Browsing by Author "Girish, G.N."

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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.
A cascaded convolutional neural network architecture for despeckling OCT images
(Elsevier Ltd, 2021) Anoop, B.N.; Kalmady, K.S.; Udathu, A.; Siddharth, V.; Girish, G.N.; Kothari, A.R.; Rajan, J.
Optical Coherence Tomography (OCT) is an imaging technique widely used for medical imaging. Noise in an OCT image generally degrades its quality, thereby obscuring clinical features and making the automated segmentation task suboptimal. Obtaining higher quality images requires sophisticated equipment and technology, available only in selected research settings, and is expensive to acquire. Developing effective denoising methods to improve the quality of the images acquired on systems currently in use has potential for vastly improving image quality and automated quantitative analysis. Noise characteristics in images acquired from machines of different makes and models may vary. Our experiments show that any single state-of-the-art method for noise reduction fails to perform equally well on images from various sources. Therefore, detailed analysis is required to determine the exact noise type in images acquired using different OCT machines. In this work we studied noise characteristics in the publicly available DUKE and OPTIMA datasets to build a more efficient model for noise reduction. These datasets have OCT images acquired using machines of different manufacturers. We further propose a patch-wise training methodology to build a system to effectively denoise OCT images. We have performed an extensive range of experiments to show that the proposed method performs superior to other state-of-the-art-methods. © 2021 Elsevier Ltd
A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images
(Institute of Electrical and Electronics Engineers Inc., 2023) Rahil, M.; Anoop, B.N.; Girish, G.N.; Kothari, A.R.; Koolagudi, S.G.; Rajan, J.
Retinal Fluids (fluid collections) develop because of the accumulation of fluid in the retina, which may be caused by several retinal disorders, and can lead to loss of vision. Optical coherence tomography (OCT) provides non-invasive cross-sectional images of the retina and enables the visualization of different retinal abnormalities. The identification and segmentation of retinal cysts from OCT scans is gaining immense attention since the manual analysis of OCT data is time consuming and requires an experienced ophthalmologist. Identification and categorization of the retinal cysts aids in establishing the pathophysiology of various retinal diseases, such as macular edema, diabetic macular edema, and age-related macular degeneration. Hence, an automatic algorithm for the segmentation and detection of retinal cysts would be of great value to the ophthalmologists. In this study, we have proposed a convolutional neural network-based deep ensemble architecture that can segment the three different types of retinal cysts from the retinal OCT images. The quantitative and qualitative performance of the model was evaluated using the publicly available RETOUCH challenge dataset. The proposed model outperformed the state-of-the-art methods, with an overall improvement of 1.8%. © 2013 IEEE.
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. Â© Springer Science+Business Media Singapore 2017.
Anomalous event detection methodologies for surveillance application: An insight
(IGI Global, 2018) Rao, T.J.N.; Girish, G.N.; Tahiliani, M.P.; Rajan, J.
Automatic visual surveillance systems serve as in-place threat detection devices being able to detect and recognize anomalous activities which otherwise would lead to potentially harmful situations, and alert the concerned authorities to take appropriate counter actions. However, development of an efficient visual surveillance system is quite challenging. Designing an unusual activity detection mechanism which is accurate and real-time is the primary challenge. Review of literature carried out led to the inference that there are some attributes which are essential for a successful unusual event detection mechanism for surveillance application. The desired approach must detect genuine anomalies in real-world scenarios with acceptable accuracy, should adapt to changing environments and, should require less computational time and memory. In this chapter, an attempt has been made to provide an insight into some of the prominent approaches employed by researchers to solve these issues with a hope that it will benefit researchers towards developing a better surveillance system. © 2019 by IGI Global.
Anomalous event detection methodologies for surveillance application: An insight
(IGI Global, 2017) Rao, T.J.N.; Tahiliani, M.P.; Girish, G.N.; Rajan, J.
Automatic visual surveillance systems serve as in-place threat detection devices being able to detect and recognize anomalous activities which otherwise would lead to potentially harmful situations, and alert the concerned authorities to take appropriate counter actions. However, development of an efficient visual surveillance system is quite challenging. Designing an unusual activity detection mechanism which is accurate and real-time is the primary challenge. Review of literature carried out led to the inference that there are some attributes which are essential for a successful unusual event detection mechanism for surveillance application. The desired approach must detect genuine anomalies in real-world scenarios with acceptable accuracy, should adapt to changing environments and, should require less computational time and memory. In this chapter, an attempt has been made to provide an insight into some of the prominent approaches employed by researchers to solve these issues with a hope that it will benefit researchers towards developing a better surveillance system. © 2018 IGI Global. All rights reserved.
Attention Assisted Patch-Wise CNN forÂ theÂ Segmentation ofÂ Fluids fromÂ theÂ Retinal Optical Coherence Tomography Images
(Springer Science and Business Media Deutschland GmbH, 2024) Anoop, B.N.; Parida, S.; Ajith, B.; Girish, G.N.; Kothari, A.R.; Kavitha, M.S.; Rajan, J.
