Browsing by Author "Gupta, P.K."

Now showing 1 - 9 of 9

A robust framework for de-speckling of optical coherence tomography images
(Science and Engineering Research Support Society ijbsbt@sersc.org PO Box 5014Sandy Bay TAS 7005 Tasmania, 2020) Gupta, P.K.; Lal, S.; Husain, F.
Recently, Optical Coherence Tomography (OCT) is emerging as a important diagnostic tool in medical application. OCT is widely used in detection of vision-related diseases. The analysis of retinal OCT images are very difficult due to speckle noise. The characteristic nature of this noise is multiplicative. A Number of de-speckling methods was proposed in the last few decades. All the existing de-speckling methods reduce the speckle noise, but these methods are not able to preserve the structure of the OCT image during de-speckling process. This paper propose a robust framework using wiener filter, soft thresholding and a weighted guided filter along with wavelet decomposition for the purpose of speckle noise reduction. The main contribution of this research is to remove speckle noise and preserve the structure of OCT image during de-noising process. In the first step of proposed framework, the original image is filtered by the wiener filter. After that a logarithmic transformation is used for the conversion of multiplicative noise into additive noise. Discrete wavelet transform (DWT) decompose the image into its constituents. Soft thresholding and weighted guided filter is used for high frequency sub-image and low frequency sub-image part respectively. In the last inverse DWT and antilog transformation are applied to acquire the de-noised image. Two different experiments are performed one on real OCT Images and other on natural images, to demonstrate the usefulness of the proposed framework. © 2020 SERSC.
Artificial Bee Colony Optimization Based Despeckling Framework for Ultrasound Images
(Eastern Macedonia and Thrace Institute of Technology, 2020) Gupta, P.K.; Lal, S.; Husain, F.
This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images. © 2020 All Rights Reserved
Basic alloying elements used in high-entropy alloys
(De Gruyter, 2023) Chandrakar, R.; Sridhar, K.; Sahu, P.S.; Chandraker, S.; Gupta, P.K.
The mechanical characteristics of high-entropy alloys (HEAs) can be improved by a variety of alloying elements; however, it is unclear how the alloying of various elements affects the changes in the microstructure and the mechanical properties of HEAs. The alloying elements like Cr, V, Ti, Zr, and Hf regulate the melting temperature, lattice constant, and the mass density of HEAs. The electrical structure and the mechanical characteristics of HEAs are significantly impacted by the valence electron concentration. High VEC can enhance mechanical characteristics while decreasing its ductility. Ti significantly affects ductility, while Cr-alloying significantly affects the mechanical characteristics of HEAs. Our findings offer the fundamental understanding required to direct the development of HEAs with superior mechanical characteristics. © 2023 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.
Hummingbird: Leveraging Heterogeneous System Architecture for deploying dynamic NFV chains
(Institute of Electrical and Electronics Engineers Inc., 2022) Chaurasia, A.K.; Raman, B.; Gupta, P.K.; Prabhu, O.; Shashank, P.; Garg, A.
Network Function Virtualization has gained traction as a network function deployment alternative due to its flexibility and cost benefits. The telecommunication (telecom) operators and infrastructure providers are looking for high throughput, low latency NFV deployment model to avail the benefits of NFV. Moreover, NFV is one of the core technology for the next-generation communication network such as 5G. Furthermore, telecom operators employ groups of network functions(NFs) that process packets in linear order so that the output of one NF becomes an input for another, thus forming the network function chain (NFC). However, these NFCs should be flexible, as all telecom packets do not necessarily need to be processed by the same set of NFs. It has been earlier shown that GPU increases the throughput of NFV chains. To the best of our knowledge, none of the GPU-based frameworks supports dynamic NFV chains. Furthermore, discrete GPUs are expensive and consume a fair amount of energy. This paper presents the design and evaluation of Hummingbird, a framework to support high throughput, dynamically routed NFV chain on Heterogeneous System Architecture (HSA). Though HSAs are affordable and power-efficient, they lack high throughput GPU-CPU synchronization. Furthermore, current technology does not provide a zero-copy mechanism for network IO between GPU and NIC for HSAs. Hummingbird addressed those challenges. As per our knowledge, this is the first such framework that provides high throughput dynamic NFV chaining, with NFs chained across GPU and CPU and designed in conformance to OpenCL 2.0 standard. Hummingbird achieves 6x throughput per-core and 3.5x throughput per unit of energy consumption compared to state-of-the-art NFV deployment framework G-net, which uses powerful and costly discrete GPU. Â© 2022 IEEE.
Non-subsampled Shearlet Domain-based De-speckling Framework for Optical Coherence Tomography Images
(International Hellenic University - School of Science, 2023) Gupta, P.K.; Chanchal, A.K.; Lal, S.; Gupta, V.
