Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Abnormal Patterns in WCE Images
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
ROC curves and video analysis optimization in intestinal capsule endoscopy
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computers in Biology and Medicine
MaZda-A software package for image texture analysis
Computer Methods and Programs in Biomedicine
Clustering stability-based feature selection for unsupervised texture classification
Machine Graphics & Vision International Journal
Pattern Analysis & Applications
Capsule endoscopy image analysis using texture information from various colour models
Computer Methods and Programs in Biomedicine
MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy
IEEE Transactions on Circuits and Systems for Video Technology
Stereoscopic visualization of laparoscope image using depth information from 3D model
Computer Methods and Programs in Biomedicine
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This paper presents an in-depth study of several approaches to exploratory analysis of wireless capsule endoscopy images (WCE). It is demonstrated that versatile texture and color based descriptors of image regions corresponding to various anomalies of the gastrointestinal tract allows their accurate detection of pathologies in a sequence of WCE frames. Moreover, through classification of single pixels described by texture features of their neighborhood, the images can be segmented into homogeneous areas well matched to the image content. For both, detection and segmentation tasks the same procedure is applied which consists of features calculation, relevant feature subset selection and classification stages. This general three-stage framework is realized using various recognition strategies. In particular, the performance of the developed Vector Supported Convex Hull classification algorithm is compared against Support Vector Machines run in configuration with two different feature selection methods.