Browsing by Author "Panda R."

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Automated retinal nerve fiber layer defect detection using fundus imaging in glaucoma
(2018) Panda R.; Puhan N.B.; Rao A.; Padhy D.; Panda G.
Retinal nerve fiber layer defect (RNFLD) provides an early objective evidence of structural changes in glaucoma. RNFLD detection is currently carried out using imaging modalities like OCT and GDx which are expensive for routine practice. In this regard, we propose a novel automatic method for RNFLD detection and angular width quantification using cost effective redfree fundus images to be practically useful for computer-assisted glaucoma risk assessment. After blood vessel inpainting and CLAHE based contrast enhancement, the initial boundary pixels are identified by local minima analysis of the 1-D intensity profiles on concentric circles. The true boundary pixels are classified using random forest trained by newly proposed cumulative zero count local binary pattern (CZC-LBP) and directional differential energy (DDE) along with Shannon, Tsallis entropy and intensity features. Finally, the RNFLD angular width is obtained by random sample consensus (RANSAC) line fitting on the detected set of boundary pixels. The proposed method is found to achieve high RNFLD detection performance on a newly created dataset with sensitivity (SN) of 0.7821 at 0.2727 false positives per image (FPI) and the area under curve (AUC) value is obtained as 0.8733. � 2018 Elsevier Ltd
Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma
(2018) Panda R.; Puhan N.B.; Rao A.; Mandal B.; Padhy D.; Panda G.
Glaucoma is a progressive optic neuropathy characterized by peripheral visual field loss, which is caused by degeneration of retinal nerve fibers. The peripheral vision loss due to glaucoma is asymptomatic. If not detected and treated at an early stage, it leads to complete blindness, which is irreversible in nature. The retinal nerve fiber layer defect (RNFLD) provides an earliest objective evidence of glaucoma. In this regard, we explore cost-effective redfree fundus imaging for RNFLD detection to be practically useful for computer-assisted early glaucoma risk assessment. RNFLD appears as a wedge shaped arcuate structure radiating from the optic disc. The very low contrast between RNFLD and background makes its visual detection quite challenging even by medical experts. In our study, we formulate a deep convolutional neural network (CNN) based patch classification strategy for RNFLD boundary localization. A large number of RNFLD and background image patches train the deep CNN model, which extracts sufficient discriminative information from the patches and results in accurate RNFLD boundary pixel classification. The proposed approach is found to achieve enhanced RNFLD detection performance with sensitivity of 0.8205 and false positive per image of 0.2000 on a newly created early glaucomatic fundus image database. � 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
Entropy thresholding based microaneurysm detection in fundus images
(2016) Das V.; Puhan N.B.; Panda R.
Microaneurysms are small red dots that occur on the retina during preliminary stage of Diabetic Retinopathy. Computer aided microaneurysm screening is necessary to prevent the aggravation of the disease and further vision loss. In this paper, Shannon and Tsallis entropy thresholding in conjunction with Na�ve Bayes classifier is suggested for microaneurysm detection. Various shape and intensity based features are extracted to eliminate the falsely detected candidates. The proposed method is evaluated by plotting the FROC curves using the Retinopathy Online Challenge (ROC) and DIARETDB1 databases. The proposed method achieves high sensitivity values of 0.421 and 0.477 (at false positive rate of 8) using Shannon and Tsallis entropy thresholding which is better than some existing methods. � 2015 IEEE.
Global vessel symmetry for optic disc detection in retinal images
(2016) Panda R.; Puhan N.B.; Panda G.
Optic disc (OD) detection is an important step in developing computer aided screening systems suitable for glaucoma analysis. In this paper, we present a new method for automatic optic disc detection in retinal (fundus) images. The method is based upon the distribution of major blood vessels. The blood vessels originate from the OD and their random distribution pattern can be approximately divided into two halves by a global symmetric axis passing through the centroid and near the optic disc. We detect this symmetry axis by using partial Hausdorff distance (PHD) measure. Then, the OD center is detected by applying the brightness property of the optic disc region. The proposed method is evaluated and compared on DRIVE, STARE and HRF databases. The average performance of the proposed method is found as: 97.5% in DRIVE, 97.5% in STARE and 100% in HRF database. � 2015 IEEE.
