Faculty Publications
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Publications by NITK Faculty
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Item Nonlocal linear minimum mean square error methods for denoising MRI(Elsevier Ltd, 2015) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Rajan, J.The presence of noise results in quality deterioration of magnetic resonance (MR) images and thus limits the visual inspection and influence the quantitative measurements from the data. In this work, an efficient two stage linear minimum mean square error (LMMSE) method is proposed for the enhancement of magnitude MR images in which data in the presence of noise follows a Rician distribution. The conventional Rician LMMSE estimator determines a closed-form analytical solution to the aforementioned inverse problem. Even-though computationally efficient, this approach fails to take advantage of data redundancy in the 3D MR data and hence leads to a suboptimal filtering performance. Motivated by this observation, we put forward the concept of nonlocal implementation with LMMSE estimation method. To select appropriate samples for the nonlocal version of the LMMSE estimation, the similarity weights are computed using Euclidean distance between either the gray level values in the spatial domain or the coefficients in the transformed domain. Assuming that the signal dependent component of the noise is optimally suppressed by this filtering and the rest is a white and uncorrelated noise with the image, we adopt a second stage LMMSE filtering in the principal component analysis (PCA) domain to further enhance the image and the noise variance is adaptively adjusted. Experiments on both simulated and real data show that the proposed filters have excellent filtering performance over other state-of-the-art methods. © 2015 Elsevier Ltd. All rights reserved.Item Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering(Elsevier Ltd, 2016) Sudeep, P.V.; Issac Niwas, S.; Ponnusamy, P.; Rajan, J.; Xiaojun, Y.; Wang, X.; Luo, Y.; Liu, L.Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter. © 2016 Elsevier Ltd.Item Speckle reduction in medical ultrasound images using an unbiased non-local means method(Elsevier Ltd, 2016) Sudeep, P.V.; Ponnusamy, P.; Rajan, J.; Baradaran, H.; Saba, L.; Gupta, A.; Suri, J.S.Enhancement of ultrasound (US) images is required for proper visual inspection and further pre-processing since US images are generally corrupted with speckle. In this paper, a new approach based on non-local means (NLM) method is proposed to remove the speckle noise in the US images. Since the interpolated final Cartesian image produced from uncompressed ultrasound data contaminated with fully developed speckle can be represented by a Gamma distribution, a Gamma model is incorporated in the proposed denoising procedure. In addition, the scale and shape parameters of the Gamma distribution are estimated using the maximum likelihood (ML) method. Bias due to speckle noise is expressed using these parameters and is removed from the NLM filtered output. The experiments on phantom images and real 2D ultrasound datasets show that the proposed method outperforms other related well-accepted methods, both in terms of objective and subjective evaluations. The results demonstrate that the proposed method has a better performance in both speckle reduction and preservation of structural features. © 2016 Elsevier Ltd. All rights reserved.Item Laser assisted zona hatching does not lead to immediate impairment in human embryo quality and metabolism(Taylor and Francis Ltd healthcare.enquiries@informa.com, 2016) Uppangala, S.; D’Souza, F.; Pudakalakatti, S.; Atreya, H.S.; Raval, K.; Kalthur, G.; Adiga, S.K.Laser assisted zona hatching (LAH) is a routinely used therapeutic intervention in assisted reproductive technology for patients with poor prognosis. However, results are not conclusive in demonstrating the benefits of zona hatching in improving the pregnancy rate. Recent observations on LAH induced genetic instability in animal embryos prompted us to look into the effects of laser assisted zona hatching on the human preimplantation embryo quality and metabolic uptake using high resolution nuclear magnetic resonance (NMR) technology. This experimental prospective study included fifty embryos from twenty-five patients undergoing intra cytoplasmic sperm injection. Embryo quality assessment followed by profiling of spent media for the non-invasive evaluation of metabolites was performed using NMR spectroscopy 24 hours after laser treatment and compared with that of non-treated sibling embryos. Both cell number and embryo quality on day 3 of development did not vary significantly between the two groups at 24 hours post laser treatment interval. Time lapse monitoring of the embryos for 24 hours did not reveal blastomere fragmentation adjacent to the point of laser treatment. Similarly, principal component analysis of metabolites did not demonstrate any variation across the groups. These results suggest that laser assisted zona hatching does not affect human preimplantation embryo morphology and metabolism at least until 24 hours post laser assisted zona hatching. However, studies are required to elucidate laser induced metabolic and developmental changes at extended time periods. Abbreviations: AH: assisted hatching; ART: assisted reproductive technology; DNA: deoxy-ribo nucleic acid; LAH: laser assisted hatching; MHz: megahertz; NMR: nuclear magnetic resonance; PCA: principal component analysis; PGD: preimplantation genetic diagnosis; TLM: time lapse monitoring © 2016 Taylor & Francis.Item Fenton's treatment of actual agriculture runoff water containing herbicides(IWA Publishing 12 Caxton Street London SW1H 0QS, 2017) Sangami, S.; Manu, B.This research was to study the efficiency of the Fenton's treatment process for the removal of three herbicides, namely 2,4-dichlorophenoxy acetic acid (2,4-D), ametryn and dicamba from the sugarcane field runoff water. The treatment process was designed with the Taguchi approach by varying the four factors such as H 2 O 2 /COD (1-3.5), H 2 O 2 /Fe 2+ (5-50), pH (2-5) and reaction time (30-240 min) as independent variables. Influence of these parameters on chemical oxygen demand (COD), ametryn, dicamba and 2,4-D removal efficiencies (dependent variables) were investigated by performing signal to noise ratio and other statistical analysis. The optimum conditions were found to be H 2 O 2 /COD: 2.125, H 2 O 2 /Fe 2+ : 27.5, pH: 3.5 and reaction time of 135 min for removal efficiencies of 100% for ametryn, 95.42% for dicamba, 88.2% for 2,4-D and with 75% of overall COD removal efficiencies. However, the percentage contribution of H 2 O 2 /COD ratio was observed to be significant among all four independent variables and were 44.16%, 67.57%, 51.85% and 50.66% for %COD, ametryn, dicamba and 2,4-D removal efficiencies, respectively. The maximum removal of herbicides was observed with the H 2 O 2 dosage of 5.44 mM and Fe 2+ dosage of 0.12 mM at pH 3.5. © IWA Publishing 2017 W.Item A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans(Elsevier Ireland Ltd, 2018) Girish, G.N.; Anima, V.A.; Kothari, A.R.; Sudeep, P.V.; Roychowdhury, S.; Rajan, J.