Wireless capsule endoscopy (WCE) is a new clinical technology permitting the visualization of the small bowel,
the most difficult segment of the digestive tract. The major drawback of this technology is the high amount
of time for video diagnosis. In this study, we propose a method for informative frame detection by isolating
useless frames that are substantially covered by turbid fluids or their contamination with other materials, e.g.,
faecal, semi-processed or unabsorbed foods etc. Such materials and fluids present a wide range of colors, from
brown to yellow, and/or bubble-like texture patterns. The detection scheme, therefore, consists of two stages:
highly contaminated non-bubbled (HCN) frame detection and significantly bubbled (SB) frame detection. Local
color moments in the Ohta color space are used to characterize HCN frames, which are isolated by the Support
Vector Machine (SVM) classifier in Stage-1. The rest of the frames go to the Stage-2, where Laguerre gauss
Circular Harmonic Functions (LG-CHFs) extract the characteristics of the bubble-structures in a multi-resolution
framework. An automatic segmentation method is designed to extract the bubbled regions based on local absolute
energies of the CHF responses, derived from the grayscale version of the original color image. Final detection of
the informative frames is obtained by using threshold operation on the extracted regions. An experiment with
20,558 frames from the three videos shows the excellent average detection accuracy (96.75%) by the proposed
method, when compared with the Gabor based- (74.29%) and discrete wavelet based features (62.21%).
This paper proposes a localized multi-channel filtering approach of image texture analysis based on the cortical behavior of Human Visual System (HVS). In our efforts, 2D Gaussian function, called Cortex Filter, in the frequency domain is used to model the band pass nature of simple cells in HVS. A block-based iterative method is addressed. In each pass, a square block of data is captured and cortex filters at various directions and radial bands are applied to filter out the available texture information in that block. Such decomposition results in a set of band pass images from a single input image and we call it Cortex Transform (CT). We use filter responses in each pass to compute the representative texture features i.e., the average filtered energies. The procedure is repeated for the subsequent blocks of data until the whole image is scanned. Various energy values calculated above are stored into different arrays or files and are regarded as feature images. Thus the obtained feature images are integrated with minimum distance classifier for supervised texture classification. We demonstrated the algorithm with various real world and synthetic images from various sources. Confusion matrix analysis shows a high average overall classification accuracy (97.01%) of our CT based approach in comparison with that (71.27%) of the popular gray level co-occurrence matrix (GLCM) approach.