Hausdorff symmetry operator towards retinal blood vessel segmentation
(2014) Panda R.; Puhan N.B.; Panda G.
Automated retinal blood vessel segmentation is a fundamental component in computer aided retinal disease screening system and diagnosis. This paper presents a novel method of Hausdorff symmetry operator for automatic centerline pixel selection towards retinal blood vessel segmentation. Centerline pixels are determined by considering geometrical symmetry (distance and orientation) and Hausdorff distance based point set matching at the centerline pixel. This is performed in subpixel resolution to achieve higher accuracy. Then ?-means clustering is applied to remove false centerline pixels. The selected centerline pixels act as seed points to be used in region growing to segment the retinal blood vessels. Our proposed method is evaluated on DRIVE and STARE databases. The experimental results demonstrate that the performance of the proposed method is comparable with state-of-the-art techniques. The advantages of the proposed method include its ability to correctly segment thin blood vessels, vessels containing light reflex, and disc area is not misclassified as vessels. � 2014 IEEE.
Hot Deformation Behavior of AA2024 with and without In Situ Titanium Diboride Dispersoids
(2019) Panda R.; Gupta R.K.; Mandal A.; Chakravarthy P.
AA2024 is known for its good combination of mechanical properties and is widely used in aircraft fuselage and other aerospace applications. However, because of its relatively lower yield strength, it has limited application in high-stress regions. In alloy AA2024, when titanium diboride particulates are embedded uniformly, it is expected to improve the strength of alloy by working as particulate dispersed composite. However, deformation of such metal matrix composite (MMC) is likely to be difficult and different from the base alloy. In this work, the deformation behavior of AA2024 alloy and its composite with titanium diboride particles developed in situ through salt-metal reaction have been studied. Hot deformation behavior was studied through hot isothermal compression tests over a temperature range of 300�C-450�C and strain rate from 0.01-10 s-1. The results show that there is an increase in flow stress with an increase in strain rate and a decrease in flow stress with an increase in temperature. Processing maps were generated based on the dynamic material model to identify the stable and unstable regions for hot working. The strain rate sensitivity of the composite has been compared to base alloy AA2024. Deformation parameters were calculated from the stress-strain data, and constitutive equations have been generated. Softening in alloy and in MMC is found to be caused by dynamic recrystallization. Microstructural modifications that are caused by titanium diboride reinforcement and its impact during hot deformation are reported. The safe zones for the hot working of both base alloy and MMC were found to be in the range of 380�C to 450�C in a strain rate of 0.001-10 s-1 � 2019 by ASTM International.
Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening
(2018) Panda R.; Puhan N.B.; Panda G.
Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.
New Binary Hausdorff Symmetry measure based seeded region growing for retinal vessel segmentation
(2016) Panda R.; Puhan N.B.; Panda G.
Automated retinal vessel segmentation plays an important role in computer-aided diagnosis of serious diseases such as glaucoma and diabetic retinopathy. This paper contributes, (1) new Binary Hausdorff Symmetry (BHS) measure based automatic seed selection, and (2) new edge distance seeded region growing (EDSRG) algorithm for retinal vessel segmentation. The proposed BHS measure directly provides a binary symmetry decision at each pixel without the computation of continuous symmetry map and image thresholding. In a multiscale mask, the BHS measure is computed using the distance sets of opposite direction angle bins with sub-pixel resolution. The computation of the BHS measure from the Hausdorff distance sets involves point set matching based geometrical interpretation of symmetry. Then, we design a new edge distance seeded region growing (EDSRG) algorithm with the acquired seeds. The performance evaluation in terms of sensitivity, specificity and accuracy is done on the publicly available DRIVE, STARE and HRF databases. The proposed method is found to achieve state-of-the-art vessel segmentation accuracy in three retinal databases; DRIVE-sensitivity (0.7337), specificity (0.9752), accuracy (0.9539); STARE-sensitivity (0.8403), specificity (0.9547), accuracy (0.9424); and HRF-sensitivity (0.8159), specificity (0.9525), accuracy (0.9420). � 2015 Na??cz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier Sp. z o.o. All rights reserved.
