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Symmetry-Based Biomedical Image Compression

Dergi Makalesi

Kaynak :

Springer Link

Dergi:

Journal of Digital Imaging

Yazar(lar) :

V. K. Bairagi

Konu :

Computer Science Applications, Radiology Nuclear Medicine and imaging, Radiological and Ultrasound Technology

Yıl :

2015

V. K. Bairagi

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Kaydet

Image compression techniques aim at reducing the amount of data needed to accurately represent an image, such that the image can be economically transmitted or archived. This paper deals with employing symmetry as a parameter for compression of biomedical images. The approach presented in this paper offers great potential in complete lossless compression of the biomedical image under consideration, with the reconstructed image being mathematically identical to the original image. The method comprises getting rid of the redundant data and encoding the non-redundant data for the purpose of regenerating the image at the receiver section without any observable change in the image data.

DOI : 10.1007/s10278-015-9779-3

Anahtar Kelimeler : Symmetry, Biomedical image compression, Redundancy, Diagnostic ability

ISSN: 0897-1889 1618-727X
Sayı: 6
Cilt: 28
Sayfa: 718-726

Classification of Chronic Obstructive Pulmonary Disease (COPD) Using Electromyography

Dergi Makalesi

Kaynak :

Springer Link

Dergi:

Sādhanā

Yazar(lar) :

V K BAIRAGI , ARCHANA B KANWADE

Konu :

Multidisciplinary

Yıl :

2020

V K BAIRAGI

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Özet Ek Bilgiler

Kaydet

Chronic lung disease, in which the airway gets obstructed, is known as Chronic Obstructive Pulmonary Disease (COPD). According to WHO, COPD kills more than 3 million people every year. Spirometry is used to diagnose COPD; has many limitations. There is a need for physiologically accurate and easy to perform diagnosis technology. Researchers confirmed the activity of sternomastoid muscle in COPD with research limitations of; sample size, few time-domain features, lack of onset detection and the non-stationary nature of Electromyographical signals (EMG). In this, paper COPD diagnosis is made by analyzing Sternomastoid muscle of respiration in time, frequency and time-frequency domain. The slope-based onset detection algorithm and conduction velocity lead to an improvement in COPD detection accuracy to 98.61%. The feature selection algorithm is developed for the selection of the most significant features. A single frequency Continuous Wavelet Transform (CWT) analysis at 7, 8 and 10 Hz of frequency is used to extract features and to classify COPD in its grades, leading to the classification accuracy of 85.89%. Non-invasive, easy to use COPD diagnosis and classification technique is developed.

DOI : 10.1007/s12046-020-01336-y

Anahtar Kelimeler : Sternomastoid, surface electromyography, COPD, wavelet analysis

ISSN: 0256-2499 0973-7677
Sayı: 1
Cilt: 45

Extracting Salient Features for EEG-based Diagnosis of Alzheimer's Disease Using Support Vector Machine Classifier

Dergi Makalesi

Kaynak :

Taylor and Francis

Dergi:

IETE Journal of Research

Yayıncı:

Informa UK Limited

Yazar(lar) :

N. N. Kulkarni , V. K. Bairagi

Konu :

Theoretical Computer Science, Electrical and Electronic Engineering, Computer Science Applications

Yıl :

2016

V. K. Bairagi

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Kaydet

Alzheimer's disease (AD) is one of the most common and fastest growing neurodegenerative diseases in the western countries. Development of different biomarkers tools are key issues for the diagnosis of AD and its progression. Prediction of cognitive performance of subjects from electroencephalography (EEG) and identification of relevant biomarkers are some of the research problems. Although EEG is a powerful and relatively cheap tool for the diagnosis of AD and dementia, it does not achieve the standards of clinical performance in terms of sensitivity and specificity to accept as a reliable technique for the screening of AD. Hence, there is an immense need to develop an efficient system and algorithms for diagnosis. Accordingly, the objective of this research paper is to analyze different features for the diagnosis of AD to serve as a possible biomarker to distinguish between AD subject and normal subject. The research is carried out on an experimental database. Three different features such as spectral-, wavelet-, and complexity-based features are computed and compared on the basis of classification accuracy obtained. The classification is carried out using support vector machine classifier giving 96% accuracy on complexity-based features and increased performance in terms of sensitivity and specificity. The results show the improved performance in the diagnosis of AD. It is observed that the severity of AD is depicted in EEG complexity. These features used in research work can be considered as the benchmark for AD diagnosis.

DOI : 10.1080/03772063.2016.1241164

Anahtar Kelimeler : Alzheimer's disease, Electroencephalography, Preprocessing, Feature extraction, Support vector machine

ISSN: 0377-2063
Sayı: 1
Cilt: 63
Sayfa: 11-22

Automated malaria parasite and there stage detection in microscopic blood images

Konferans Bildirileri

Kaynak :

IEEE Xplore

Yazar(lar) :

Kshipra C. Charpe , V. K. Bairagi

Konu :

Aerospace, Bioengineering, Communication, Networking and Broadcast Technologies, Components, Circuits, Devices and Systems, Computing and Processing, Engineered Materials, Dielectrics and Plasmas, Fields, Waves and Electromagnetics, General Topics for Engineers, Geoscience, Photonics and Electrooptics, Power, Energy and Industry Applications, Robotics and Control Systems, Signal Processing and Analysis

Yıl :

2015

V. K. Bairagi

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Özet

Kaydet

Malaria is a dreadful disease in the hematological field causing millions of death. Hence the rapid diagnosing and proper medication is the intense need of era. The following work aims an automated system which will detect the malaria parasite along with its stage in which it is. