Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT-framework(John Wiley and Sons Inc, 2015) Kumar, P.K.; Darshan, P.; Kumar, S.; Ravindra, R.; Rajan, J.; Saba, L.; Suri, J.S.The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state-of-the-art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256-264, 2015 © 2015 Wiley Periodicals, Inc.Item Stroke classification from computed tomography scans using 3D convolutional neural network(Elsevier Ltd, 2022) Neethi, A.S.; Niyas, S.; Kannath, S.K.; Mathew, J.; Anzar, A.M.; Rajan, J.Stroke is a cerebrovascular condition with a significant morbidity and mortality rate and causes physical disabilities for survivors. Once the symptoms are identified, it requires a time-critical diagnosis with the help of the most commonly available imaging techniques. Computed tomography (CT) scans are used worldwide for preliminary stroke diagnosis. It demands the expertise and experience of a radiologist to identify the stroke type, which is critical for initiating the treatment. This work attempts to gather those domain skills and build a model from CT scans to diagnose stroke. The non-contrast computed tomography (NCCT) scan of the brain comprises volumetric images or a 3D stack of image slices. So, a model that aims to solve the problem by targeting a 2D slice may fail to address the volumetric nature. We propose a 3D-based fully convolutional classification model to identify stroke cases from CT images that take into account the contextual longitudinal composition of volumetric data. We formulate a custom pre-processing module to enhance the scans and aid in improving the classification performance. Some of the significant challenges faced by 3D CNN are the less number of training samples, and the number of scans is mostly biased in favor of normal patients. In this work, the limitation of insufficient training volume and class imbalanced data have been rectified with the help of a strided slicing approach. A block-wise design was used to formulate the proposed network, with the initial part focusing on adjusting the dimensionality, at the same time retaining the features. Later on, the accumulated feature maps were effectively learned utilizing bundled convolutions and skip connections. The results of the proposed method were compared against 3D CNN stroke classification models on NCCT, various 3D CNN architectures on other brain imaging modalities, and 3D extensions of some of the classical CNN architectures. The proposed method achieved an improvement of 14.28% in the F1-score over the state-of-the-art 3D CNN stroke classification model. © 2022 Elsevier LtdItem SMC-CNN: Stacked Multi-Channel Convolution Neural Network for Predicting Acute Brain Infarct From Magnetic Resonance Imaging Sequences(Institute of Electrical and Electronics Engineers Inc., 2024) Shetty, S.; Ananthanarayana, V.S.; Mahale, A.; Devi, S.A.Acute brain infarct is a major cause of stroke and the second most common cause of fatality worldwide. It's characterized by abrupt symptoms persisting over 24 hours or leading to death due to blood vessel blockage. There is a need for a fast and automated way to diagnose and predict the outcome of this condition. Medical image analysis has witnessed promising outcomes with the application of deep learning (DL) techniques. To address this problem, we propose two Stacked Multi-Channel Convolutional Neural Networks (SMC-CNNs) for predicting acute infarct using individual and multiple Magnetic Resonance Imaging (MRI) sequences, including Diffusion-Weighted Imaging (DWI), Apparent Diffusion Coefficient (ADC), T2-weighted Fluid-Attenuated Inversion Recovery (T2-FLAIR), and Susceptibility-Weighted Imaging (SWI). We collected de-identified and de-linked MRI sequences from KMC Hospital (Mangalore, India) and compared the efficacy of our models with eight baseline state-of-the-art DL models in an extensive benchmarking study. The collected dataset was pre-processed using a proposed contour-based brain segmentation technique to isolate brain contours from the MRI sequences. These contours were ingested into the two proposed models: Stacked Multi-Channel Convolutional Neural Network for Individual sequences (SMC-CNN-I) and Stacked Multi-Channel Convolutional Neural Network for Multiple sequences (SMC-CNN-M), to predict acute infarct. We conducted experimental evaluations on individual MRI sequences to assess the effectiveness of the models for each sequence and found that the DWI and T2-FLAIR imaging sequences contained more discriminative features for acute infarct prediction than the other sequences. We performed an ablation study by varying and fusing different MRI sequences and observed that the proposed model achieved superior results when all four MRI sequences were used as inputs. We also tested the proposed models on synthetic MRI data generated using a Deep Convolutional Generative Adversarial Network (DCGAN) architecture. We found that the models produced improved results, demonstrating their ability to perform well on real-world data. We conducted a quantitative analysis followed by a qualitative analysis by visualizing infarcts using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. This demonstrated the model's ability to detect the precise location of abnormalities. © 2013 IEEE.