Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A hybrid model for rician noise reduction in MRI(IEEE Computer Society help@computer.org, 2013) Sudeep, P.V.; Ponnusamy, P.; Rajan, J.Magnetic Resonance Images (MRI) are normally corrupted with random noise mainly arised from the patient's body and from the scanning apparatus. This paper describes a new technique to remove the homogeneous Rician noise in the magnitude magnetic resonance (MR) images. Linear minimum mean square error (LMMSE) estimator is a good choice to solve this inverse problem. In another way, denoising can be considered as a solution for L1 regularization problem of compressed sensing (CS). The Split Bregman iteration technique is effectively used in this stage in order to minimize the total variation (TV) functional. By combining these results in transform domain, the denoising is expected to be improved. Experiments show that the proposed algorithm outperforms other existing methods in the literature in terms of Peak Signal to Noise Ratio (PSNR). © 2013 IEEE.Item A new nonlocal maximum likelihood estimation method for denoising magnetic resonance images(2013) Rajan, J.; den Dekker, A.J.; Juntu, J.; Sijbers, J.Denoising of Magnetic Resonance images is important for proper visual analysis, accurate parameter estimation, and for further preprocessing of these images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising Magnetic Resonance (MR) images. Among the ML based methods, the recently proposed Non Local Maximum Likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation and, as a result, optimal results cannot be achieved because of over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness. © Springer-Verlag 2013.Item Single image super resolution from compressive samples using two level sparsity based reconstruction(Elsevier B.V., 2015) Nath, A.G.; Nair, M.S.; Rajan, J.Super Resolution based on Compressed Sensing (CS) considers low resolution (LR) image patch as the compressive samples of its high resolution (HR) patch. Compressed sensing based image acquisition systems acquire less number of random linear measurements without first collecting all the pixel values. But using these compressive measurements directly to reconstruct the image causes quality issues. In this paper an image super-resolution method with two level sparsity based reconstruction via patch based image interpolation and dictionary learning is proposed. The first level reconstruction generates a low resolution image from random samples and the interpolation scheme used in this algorithm reduces the HR-LR patch coherency due to neighborhood issue which is a major drawback of single image super resolution algorithms. The dictionary based reconstruction phase generates the high resolution image from the low resolution output of the first level reconstruction phase. The experimental results proved that the proposed two level reconstruction scheme recovers more details of the image and yields improved results from very few samples (around 35-45%) than the state-of-the-art algorithms which uses low resolution image itself as input. The results are compared by considering both PSNR values and visual perception. © 2015 The Authors.Item Coupled PDE for Ultrasound Despeckling Using ENI Classification(Elsevier B.V., 2016) Soorajkumar, R.; Krishnakumar, P.; Girish, D.; Rajan, J.Speckle is a type of noise which is often present in ultrasound images. Speckle is formed due to constructive or destructive interference of ultrasound waves. Due to the granular pattern of speckle noise, it hides important details in ultrasound images. Many despeckling techniques are proposed in the literature, but most of them fail to reach a balance between the removal of speckle noise and preservation of the fine details in the image. In this work, an improved coupled PDE model is proposed which combines second order selective degenerate diffusion (SDD) model and fourth order PDE model based on the assumption that speckle in ultrasound image follows Gamma distribution. An edge noise interior (ENI) method is used to control the diffusion. With the help of ENI controlling function, the diffusion at edge pixels and noisy pixels are selectively accomplished with varying speed. Thus, the proposed model preserves the edges and fine texture details in the image. The model is tested on simulated images after corrupting the images with various levels of Gamma noise. Further, we have tested it on real ultrasound images also. The performance of the proposed model is compared with other similar techniques and the proposed method outperforms other state-of-the-art methods, both in terms of qualitative and quantitative measures. © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.Item A comparative study of different auto-focus methods for mycobacterium tuberculosis detection from brightfield microscopic images(Institute of Electrical and Electronics Engineers Inc., 2016) Saini, G.; Panicker, R.O.; Soman, B.; Rajan, J.Automatic tuberculosis (TB) detection methods using microscopic images are becoming more popular now a days. Auto-focusing is the first and foremost step in the development of an automated microscope for TB detection. Different focus measures exist for the selection of in-focus image from both fluorescence and bright field microscopic images. Recently, some researchers have investigated and compared several different focus measures for TB sputum microscopy. In this study we focused on bright field microscopic images and considered around 20 popular focus measures. Experiments were conducted on a large set of images having different features. © 2016 IEEE.Item Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform(Institute of Electrical and Electronics Engineers Inc., 2016) Girish, G.N.; Kothari, A.R.; Rajan, J.Optical Coherence Tomography (OCT) has emerged as a major diagnostic modality for retinal imaging. Although OCT generates gross volumetric data, manual analysis of the images for locating or quantifying retinal cysts is a time consuming process. Recently semi- and fully-automatic methods for locating and segmenting retinal cysts have been