Browsing by Author "Aparna, P."

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Adaptive local cosine transform for seismic image compression
(2006) Aparna, P.; Sumam, David S.
A typical seismic analysis involves collection of data by an array of seismometers, transmission over a narrow-band channel, and storage of data for analysis. Transmission and archiving of large volumes of data involves great cost. Hence there is a need to devise suitable methods for compressing the seismic data without compromising on the quality of the reconstructed signal. This paper presents our work on the seismic data compression based on adaptive local cosine transform and its associated multi-resolution and best-basis methodology and compares the results with wavelet based implementation. � 2006 IEEE.
Analysis of real-time tracking filters implementation in FPGA
(2019) Haritha, G.; Aparna, P.; Srihari, P.; Satapathi, G.S.
This paper presents real time tracking hardware based on field programmable gate arrays (FPGA). Traditionally, tracking system is simulated in MATLAB environment and an equivalent hardware description language (HDL) code is written for FPGA. But, in this paper a simulated and verified MATLAB code is directly converted into HDL which is used in FPGA. This method improves the efficiency of the real time tracker and flexibility in usage of FPGA. Moreover, the simulated and hardware outputs will be consistent. Three types tracking algorithm namely Kalman filter (KF), extended Kalman filter (EKF), and ??? filter are implemented to validate the proposed approach. � 2018 IEEE.
Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach
(2019) Srinidhi, C.L.; Aparna, P.; Rajan, J.
Separation of the vascular tree into arteries and veins is a fundamental prerequisite in the automatic diagnosis of retinal biomarkers associated with systemic and neurodegenerative diseases. In this paper, we present a novel graph search metaheuristic approach for automatic separation of arteries/veins (A/V) from color fundus images. Our method exploits local information to disentangle the complex vascular tree into multiple subtrees, and global information to label these vessel subtrees into arteries and veins. Given a binary vessel map, a graph representation of the vascular network is constructed representing the topological and spatial connectivity of the vascular structures. Based on the anatomical uniqueness at vessel crossing and branching points, the vascular tree is split into multiple subtrees containing arteries and veins. Finally, the identified vessel subtrees are labeled with A/V based on a set of hand-crafted features trained with random forest classifier. The proposed method has been tested on four different publicly available retinal datasets with an average accuracy of 94.7%, 93.2%, 96.8%, and 90.2% across AV-DRIVE, CT-DRIVE, INSPIRE-AVR, and WIDE datasets, respectively. These results demonstrate the superiority of our proposed approach in outperforming the state-of-The-Art methods for A/V separation. 1992-2012 IEEE.
A comprehensive solution to road traffic accident detection and ambulance management
(2017) Hari, Sankar, S.; Jayadev, K.; Suraj, B.; Aparna, P.
Delay in providing Emergency Medical Services (EMS) is the cause of the high mortality rate in road traffic accidents in countries like India. There is delay involved in each and every stage of the process, right from reporting an accident to dispatching an ambulance, till the patient is safely handed over to the casualty. Minimizing this delay can help save lives. We propose a comprehensive solution to both accident detection and ambulance management. When the in-vehicle accident detection module reports an accident, the main server automatically dispatches the nearest ambulance to the accident spot. The android application used by the ambulance driver assists the driver to reach the location quickly and safely. Automation of accident detection and ambulance dispatch, along with providing guidance to the ambulance driver, is achieved here. This can save precious time and help standardize the whole process. � 2016 IEEE.
An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications
(2014) Parameshwari, D.S.; Aparna, P.
In this paper, we propose an efficient textural feature extraction algorithm (TFEA) based on higher order statistical cumulant namely Kurtosis for a class of brain MR imaging applications. Using a model that represents the wavelet coefficient energies of the sub-bands of multi-level decomposition of the image as a basis, a feature set involving three parameters for each band corresponding to probability density function (PDF) of generalized Gaussian type is derived. The logical correctness and working of the proposed TFEA are first verified based on MATLAB ver.2010a tool. The algorithm is applied in conjunction with one of the popularly used canny edge detection algorithm for segmenting a class of real and synthetic magnetic resonance (MR) images to detect the region of a tumor if present. The use of the proposed approach results in reduced feature set size thus obviating the need for employing specialized feature selection/ reduction algorithms. A detailed look at the experimental results clearly show an improvement in the segmentation quality compared with conventional method. � 2014 IEEE.