Recurrent neural network based retinal nerve fiber layer defect detection in early glaucoma
(2017) Panda R.; Puhan N.B.; Rao A.; Padhy D.; Panda G.
Retinal nerve fiber layer defect (RNFLD) is the earliest objective evidence of glaucoma in fundus images. Glaucoma is an optic neuropathy which causes irreversible vision impairment. Early glaucoma detection and its prevention are the only way to prevent further damage to human vision. In this paper, we propose a new automated method for RNFLD detection in fundus images through patch features driven recurrent neural network (RNN). A new dataset of fundus images is created for evaluation purpose which contains several challenging RNFLD boundaries. The true boundary pixels are classified using the RNN trained by novel cumulative zero count local binary pattern (CZC-LBP), directional differential energy (DDE) patch features. The experimental results demonstrate high RNFLD detection rate along with accurate boundary localization. � 2017 IEEE.
Retinal verification using point set matching
(2015) Ekka B.K.; Puhan N.B.; Panda R.
In this paper, we propose a new retinal verification method based on point set matching. First, image processing techniques are applied to segment the retinal image's optic disc. The blood vessel map is generated inside the segmented optic disc region. We observe that optic disc region blood vessels are more stable and exhibit unique variation for a particular person. The edge map of the optic disc blood vessels is computed and used as the feature for similarity measurement. The partial Hausdorff measure is used to compute similarity measure between two edge based feature maps. Experimental results on a set of retinal images in the publicly available VARIA database show promising verification (FAR, FRR and EER) performance. � 2015 IEEE.
Robust and accurate optic disk localization using vessel symmetry line measure in fundus images
(2017) Panda R.; N.B. P.; Panda G.
Accurate optic disk (OD) localization is an important step in fundus image based computer-aided diagnosis of glaucoma and diabetic retinopathy. Robust OD localization becomes more challenging with the presence of common pathological variations which could alter its overall appearance. This paper presents a novel OD localization method by incorporating salient visual cues of retinal vasculature: (1) global vessel symmetry, (2) vessel component count and (3) local vessel symmetry inside OD region. In the proposed method, a new vessel symmetry line (VSL) measure is designed to demarcate the lines that divide the retinal vasculature into approximately similar halves. The initial OD center location is computed using the highest number of major blood vessel components in the skeleton image. The final OD center localization involves an iterative center of mass computation to exploit the local vessel symmetry in the OD region of interest. The proposed method shows effectiveness in diseased retinas having diverse symptoms like bright lesions, hemorrhages, and tortuous vessels that create potential ambiguity for OD localization. A total of ten publicly available retinal image databases are considered for extensive evaluation of the proposed method. The experimental results demonstrate high average OD detection accuracy of 99.49%, while achieving state-of-the-art OD localization error in all databases. � 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
The theoretical study of the correlation between band filling and Coulomb interaction in the charge gap of graphene-on-substrate in paramagnetic limit
(2019) Panda R.; Sahu S.; Rout G.C.
A suitable substrate breaks the sub-lattice symmetry leading to generation of a gap at the Fermi level. We propose here a tight binding model Hamiltonian for graphene on a substrate with nearest neighbour-hopping in presence of symmetry breaking interaction due to substrate effect. The sub-lattice Coulomb interaction between the electrons which produces varieties of magnetic, non-magnetic and collective interactions is considered within mean-field approximation in the paramagnetic limit. The Hamiltonian is solved by Zubarev�s double time Green�s function technique. The electron occupancies of the two sub-lattices are calculated from the correlation functions. Finally, the expression for the temperature dependent charge gap is derived and calculated numerically. The evolution of the charge gap in graphene is investigated by varying the Coulomb interaction, electron-occupancy and substrate induced gap. The magnitude of the electron occupancy at A-site becomes larger than that at B-site indicating symmetry breaking of the two sub-lattices of graphene. Copyright � 2019 Inderscience Enterprises Ltd.