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Simplified GPU arquitecture. An example of how thread blocks are processed on GPU multiprocessors. A multiprocessor can execute more then one thread block concurrently.
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Wireless capsule endoscopy (WCE) has emerged as a powerful tool in the diagnosis of small intestine diseases. One of the main limiting factor is that it produces a huge number of images, whose analysis, to be done by a doctor, is an extremely time consuming process. Recently, we proposed [8] a computer-aided diagnosis system for blood detection in...
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... in groups of 32 threads (a warp), they execute synchronously and are time-sliced among the stream processors of each multiprocessor. Figure 7 depicts a simplified overview of the GPU architecture. It shows that several multiprocessors contain a large number of stream processors (the number of stream processors and multiprocessors depends on the model and architecture of the GPU). ...
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Citations
... Another transformation form of the RGB color space is the OHTA color space. In [61], the OHTA color space was utilized to extract the features of bleeding from CE images. ...
... Global feature: The entire image information is used in the global feature extraction technique. Using statistical features (such as mean, mode, variance, moment, entropy, energy, skewness, kurtosis, etc.), several articles [1,30,61,71,78,79,94,96,103,109,115,116,122] extracted bleeding features from whole CE images. In [50], statistical color features of bleeding images were extracted from the RGB plane's first-order histogram. ...
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
... Another transformation form of RGB color space is OHTA color space. In [60], OHTA color space was utilized to extract the feature of bleeding from CE images. ...
Capsule endoscopy (CE) has been a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. But the CE captures a huge number of image frames that are time-consuming and tedious tasks for medical experts to diagnose manually. To address this issue, researchers focused on the computer-aided bleeding detection system to identify bleeding automatically in real-time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories: Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect for all original publications on computer-aided bleeding detection published between 2001 and 2021. The PRISMA methodology was used to perform the review, and 112 full-texts of scientific papers were reviewed. The contributions of this paper are I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified, II) the available state-of-the-art computer-aided bleeding detection algorithms including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers are discussed, and III) identify the most effective algorithm for practical use cases. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
In the last two decades, we have seen an amazing development of image processing techniques targeted for medical applications. We propose multi-GPU-based parallel real-time algorithms for segmentation and shape-based object detection, aiming at accelerating two medical image processing methods: automated blood detection in wireless capsule endoscopy (WCE) images and automated bright lesion detection in retinal fundus images. In the former method we identified segmentation and object detection as being responsible for consuming most of the global processing time. While in the latter, as segmentation was not used, shape-based object detection was the compute-intensive task identified. Experimental results show that the accelerated method running on multi-GPU systems for blood detection in WCE images is on average 265 times faster than the original CPU version and is able to process 344 frames per second. By applying the multi-GPU framework for bright lesion detection in fundus images we are able to process 62 frames per second with a speedup average 667 times faster than the equivalent CPU version.
Computer-aided detection and diagnosis of diabetic retinopathy with retinal fundus images is the necessary step for the implementation of a large scale screening effort in regions where ophthalmologists are not available. In this paper we propose computer-aided binary detector of bright lesions in retinal fundus images. It is based on wavelets for multiresolution feature discrimination and support vector machine (SVM) for classification. After thresholding the sub-band images resulting from the Isotropic Undecimated Wavelet Transform (IUWT) decomposition of the input image, we employ an approach based on the image Hessian eigenvalues and multi-scale image analysis, for designing good feature descriptors of bright lesions. These are afterwards used in the SVM model classifier.
Experimental results on our current data set show that the proposed method is efficient and achieves a
very good success rate.