Efficient Distributed Video Coding based on principle of syndrome coding
(2011) Aparna, P.; Sumam, David S.
Distributed Video Coding (DVC) is a new video coding paradigm, the main objective of which is to reduce the encoder complexity to support a separate class of uplink-friendly applications like wireless video applications, besides achieving the rate-distortion performance of conventional video coders. In this paper, we describe and present the simulation results of the video coding method based on the principle of distributed source coding using Golay codes and then propose an improvement to it. In this, the side information is improved by performing a very coarse motion search at the encoder and transmitting the position of the side information block as the hash information to the decoder, which will help the decoder to perform motion estimation. Copyright 2011 Inderscience Enterprises Ltd.
An efficient framework for segmentation and identification of tumours in brain MR images
(2016) Parameshwari, D.S.; Aparna, P.
In this research work, two efficient textural feature extraction (TFE) algorithms (TFEA-I and TFEA-II) are proposed for a class of brain magnetic resonance imaging (MRI) applications. TFEA-I employs higher order statistical cumulant, namely, Kurtosis in order to generate a feature set based on the probability density function (PDF) of generalised Gaussian model that represents thewavelet coefficient energies of the sub-bands of decomposed image. TFEA-II derives a feature set employing cooccurrence matrix model for second order statistical characterisation of wavelet coefficients. In conjunction with TFEA-I and TFEA-II, we propose segmentation framework to compute coarse and smooth segmented boundaries for the tumour. When compared with the conventional TFEA methods reported in the literature, the use of proposed TFEA-I and TFEA-II results in two important advantages; considerable reduction in the feature set size and elimination of the need for using specialised feature selection/reduction algorithms thereby making them highly attractive for a class of brain MR imaging application. Copyright 2016 Inderscience Enterprises Ltd.
Fast response search and rescue robot, assisted low power WSN net for navigation and detection
(2013) Kumar, S.; Reddy, V.; Prakash, P.; Aparna, P.
Our project is a remotely controlled robot capable of surveying disaster situations or act as a security countermeasure. It utilizes a stationary network of passive infrared sensor nodes interconnected through a multi-hop Zig bee network. The sensors are motion sensitive and using regional localization can be used for identifying the location of survivors or intruders based on the situation. The robot is controlled via a Wi-fi link which streams real time video back to the base station. The main processor is a TI Sitara AM335x ARM processor. It also acts as relay for the sensor data. Each node consists of three passive infrared detection circuits each covering a sector of 120 degrees and connects via the TI CC2530 ZNP chip. The raw PIR data is signal conditioned using an LM324 Op-amp. The nodes can be deployed easily due to their compact size. Their low power consumption and low cost makes them ideal for remote areas and can be deployed in large numbers. � 2013 IEEE.
A feasible QRS detection algorithm for arrhythmia diagnosis
(2017) Khadirnaikar, S.; Aparna, P.
This paper presents a reliable QRS detection algorithm to detect and classify Electrocardiogram (ECG) waveform abnormalities by extracting features such as heart rate and duration of QRS complex. As R peak detection is the pivotal step in automatic electrocardiogram analysis, various mathematical operations like clipping, differentiation and squaring are carried out in the preprocessing stage to enhance the section containing the QRS complex. Thresholding is performed to detect the R peaks. In order to improve the accuracy of the algorithm search back technique is implemented to determine the missing R peaks and heart beat. Once the position of R peak is detected, positions of Q and S are determined using two different search intervals to take into account the anomalous conditions as well. The heart rate and QRS width are then computed and compared with the normal values to determine the degree and type of abnormality. MIT-BIH database is used to evaluate this algorithm. The algorithm gives sensitivity of 99.34% and positive predictivity of 96.79%. In the proposed algorithm complicated mathematical operations like Fourier Transform, Hilbert Transform or crosscorrelation are not computed, hence is convenient to realize. � 2016 IEEE.
