Dr. Khayrul Bashar Profile

Dr. Khayrul Bashar

at Nagoya Univ

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Proceedings Article | 11 March 2008 Paper

Informative frame detection from wireless capsule video endoscopic images

Md. Khayrul Bashar, Kensaku Mori, Yasuhito Suenaga, Takayuki Kitasaka, Yoshito Mekada

Proceedings Volume 6914, 69142A (2008) https://doi.org/10.1117/12.772418

KEYWORDS: Image segmentation, Video, Endoscopy, Wavelets, Image filtering, Visualization, Tissues, Feature extraction, Liquids, Feature selection

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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%).

Proceedings Article | 11 February 2002 Paper

Cortex transform and its application for supervised texture classification of digital images

M. Bashar, Noboru Ohnishi, R. Shevgaonkar

Proceedings Volume 4567, (2002) https://doi.org/10.1117/12.455251

KEYWORDS: Image filtering, Image classification, Gaussian filters, Optical filters, Image segmentation, Computed tomography, Visualization, Visual system, Visual cortex, Independent component analysis

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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.

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