An effective instrument for obtaining an image of the retina is an optical coherence tomography (OCT) imaging device. OCT images of the retina are useful for diagnosing and tracking eye diseases. However, different physical configurations in the imaging apparatus are to blame for the speckle noise in retinal OCT images. The OCT image quality and assessment reliability are reduced due to aforementioned noise. This paper offered a paradigm for reducing speckle noise that was motivated by the mathematical formulation of speckle noise. Two distinct noise components make up speckle noise, one of which is additive and the other of which is multiplicative in nature. For each sort of noise, the suggested structure employs a different filter. To reduce the additive component of speckle noise, Weiner filtering is used. To minimize the multiplicative component of noise, a particular arrangement based on non-subsampled shearlet transform (NSST) is used. It is now widely acknowledge that NSST overcome the limitations of traditional wavelet transform therefore it very useful in dealing of distributed discontinuities therefore it is prefer in this research work.Real retinal OCT pictures are used to assess the proposed framework's quantitative and qualitative performance. The PSNR, MSE, SSIM, and CNR metrics are used to compare the suggested framework. In comparison to existing cutting-edge filters, the proposed framework performs better in terms of noise suppression capability with structure preservation capabilities. The proposed technique gives highest PSNR, SSIM and CNR value that indicate the effectiveness of proposed work in addition to this proposed work give lowest MSE value. The proposed work give better enhance images in comparison to other existing filter therefore it may be helpful to find out any abnormality in OCT image and improve the diagnose of OCT retinal image. © 2023 School of Science, IHU. All rights reserved.
Performance analysis of despeckling filters for retinal optical coherence tomography images
(2018) Gupta, P.K.; Lal, S.; Husain, F.
This paper presents performance analysis of different despeckling filters used for denoising of the optical coherence tomography (OCT) Images. Currently OCT imaging is one of the best technique used in biomedical application to detect the abnormality in the human eye. OCT images normally suffer from granular patterns called speckle noise. Speckle noise is an inherent property of an OCT images which affects the visual quality of the images, hence difficult to diagnosis the patients. Therefore, speckle noise reduction from the OCT images is an important prerequisite, whenever OCT imaging is used for diagnosis. Here, a comparative analysis of different despeckling filters used for the denoising of OCT images is presented. The speckle noise intensity is depends on the various imaging system parameters and on the different structure representations used for the image tissues. A denoising technique is to be designed in such a way that it should be able to reduce the speckle noise from the OCT images while preserve the tissues and fine details of the images. � 2018 IEEE.
Performance analysis of despeckling filters for retinal optical coherence tomography images
(Institute of Electrical and Electronics Engineers Inc., 2018) Gupta, P.K.; Lal, S.; Husain, F.
This paper presents performance analysis of different despeckling filters used for denoising of the optical coherence tomography (OCT) Images. Currently OCT imaging is one of the best technique used in biomedical application to detect the abnormality in the human eye. OCT images normally suffer from granular patterns called speckle noise. Speckle noise is an inherent property of an OCT images which affects the visual quality of the images, hence difficult to diagnosis the patients. Therefore, speckle noise reduction from the OCT images is an important prerequisite, whenever OCT imaging is used for diagnosis. Here, a comparative analysis of different despeckling filters used for the denoising of OCT images is presented. The speckle noise intensity is depends on the various imaging system parameters and on the different structure representations used for the image tissues. A denoising technique is to be designed in such a way that it should be able to reduce the speckle noise from the OCT images while preserve the tissues and fine details of the images. Â© 2018 IEEE.
Towards a Federated Learning Approach for NLP Applications
(Springer Science and Business Media Deutschland GmbH, 2021) Prabhu, O.S.; Gupta, P.K.; Shashank, P.; Chandrasekaran, K.; Divakarla, D.
Traditional machine learning involves the collection of training data to a centralized location. This collected data is prone to misuse and data breach. Federated learning is a promising solution for reducing the possibility of misusing sensitive user data in machine learning systems. In recent years, there has been an increase in the adoption of federated learning in healthcare applications. On the other hand, personal data such as text messages and emails also contain highly sensitive data, typically used in natural language processing (NLP) applications. In this paper, we investigate the adoption of federated learning approach in the domain of NLP requiring sensitive data. For this purpose, we have developed a federated learning infrastructure that performs training on remote devices without the need to share data. We demonstrate the usability of this infrastructure for NLP by focusing on sentiment analysis. The results show that the federated learning approach trained a model with comparable test accuracy to the centralized approach. Therefore, federated learning is a viable alternative for developing NLP models to preserve the privacy of data. Â© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images
(Springer Science and Business Media Deutschland GmbH, 2024) Gupta, P.K.; Lal, S.; Kiran, M.S.; Husain, F.
A clinical ultrasound imaging plays a significant role in the proper diagnosis of patients because, it is a cost-effective and non-invasive technique in comparison with other methods. The speckle noise contamination caused by ultrasound images during the acquisition process degrades its visual quality, which makes the diagnosis task difficult for physicians. Hence, to improve their visual quality, despeckling filters are commonly used for processing of such images. However, several disadvantages of existing despeckling filters discourage the use of existing despeckling filters to reduce the effect of speckle noise. In this paper, two dimensional cuckoo search optimization algorithm based despeckling filter is proposed for avoiding limitations of various existing despeckling filters. Proposed despeckling filter is developed by combining fast non-local means filter and 2D finite impulse response (FIR) filter with cuckoo search optimization algorithm. In the proposed despeckling filter, the coefficients of 2D FIR filter are optimized by using the cuckoo search optimization algorithm. The quantitative results comparison between the proposed despeckling filter and other existing despeckling filters are analyzed by evaluating PSNR, MSE, MAE, and SSIM values for different real ultrasound images. Results reveal that the visual quality obtained by the proposed despeckling filter is better than other existing despeckling filters. The numerical results also reveal that the proposed despeckling filter is highly effective for despeckling the clinical ultrasound images. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.