proposed in the literature. Our paper proposes a fully automatic method for intra-retinal cyst segmentation using marker controlled watershed transform on B-scan images obtained on OCT scanning. Markers are obtained using k-means clustering and used as sources for topographical based watershed transform for final segmentation. Proposed method was evaluated both quantitatively and qualitatively on Optima Cyst Challenge dataset against ground truth obtained from two graders. Experimental results show that the proposed method outperformed other recently proposed methods. Our algorithm achieved a recall rate of 82% while preserving precision rate of 77%, and gave a higher correlation rate of 96% with ground truth obtained from two graders. © 2016 IEEE.Item Fourth order PDE based ultrasound despeckling using ENI classification(Institute of Electrical and Electronics Engineers Inc., 2016) Soorajkumar, R.; Krishna Kumar, P.; Girish, D.; Rajan, J.Medical ultrasound images are generally corrupted with a type of signal dependent noise called speckle. The major reason for the speckle in ultrasound images is the constructive or destructive interference of ultrasound waves. The granular pattern of the speckle noise degrades the image and hinders the information present in it. In this work, we developed an improved speckle denoising method using a fourth order partial differential equation (PDE) model by integrating Edge Noise Interior method in it. Edge Noise Interior (ENI) method preserves the edges and counts the number of homogeneous pixels in the neighbourhood to classify the edges. Furthermore, a maximum likelihood technique is used to estimate and remove the bias in the denoised images. The proposed method is compared against other existing methods and validated for both simulated as well as real ultrasound images. The proposed method outperforms other state-of-the-art methods in terms of qualitative and quantitative analysis. © 2016 IEEE.Item An improved contextual information based approach for anomaly detection via adaptive inference for surveillance application(Springer Verlag service@springer.de, 2017) Rao, T.J.N.; Girish, G.N.; Rajan, J.Anomalous event detection is the foremost objective of a visual surveillance system. Using contextual information and probabilistic inference mechanisms is a recent trend in this direction. The proposed method is an improved version of the Spatio-Temporal Compositions (STC) concept, introduced earlier. Specific modifications are applied to STC method to reduce time complexity and improve the performance. The non-overlapping volume and ensemble formation employed reduce the iterations in codebook construction and probabilistic modeling steps. A simpler procedure for codebook construction has been proposed. A non-parametric probabilistic model and adaptive inference mechanisms to avoid the use of a single experimental threshold value are the other contributions. An additional feature such as event-driven high-resolution localization of unusual events is incorporated to aid in surveillance application. The proposed method produced promising results when compared to STC and other state-of-the-art approaches when experimented on seven standard datasets with simple/complex actions, in non-crowded/crowded environments. © Springer Science+Business Media Singapore 2017.Item A semi-automatic method for carotid artery wall segmentation in MR images(Institute of Electrical and Electronics Engineers Inc., 2017) Kumar, P.K.; Kesavadas, C.; Rajan, J.The quantification of carotid artery stenosis via imaging techniques guides the physicians to take a decision regarding surgical interventions. The measurement of wall thickness from magnetic resonance (MR) images is a promising approach to measure the degree of carotid stenosis. Manual tracing of the carotid vessel walls is time consuming and is sensitive to observer variability. Further, the existing segmentation techniques are limited by the poor contrast and presence of noise in MR images. The objective this paper is to present a novel segmentation strategy for carotid lumen and outer wall from MR images. The segmentation has been carried out in two stages which starts with a user assisted region of interest selection. In the first stage, an active contour based global segmentation has been applied to classify the lumen region. In the second stage, morphological gradient of the region of interest has been computed. This is followed by particle swarm optimization based localized segmentation to separate the wall region. The results demonstrate excellent correspondence between the automatic and manual tracings for lumen and outer walls of the carotid artery. © 2016 IEEE.Item Study of malignancy associated changes in sputum images as an indicator of lung cancer(Institute of Electrical and Electronics Engineers Inc., 2017) Sudheesh, R.K.; Rajan, J.; Veena, V.S.; Sujathan, K.Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC) are the subtle changes that happens to the normal appearing cells near or distant from the malignant cells. Literature suggests that these changes can be used as an indicator for lung cancer rather than using malignant cells which are very less in number compared to the normal appearing cells in sputum cytology images. The proposed work is intended to detect cells with MAC from sputum smear images. Analysis of nuclei texture features of sputum cell nuclei using Gray Level Co-occurrence Matrix and Gray Level Run Length Matrix from both normal and cancer patients revealed that both type of cells could be differentiated. Among 110 texture features calculated for each nuclei, a set of 35 features which clearly distinguishes normal cells and normal appearing cells were chosen. Support Vector Machine (SVM) classifier is used to classify the cells into two classes i.e cells with MAC and cells without MAC. This study demonstrates that the presence of MAC cells in conventional microscopic sputum cytology images can be identified using image processing techniques and it can have some significance in the early detection of lung cancer. © 2016 IEEE.
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