Optical Coherence Tomography (OCT) is an important imaging modality in ophthalmology to visualize the abnormalities present in the retina. One of the major reasons for blindness is the accumulation of fluids in the various layers of the retina called retinal cysts. Accurate estimation of the type of cyst and its volume is important for effective treatment planning. In this paper, we propose attention assisted convolutional neural network-based architecture to detect and quantify three types of retinal cysts namely the intra-retinal cyst, sub-retinal cyst and pigmented epithelial detachment from the OCT images of the human retina. The proposed architecture has an encoder-decoder structure with an attention and a multi-scale module. The qualitative and quantitative performance of the model is evaluated on the publicly available RETOUCH retinal OCT fluid detection challenge data set. The proposed model outperforms the state-of-the-art methods in terms of precision, recall, and dice coefficient. Furthermore, the proposed model is computationally efficient due to its less number of model parameters. Â© Springer Nature Switzerland AG 2024.
Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform
(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.
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. Â© 2016 IEEE.
Deep Learning Based Sub-Retinal Fluid Segmentation in Central Serous Chorioretinopathy Optical Coherence Tomography Scans
(2019) Narendra, Rao, T.J.; Girish, G.N.; Kothari, A.R.; Rajan, J.
Development of an automated sub-retinal fluid segmentation technique from optical coherence tomography (OCT) scans is faced with challenges such as noise and motion artifacts present in OCT images, variation in size, shape and location of fluid pockets within the retina. The ability of a fully convolutional neural network to automatically learn significant low level features to differentiate subtle spatial variations makes it suitable for retinal fluid segmentation task. Hence, a fully convolutional neural network has been proposed in this work for the automatic segmentation of sub-retinal fluid in OCT scans of central serous chorioretinopathy (CSC) pathology. The proposed method has been evaluated on a dataset of 15 OCT volumes and an average Dice rate, Precision and Recall of 0.91, 0.93 and 0.89 respectively has been achieved over the test set. � 2019 IEEE.
Deep Learning Based Sub-Retinal Fluid Segmentation in Central Serous Chorioretinopathy Optical Coherence Tomography Scans
(Institute of Electrical and Electronics Engineers Inc., 2019) Narendra Rao, T.J.; Girish, G.N.; Kothari, A.R.; Rajan, J.
Development of an automated sub-retinal fluid segmentation technique from optical coherence tomography (OCT) scans is faced with challenges such as noise and motion artifacts present in OCT images, variation in size, shape and location of fluid pockets within the retina. The ability of a fully convolutional neural network to automatically learn significant low level features to differentiate subtle spatial variations makes it suitable for retinal fluid segmentation task. Hence, a fully convolutional neural network has been proposed in this work for the automatic segmentation of sub-retinal fluid in OCT scans of central serous chorioretinopathy (CSC) pathology. The proposed method has been evaluated on a dataset of 15 OCT volumes and an average Dice rate, Precision and Recall of 0.91, 0.93 and 0.89 respectively has been achieved over the test set. Â© 2019 IEEE.
Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation
(2019) Girish, G.N.; Saikumar, B.; Roychowdhury, S.; Kothari, A.R.; Rajan, J.
Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical coherence tomography (OCT) scans is a challenging task due to present of speckle noise and scan intensity variations across the vendors. This work proposes a convolutional neural network (CNN) model with an encoder-decoder pair architecture for IRC segmentation across different cross-vendor OCT scans. Since deep CNN models have high computational complexity due to a large number of parameters, the proposed method of depthwise separable convolutional filters aids model generalizability and prevents model over-fitting. Also, the swish activation function is employed to prevent the vanishing gradient problem. The optima cyst segmentation challenge (OCSC) dataset with four different vendor OCT device scans is used to evaluate the proposed model. Our model achieves a mean Dice score of 0.74 and mean recall/precision rate of 0.72/0.82 across different imaging vendors and it outperforms existing algorithms on the OCSC dataset. � 2019 IEEE.
Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation
(Institute of Electrical and Electronics Engineers Inc., 2019) Girish, G.N.; Saikumar, B.; Roychowdhury, S.; Kothari, A.R.; Rajan, J.
Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical coherence tomography (OCT) scans is a challenging task due to present of speckle noise and scan intensity variations across the vendors. This work proposes a convolutional neural network (CNN) model with an encoder-decoder pair architecture for IRC segmentation across different cross-vendor OCT scans. Since deep CNN models have high computational complexity due to a large number of parameters, the proposed method of depthwise separable convolutional filters aids model generalizability and prevents model over-fitting. Also, the swish activation function is employed to prevent the vanishing gradient problem. The optima cyst segmentation challenge (OCSC) dataset with four different vendor OCT device scans is used to evaluate the proposed model. Our model achieves a mean Dice score of 0.74 and mean recall/precision rate of 0.72/0.82 across different imaging vendors and it outperforms existing algorithms on the OCSC dataset. Â© 2019 IEEE.
Despeckling algorithms for optical coherence tomography images: A review
(IGI Global, 2019) Anoop, B.N.; Girish, G.N.; Sudeep, P.V.; Rajan, J.
Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology. The presence of speckle affects the quality of OCT images. Despeckling is necessary to improve its visual quality, and it is an integral part of software packages used for the computerized analysis of OCT. Even though a few methods for despeckling OCT are available in the literature, a cross-comparison of their performance is not known to be available. In this chapter, the techniques available in the literature for despeckling the OCT images have been identified. The results of the despeckling algorithms are compared both qualitatively and quantitatively by concerning the noise suppression capability and feature preservation. Among the available techniques, iterative adaptive unbiased (IAUB) filter is found to be superior as far as its performance regarding despeckling on retinal OCT images. © 2019, IGI Global.
An improved contextual information based approach for anomaly detection via adaptive inference for surveillance application
(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.
Marker controlled watershed transform for intra-retinal cysts segmentation from optical coherence tomography B-scans
(Elsevier B.V., 2020) Girish, G.N.; R Kothari, A.; Rajan, J.
Retinal cysts have pathological significance in several eye disorders. Detecting and quantifying such cysts from optical coherence tomography (OCT) scans is currently tedious and requires expertise. To aid the diagnostic process, an automatic intra-retinal cyst segmentation method using marker-controlled watershed transform on OCT B-scans is proposed in this paper. The proposed method is based on two stages – k-means clustering technique is used to identify cysts in the form of markers, followed by topographical based watershed transform for final segmentation. Qualitative and quantitative evaluation of proposed method was carried out against ground truth obtained from two graders on OPTIMA cyst segmentation challenge dataset. This method efficiently segments cystic structures with mean recall and precision rate 0.67 and 0.78, respectively, while preserving high correlation coefficient of 0.95 against ground truth obtained from both graders. Obtained results show that the proposed method outperformed other existing methods. © 2017 Elsevier B.V.
Retinal-Layer Segmentation Using Dilated Convolutions
(Springer Science and Business Media Deutschland GmbH, 2020) Guru Pradeep Reddy, T.; Ashritha, K.S.; Prajwala, T.M.; Girish, G.N.; Kothari, A.R.; Koolagudi, S.G.; Rajan, J.
Visualization and analysis of Spectral Domain Optical Coherence Tomography (SD-OCT) cross-sectional scans has gained a lot of importance in the diagnosis of several retinal abnormalities. Quantitative analytic techniques like retinal thickness and volumetric analysis are performed on cross-sectional images of the retina for early diagnosis and prognosis of retinal diseases. However, segmentation of retinal layers from OCT images is a complicated task on account of certain factors like speckle noise, low image contrast and low signal-to-noise ratio amongst many others. Owing to the importance of retinal layer segmentation in diagnosing ophthalmic diseases, manual segmentation techniques have been proposed and adopted in clinical practice. Nonetheless, manual segmentations suffer from erroneous boundary detection issues. This paper thus proposes a fully automated semantic segmentation technique that uses an encoderâ€“decoder architecture to accurately segment the prominent retinal layers. Â© 2020, Springer Nature Singapore Pte Ltd.
Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model
(Institute of Electrical and Electronics Engineers Inc., 2019) Girish, G.N.; Thakur, B.; Chowdhury, S.R.; Kothari, A.R.; Rajan, J.
Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Intra-retinal cysts (IRCs) occur in several macular disorders such as, diabetic macular edema, retinal vascular disorders, age-related macular degeneration, and inflammatory disorders. Automated segmentation of IRCs poses challenges owing to variations in the acquisition system scan intensities, speckle noise, and imaging artifacts. Several segmentation methods have been proposed in the literature for IRC segmentation on vendor-specific OCT images that lack generalizability across imaging systems. In this paper, we propose a fully convolutional network (FCN) model for vendor-independent IRC segmentation. The proposed method counteracts image noise variabilities and trains FCN models on OCT sub-images from the OPTIMA cyst segmentation challenge dataset (with four different vendor-specific images, namely, Cirrus, Nidek, Spectralis, and Topcon). Further, optimal data augmentation and model hyperparametrization are shown to prevent over-fitting for IRC area segmentation. The proposed method is evaluated on the test dataset with a recall/precision rate of 0.66/0.79 across imaging vendors. The Dice correlation coefficient of the proposed method outperforms that of the published algorithms in the OPTIMA cyst segmentation challenge with a Dice rate of 0.71 across the vendors. © 2013 IEEE.
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
Text-independent automatic accent identification system for Kannada language
(2017) Soorajkumar, R.; Girish, G.N.; Ramteke, P.B.; Joshi, S.S.; Koolagudi, S.G.
Accent identification is one of the applications paid more attention in speech processing.Atext-independent accent identification system is proposed using Gaussian mixturemodels (GMMs) for Kannada language. Spectral and prosodic features such as Mel-frequency cepstral coefficients (MFCCs), pitch, and energy are considered for the experimentation. The dataset is collected from three regions of Karnataka namely Mumbai Karnataka, Mysore Karnataka, and Karavali Karnataka having significant variations in accent. Experiments are conducted using 32 speech samples from each region where each clip is of one minute duration spoken by native speakers. The baseline system implemented using MFCC features found to achieve 76.7% accuracy. From the results it is observed that the hybrid features improve the performance of the system by 3 %. � Springer Science+Business Media Singapore 2017.