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Wireless Capsule Endoscopy (WCE) is a technology in the field of endoscopic imaging which facilitates direct visualization of the entire small intestine. Many algorithms are being developed to automatically identify clinically important frames in WCE videos. This paper presents a supervised method for automated detection of bleeding regions present in WCE(More)
This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation is taken as the L 1-regularization functional and solved(More)
Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS(More)
The paper presents an area- and power-efficient implementation of an image compressor for wireless capsule endoscopy application. The architecture uses a direct mapping to compute the two-dimensional discrete cosine transform which eliminates the need of transpose operation and results in reduced area and low processing time. The algorithm has been modified(More)
The state-of-the-art capsule endoscopy (CE) technology offers painless examination for the patients and the ability to examine the interior of the gastrointestinal tract by a noninvasive procedure for the gastroenterologists. In this work, a modular and flexible CE development system platform consisting of a miniature field programmable gate array (FPGA)(More)
In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover(More)
This paper presents a novel technique for detecting bleeding regions in capsule endoscopy images. The proposed algorithm extracts color features from image-regions by calculating mean, standard deviation, skew and energy from the first order histogram of the RGB planes separately. Through the use of RGB color space, three times more number of features can(More)