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Neeraj Sharma

Bio: Neeraj Sharma is an academic researcher from Indian Institute of Technology (BHU) Varanasi. The author has contributed to research in topics: Image segmentation & Materials science. The author has an hindex of 14, co-authored 134 publications receiving 1349 citations. Previous affiliations of Neeraj Sharma include Indian Institutes of Technology & Institute of Medical Sciences, Banaras Hindu University.


Papers
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Journal ArticleDOI
TL;DR: This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
Abstract: Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

702 citations

Journal ArticleDOI
TL;DR: An attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool, which combines second, third, and fourth steps into one algorithm.
Abstract: The objective of developing this software is to achieve auto-segmentation and tissue characterization. Therefore, the present algorithm has been designed and developed for analysis of medical images based on hybridization of syntactic and statistical approaches, using artificial neural network (ANN). This algorithm performs segmentation and classification as is done in human vision system, which recognizes objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. The analysis of medical image is directly based on four steps: 1) image filtering, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features by pattern recognition system or classifier. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semisupervised approach in which supervision is involved only at the level of defining texture-primitive cell; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. The algorithm was first tested on Markov textures, and the success rate achieved in classification was 100%; further, the algorithm was able to give results on the test images impregnated with distorted Markov texture cell. In addition to this, the output also indicated the level of distortion in distorted Markov texture cell as compared to standard Markov texture cell. Finally, algorithm was applied to selected medical images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated.

156 citations

Journal ArticleDOI
TL;DR: In this article, the authors provided the first narrative deep learning review by considering all facets of image classification using AI and employed a PRISMA search strategy using Google Scholar, PubMed, IEEE, and Elsevier Science Direct, through which 127 relevant HDL studies were considered.

50 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used solo deep learning and hybrid deep learning (HDL) models for plaque segmentation in the internal carotid artery (ICA) using B-mode ultrasound (US).

50 citations

Journal ArticleDOI
TL;DR: The proposed modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging performs well and has been found helpful in the better diagnosis of MR images.

42 citations


Cited by
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01 Jan 2006

3,012 citations

Journal ArticleDOI
TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
Abstract: Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

1,599 citations

Book ChapterDOI
01 Jan 2005
TL;DR: Compare your culture to one of the cultures discussed in this unit, and list as many similarities and differences between the two as you can think of.
Abstract: Compare your culture to one of the cultures discussed in this unit. On a sheet of paper, list the cultures you are comparing and make one column titled “similarities,” and a second column titled “differences.” Now, list as many similarities and differences between the two as you can think of. Are there more similarities or differences between the two cultures you selected? Have you ever met anyone from this culture? How can you use this information to build greater respect between cultures?

1,000 citations