Faculty Publications
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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 Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach(Springer Verlag service@springer.de, 2017) Krishna Kumar, P.; Araki, T.; Rajan, J.; Saba, L.; Lavra, F.; Ikeda, N.; Sharma, A.M.; Shafique, S.; Nicolaïdes, A.; Laird, J.R.; Gupta, A.; Suri, J.S.Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients’ left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p < 0.0001) and 0.99 (p < 0.0001), respectively. The precision of merit between the manual expert tracings and the automated system was 97.7 and 98.7%, respectively. The experimental analysis demonstrated superior performance of the proposed method over conventional approaches. Several statistical tests demonstrated the stability and reliability of the automated system. © 2016, International Federation for Medical and Biological Engineering.Item Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods(PWN-Polish Scientific Publishers bbe@ibib.waw.pl, 2018) Panicker, R.O.; Kalmady, K.S.; Rajan, J.; Sabu, M.K.An automatic method for the detection of Tuberculosis (TB) bacilli from microscopic sputum smear images is presented in this paper. According to WHO, TB is the ninth leading cause of death all over the world. There are various techniques to diagnose TB, of which conventional microscopic sputum smear examination is considered to be the gold standard. However, the aforementioned method of diagnosis is time intensive and error prone, even in experienced hands. The proposed method performs detection of TB, by image binarization and subsequent classification of detected regions using a convolutional neural network. We have evaluated our algorithm using a dataset of 22 sputum smear microscopic images with different backgrounds (high density and low-density images). Experimental results show that the proposed algorithm achieves 97.13% recall, 78.4% precision and 86.76% F-score for the TB detection. The proposed method automatically detects whether the sputum smear images is infected with TB or not. This method will aid clinicians to predict the disease accurately in a short span of time, thereby helping in improving the clinical outcome. © 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of SciencesItem Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique(Elsevier Ltd, 2018) Yamanakkanavar, Y.; Madipalli, P.; Rajan, J.; Kumar, P.K.; Narasimhadhan, A.V.Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images. © 2017 Elsevier LtdItem A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images(Elsevier Ltd, 2018) Srinidhi, C.L.; Aparna., P.; Rajan, J.Background and objective: Accurate segmentation of retinal vessels from color fundus images play a significant role in early diagnosis of various ocular, systemic and neuro-degenerative diseases. Segmenting retinal vessels is challenging due to varying nature of vessel caliber, the proximal presence of pathological lesions, strong central vessel reflex and relatively low contrast images. Most existing methods mainly rely on carefully designed hand-crafted features to model the local geometrical appearance of vasculature structures, which often lacks the discriminative capability in segmenting vessels from a noisy and cluttered background. Methods: We propose a novel visual attention guided unsupervised feature learning (VA-UFL) approach to automatically learn the most discriminative features for segmenting vessels in retinal images. Our VA-UFL approach captures both the knowledge of visual attention mechanism and multi-scale contextual information to selectively visualize the most relevant part of the structure in a given local patch. This allows us to encode a rich hierarchical information into unsupervised filtering learning to generate a set of most discriminative features that aid in the accurate segmentation of vessels, even in the presence of cluttered background. Results: Our proposed method is validated on the five publicly available retinal datasets: DRIVE, STARE, CHASE_DB1, IOSTAR and RC-SLO. The experimental results show that the proposed approach significantly outperformed the state-of-the-art methods in terms of sensitivity, accuracy and area under the receiver operating characteristic curve across all five datasets. Specifically, the method achieved an average sensitivity greater than 0.82, which is 7% higher compared to all existing approaches validated on DRIVE, CHASE_DB1, IOSTAR and RC-SLO datasets, and outperformed even second-human observer. The method is shown to be robust to segmentation of thin vessels, strong central vessel reflex, complex crossover structures and fares well on abnormal cases. Conclusions: The discriminative features learned via visual attention mechanism is superior to hand-crafted features, and it is easily adaptable to various kind of datasets where generous training images are often scarce. Hence, our approach can be easily integrated into large-scale retinal screening programs where the expensive labelled annotation is often unavailable. © 2018 Elsevier LtdItem Automatic detection and localization of Focal Cortical Dysplasia lesions in MRI using fully convolutional neural network(Elsevier Ltd, 2019) Bijay Dev, K.M.; Pawan, P.S.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.Focal cortical dysplasia (FCD) is the leading cause of drug-resistant epilepsy in both children and adults. At present, the only therapeutic approach in patients with drug-resistant epilepsy is surgery. Hence, the quantification of FCD via non-invasive imaging techniques helps physicians to decide on surgical interventions. The properties like non-invasiveness and capability to produce high-resolution images makes magnetic resonance imaging an ideal tool for detecting the FCD to an extent. The FCD lesions vary in size, shape, and location for different patients and make the manual detection time consuming and sensitive to the experience of the observer. Automatic segmentation of FCD lesions is challenging due to the difference in signal strength in images acquired with different machines, noise, and other kinds of distortions such as motion artifacts. Most of the methods proposed in the literature use conventional machine learning and image processing techniques in which their accuracy relies on the trained features. Hence, feature extraction should be done more precisely which requires human expertise. The ability to learn the appropriate features/representations from the training data without any human interventions makes the convolutional neural network (CNN) the suitable method for addressing these drawbacks. As far as we are aware, this work is the first one to use a CNN based model to solve the aforementioned problem using only MRI FLAIR images. We customized the popular U-Net architecture and trained the proposed model from scratch (using MRI images acquired with 1.5T and 3T scanners). FCD detection rate (recall) of the proposed model is 82.5 (33/40 patients detected correctly). © 2019Item Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images(Elsevier Sp. z o.o., 2020) Anoop, B.N.; Pavan, R.; Girish, G.N.; Kothari, A.R.; Rajan, J.Segmentation of retinal layers is a vital and important step in computerized processing and the study of retinal Optical Coherence Tomography (OCT) images. However, automatic segmentation of retinal layers is challenging due to the presence of noise, widely varying reflectivity of image components, variations in morphology and alignment of layers in the presence of retinal diseases. In this paper, we propose a Fully Convolutional Network (FCN) termed as DelNet based on a deep ensemble learning approach to selectively segment retinal layers from OCT scans. The proposed model is tested on a publicly available DUKE DME dataset. Comparative analysis with other state-of-the-art methods on a benchmark dataset shows that the performance of DelNet is superior to other methods. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences