Journal ArticleDOI
A comprehensive review of denoising techniques for abdominal CT images
TLDR
This study aims to compare the capabilities of several notable and contemporary denoising techniques in the presence of different types of noise present in abdominal CT images to determine the most suitableDenoising technique for practitioners and researchers that can be used in real life scenarios.Abstract:
Computed Tomography (CT) is one of the effective imaging modality in medical sciences that assist in diagnosing various pathologies inside the human body. Despite considerable advancement in acquisition speed, signal to noise ratio and image resolution of computed tomography imaging technology, CT images are still affected by noise and artifacts. A tradeoff between the amount of noise reduced and conservation of genuine image details has to be made in such a way that it enhances the clinically relevant image content. Therefore, noise reduction in medical images is an important and challenging task, as it helps to improve the performance of other image processing procedures such as segmentation or classification to perform better diagnosis by clinicians. Different techniques have been suggested in the literature on denoising of CT images, and each technique has its own presumptions, benefits, and drawbacks. To the best of our knowledge, no survey paper was found in the literature that compares the performance of various denoising techniques for CT images. This study aims to compare the capabilities of several notable and contemporary denoising techniques in the presence of different types of noise present in abdominal CT images. This comparative analysis helps to determine the most suitable denoising technique for practitioners and researchers that can be used in real life scenarios. Furthermore, the advantages and disadvantages of considered denoising methods have also been discussed along with some recommendations for further research in this area.read more
Citations
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Journal ArticleDOI
Automated detection of Glaucoma using deep learning convolution network (G-net)
Mamta Juneja,Shaswat Singh,Naman Agarwal,Shivank Bali,Shubham Gupta,Niharika Thakur,Prashant Jindal +6 more
TL;DR: An Artificially Intelligent glaucoma expert system based on segmentation of optic disc and optic cup based on Convolution neural networks working in conjunction to segment optic cup and disc is presented.
Book ChapterDOI
Machine Learning: A Review of the Algorithms and Its Applications
TL;DR: The algorithms of machine learning, its principles and highlighting the advantages and disadvantages in this field are introduced and the advancements that have been carried out are focused on so that the current researchers can be benefitted out of it.
Journal ArticleDOI
Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation
TL;DR: A new recurrent generative adversarial architecture named RNN-GAN is proposed to mitigate imbalance data problem in medical image semantic segmentation where the number of pixels belongs to the desired object are significantly lower than those belonging to the background.
Journal ArticleDOI
A review on Deep Learning approaches for low-dose Computed Tomography restoration
TL;DR: In this paper, the role of DL techniques in low-dose CT (LDCT) restoration is discussed and the applications of DL-based approaches for LDCT restoration are reviewed.
Journal ArticleDOI
A hybrid edge-based technique for segmentation of renal lesions in CT images
TL;DR: A hybrid segmentation technique based on two methods which include spatial intuitionistic fuzzy c-means clustering (SIFCM) that integrates spatial image details and, distance regularized level-sets method for extraction of renal lesions correctly and proficiently in computed tomography (CT) images is suggested.
References
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Journal ArticleDOI
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
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