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120 results on '"Thakur, Niharika"'

7. Region-based Convolutional Neural Network (R-CNN) architecture for auto-cropping of pancreatic computed tomography.

Author

Juneja, Mamta, Singh, Gurunameh, Chanana, Chirag, Verma, Rishabh, Thakur, Niharika, and Jindal, Prashant

Subjects
CONVOLUTIONAL neural networks, COMPUTER-aided diagnosis, COMPUTED tomography, COMPUTER-assisted image analysis (Medicine), IMAGE analysis
Abstract

Automatic pancreas detection and cropping with high precision from medical images is an important yet challenging problem for medical image analysis and Computer-Aided Diagnosis (CAD). Factors relating to the limited availability of image data and segmentation methodology hinder this task. High variability in the location of the pancreas,which occupies a very small area of the pancreatic Computed Tomography (CT) scans,and the anatomy of organs also add to the list of issues. These challenges necessitate an urgent need for the development of localization and auto-cropping methods of the region of interest (ROI). This paper presents the results obtained by the implementation of Region-based Convolutional Neural Network (RCNN)-Crop inspired by the Region Proposal Network (RPN) and Feature Pyramid Network (FPN) to localize the pancreas by building bounding boxes and auto-crop the ROI obtained from various other organs in the pancreatic CT scans and has a Mean Average Precision (mAP) of 28.10% for the dataset provided. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Survey of Classification Approaches for Glaucoma Diagnosis from Retinal Images

Author

Thakur, Niharika, Juneja, Mamta, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Choudhary, Ramesh K., editor, Mandal, Jyotsna Kumar, editor, and Bhattacharyya, Dhananjay, editor

Published
2018
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43. Denoising of computed tomography using bilateral median based autoencoder network.

Author

Juneja, Mamta, Joshi, Saasha, Singla, Naveen, Ahuja, Shaurya, Saini, Sumindar Kaur, Thakur, Niharika, and Jindal, Prashant

Subjects
COMPUTED tomography, COMPUTER-aided diagnosis, IMAGE processing, IMAGE denoising, RANDOM noise theory, SIGNAL-to-noise ratio
Abstract

Denoising of Computed tomography (CT) images is a critical aspect of image processing that is expected to improve the performance of Computer‐aided diagnosis (CAD) systems. However, the use of complex imaging modalities such as CT imaging to ascertain pancreatic cancer is vulnerable to gaussian and poisson noises, making image denoising an imperative step for the accurate performance of CAD systems. This paper presents a Bilateral median based autoencoder network (BMAuto‐Net) constructed with intermediate batch normalization layers and dropout factors to eliminate gaussian noise from the CT images. The skip connections adjoining the network, prevent performance degradation that generally occurs in most autoencoder architectures. Based on the presented study, BMAuto‐Net is reckoned to outperform other traditional filters and autoencoders. The performance measurement of the proposed architecture is performed using the peak signal‐to‐noise ratio (PSNR), mean squared error (MSE), and structured similarity index (SSIM) metric values. The Cancer imaging archive (TCIA) dataset consisting of 19 000 CT images is used to validate the performance of the architecture with average PSNR values of 30.01, 30.53, and 30.52, MSE values of 98.23, 98.87, and 98.94, and SSIM of values of 0.67, 0.60, and 0.57 for noise factors (NFs) of 0.1, 0.3, and 0.5 respectively. [ABSTRACT FROM AUTHOR]

Published
2022
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