Gradient-oriented directional predictor for HEVC planar and angular intra prediction modes to enhance lossless compression
(2018) Shilpa, Kamath, S.; Aparna, P.; Antony, A.
Recent advancements in the capture and display technologies motivated the ITU-T Video Coding Experts Group and ISO/IEC Moving Picture Experts Group to jointly develop the High-Efficiency Video Coding (HEVC), a state-of-the-art video coding standard for efficient compression. The compression applications that essentially require lossless compression scenarios include medical imaging, video analytics, video surveillance, video streaming etc., where the content reconstruction should be flawless. In the proposed work, we present a gradient-oriented directional prediction (GDP) strategy at the pixel level to enhance the compression efficiency of the conventional block-based planar and angular intra prediction in the HEVC lossless mode. The detailed experimental analysis demonstrates that the proposed method outperforms the lossless mode of HEVC anchor in terms of bit-rate savings by 8.29%, 1.65%, 1.94% and 2.21% for Main-AI, LD, LDP and RA configurations respectively, without impairing the computational complexity. The experimental results also illustrates that the proposed predictor performs superior to the existing state-of-the-art techniques in the literature. 2018 Elsevier GmbH
Irreversible wavelet compression of radiological images based on visual threshold
(2016) Chandrika, B.K.; Aparna, P.; Sumam, David S.
Visually lossless irreversible coding permit compression algorithms to improve the compression gain without disturbing the visual image quality. This paper proposes a novel coding scheme in which wavelet based visual model is embedded into lossless compression algorithm to compress the volumetric medical image data. Obtained experimental results are compared with numerically lossless compression schemes such as Differential Pulse Code Modulation (DPCM), Joint Photographic Experts Group-Lossless (JPEG-LS), JPEG-2000 and High Efficiency Video Coding (HEVC). The proposed method achieves reduced bit rate without deteriorating the visual quality of the resulting images. � 2015 IEEE.
Multilevel coset coding of video with Golay codes
(2011) Aparna, P.; Sumam, David S.
This paper presents a video coding method based on the principle of distributed source coding. This work aims in shifting the encoder complexity to the decoder to support uplink friendly video applications, simultaneously achieving the rate-distortion performance of the conventional predictive coding system. In this work concept of syndrome coding with Golay codes is adopted for compression. The simulation results presented in this paper reveals the superior performance of this distributed video coder over the Intraframe coders and predictive coders for video data with less correlation between frames. � 2011 IEEE.
A nearest neighbor based approach for classifying epileptiform EEG using nonlinear DWT features
(2012) Holla, A.V.R.; Aparna, P.
Epilepsy is a pathological condition characterized by spontaneous, unforeseeable occurrence of seizures, during which the perception or behaviour of a person is altered, if not disturbed. In prediction of occurance of seizures, better classification accuracies have been reported with the use of non linear features and hence they have been estimated from wavelet transformed Electro Encephalo Graph (EEG) data and used to train k Nearest Neighbour (kNN) classifier to classify the EEG into normal, background and epileptic classes. Very good accuracy performance of nearly 100% has been reported from the current work. � 2012 IEEE.
Perceptually lossless coder for volumetric medical image data
(2017) Chandrika, B.K.; Aparna, P.; Sumam, David S.