(Background and objectives) Retinal cysts are formed by accumulation of fluid in the retina caused by leakages from inflammation or vitreous fractures. Analysis of the retinal cystic spaces holds significance in detection and treatment of several ocular diseases like age-related macular degeneration, diabetic macular edema etc. Thus, segmentation of intra-retinal cysts and quantification of cystic spaces are vital for retinal pathology and severity detection. In the recent years, automated segmentation of intra-retinal cysts using optical coherence tomography B-scans has gained significant importance in the field of retinal image analysis. The objective of this paper is to compare different intra-retinal cyst segmentation algorithms for comparative analysis and benchmarking purposes. (Methods) In this work, we employ a modular approach for standardizing the different segmentation algorithms. Further, we analyze the variations in automated cyst segmentation performances and method scalability across image acquisition systems by using the publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge). (Results) Several key automated methods are comparatively analyzed using quantitative and qualitative experiments. Our analysis demonstrates the significance of variations in signal-to-noise ratio (SNR), retinal layer morphology and post-processing steps on the automated cyst segmentation processes. (Conclusion) This benchmarking study provides insights towards the scalability of automated processes across vendor-specific imaging modalities to provide guidance for retinal pathology diagnostics and treatment processes. © 2017 Elsevier B.V.Item A novel pre-processing procedure for enhanced feature extraction and characterization of electromyogram signals(Elsevier Ltd, 2018) Powar, O.S.; Chemmangat, K.; Figarado, S.In the analysis of electromyogram signals, the challenge lies in the suppression of noise associated with the measurement and signal conditioning. The main aim of this paper is to present a novel pre-processing step, namely Minimum Entropy Deconvolution Adjusted (MEDA), to enhance the signal for feature extraction resulting in better characterization of different upper limb motions. MEDA method is based on finding the set of filter coefficients that recover the output signal with maximum value of kurtosis while minimizing the low kurtosis noise components. The proposed method has been validated on surface electromyogram dataset collected from seven subjects performing eight classes of hand movements (wrist flexion, wrist radial deviation, hand close, tripod, wrist extension, wrist ulnar deviation, cylindrical and key grip) with only two pairs of electrodes recorded from flexor carpi radialis and extensor carpi radialis on the forearm. The performance of the MEDA has been compared across four classifiers namely J-48, k-nearest neighbours (KNN), Naives Bayes and Linear Discriminant Analysis (LDA) attaining the classification accuracy of 85.3 ± 4%, 85.67 ± 5%, 76 ± 6% and 91.2 ± 2% respectively. Practical results demonstrate the feasibility of the approach with mean percentage increase in classification accuracy of 20.5%, without significant increase in computational time across seven subjects demonstrating the significance of MEDA in classification. © 2018 Elsevier LtdItem A Noise Reduction Technique Based on Nonlinear Kernel Function for Heart Sound Analysis(Institute of Electrical and Electronics Engineers Inc., 2018) Mondal, A.; Saxena, I.; Tang, H.; Banerjee, P.The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods. © 2013 IEEE.Item An Integrated Approach of CNT Front-end Amplifier towards Spikes Monitoring for Neuro-prosthetic Diagnosis(SpringerOpen, 2018) Kumar, S.; Kim, B.-S.; Song, H.The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to 80 ?V while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of 2 ?W under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves 60 G? of input impedance and input referred noise of 8.5 nv/Hzover the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists. © 2018, The Korean BioChip Society and Springer-Verlag GmbH Germany, part of Springer Nature.Item Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition(Elsevier Ltd, 2022) Chandrasekar, A.; Shekar, D.D.; Hiremath, A.C.; Chemmangat, K.The electrocardiogram is a widely used measurement for individual heart conditions, and much effort has been put into automatic arrhythmia diagnosis using machine learning. However, the classification performance is hampered by the use of less representative data in conjunction with traditional machine learning models. This paper proposes a novel algorithm for pre-processing raw Electrocardiogram signals via Gaussian Assisted Signal Smoothing. In this method, the ECG signal is modeled as a low pass component and a weighted sum of Gaussians. The Gaussians are used to model the peak characteristics of the signal, effectively preserving its structure and morphology while eliminating the noise, which is evident by the enhanced peak signal-to-noise ratio of the GASS signal. The R peaks obtained from the Pan Tompkins algorithm are used to extract the heartbeats from the filtered signal using a windowing technique. A cascaded combination of a Convolutional Neural Network and a Quadratic Support Vector Machine is then used to classify the heartbeats. The CNN model has 131,661 parameters, making it much lighter than previously reported works. The MIT-BIH Arrhythmia Database was used for our experiments. Across eleven classes, our results reveal that the model has an accuracy of 97.63% and an average F1 score of 0.9263. In contrast, previous works have primarily focused on a one vs. all or a five-class classification. From a signal processing standpoint, the proposed method offers a promising solution for Signal Filtering and Arrhythmia Classification. © 2021 Elsevier Ltd