cancer is usually supported by a staging system, such as the Duke or TNM system. In this work we discuss computer-aided pit-pattern classification of surface structures observed during high-magnification colonoscopy in order to support dignity assessment of colonic polyps. This is considered a quite promising approach because it allows in vivo staging of colorectal l...
متن کاملPit Pattern Classification in Colonoscopy using Wavelets
Computer assisted analysis of medical imaging data is a very important field of research. One topic of interest in this research area is colon cancer detection. A new classification method, namely the pit pattern classification scheme, developed some years ago delivers very promising results already. Although this method is not yet used in practical medicine, it is a hot topic of research since...
متن کامل