With the development of modern imaging techniques, every medical examination would result in a huge volume of image data. Analysis, storage and/or transmission of these data demands high compression without any loss of diagnostically significant data. Although, various 3-D compression techniques have been proposed, they have not been able to meet the current requirements. This paper proposes a novel method to compress 3-D medical images based on human vision model to remove visually insignificant information. The block matching algorithm applied to exploit the anatomical symmetry remove the spatial redundancies. The results obtained are compared with those of lossless compression techniques. The results show better compression without any degradation in visual quality. The rate-distortion performance of the proposed coders is compared with that of the state-of-the-art lossy coders. The subjective evaluation performed by the medical experts confirms that the visual quality of the reconstructed image is excellent. 2017
Performance enhancement of HEVC lossless mode using context-based angular and planar intra predictions
(2020) Sowmya, Kamath S.; Aparna, P.; Antony, A.
Lossless mode of High-Efficiency Video Coding (HEVC), the state-of-the-art video coding standard, can be used for distortion-free reconstruction of the input data for a wide variety of applications. HEVC relies on the usage of efficient intra prediction strategies to achieve superior compression than its predecessor H.264. A large amount of spatial redundancy exists in almost all video sequences due to coherence, smoothness and the inherent correlation within the neighboring pixels. In this paper, a context-based intra prediction scheme is proposed to minimize this local redundancy by identifying the edges and textures to appropriately modify the prediction strategy at the pixel level, without further increase in the computational complexity. The variability in the sum of absolute differences and local pixel intensity values are chosen to derive the context of the nearby region around the target pixel in the planar and angular intra prediction modes respectively. The experimental results validate the superiority of the proposed method over the HEVC anchor and other state-of-the-art techniques in the literature. 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Pixelwise improvised blend of predictors in HEVC lossless mode
(2020) Shilpa, Kamath, S.; Aparna, P.; Antony, A.
The commendable work by the two video coding pioneers ISO/IEC and ITU-T, to handle the next-generation of multimedia services has led to the evolution of High Efficiency Video Coding (HEVC) standard. The lossless mode of HEVC is essential when no loss in fidelity is desired to aide most of the real-world applications like video analytics, web collaboration, remote desktop sharing, etc. The proposed work intends to improvise the HEVC intra prediction scheme through the application of the heuristic history-based blend of predefined sub-predictors, while in lossless mode. The prime element of the locally adaptive mechanism is the derivation of the penalizing factors that are imposed on the sub-predictors, based on the neighborhood residuals. The experimental analysis highlights that the proposed method outperforms the lossless mode of HEVC anchor and the prevalent state-of-the-art prediction techniques in terms of savings in bit-rate which is achieved without any increase in run-time. 2019 Elsevier GmbH
Recent Advancements in Retinal Vessel Segmentation
(2017) L, Srinidhi, C.; Aparna, P.; Rajan, J.
Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system. 2017, Springer Science+Business Media New York.
Sample-based DC prediction strategy for HEVC lossless intra prediction mode
(2018) Sowmya, Kamath S.; Aparna, P.; Antony, A.
High-Efficiency Video Coding (HEVC), the state-of-the-art video coding standard by the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group, is presently being prepared to handle the next generation multi-media services. Lossless mode of HEVC is designed to support a variety of lossless compression applications like medical imaging, preservation of artwork, video analytics, etc. The accuracy of the intra prediction can be improved through the incorporation of sample-based prediction strategies which replace the block-based prediction within HEVC. In this work, we propose a sample-based DC intra prediction strategy to enhance the compression efficiency of the HEVC lossless mode. The detailed experimental analysis demonstrates that the proposed method outperforms the HEVC lossless mode of HM16.12 in terms of bit-rate savings by 1.43% and 0.46% on an average for AI-Main and AI-Main10 configurations respectively, without any increase in run-time. � 2017 IEEE.
Symmetry based perceptually lossless compression of 3D medical images in spatial domain
(2014) Chandrika, B.K.; Aparna, P.; Sumam, David S.
A perceptual model developed in spatial domain based on background luminance is combined with lossless compression frame work to remove visually redundant information. Block matching is applied on anatomical symmetry present in medical images to remove intra slice and inter slice correlations. The obtained results show better compression gains against lossless compression techniques, without any degradation in visual quality. � 2014 IEEE.
A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images
(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 Ltd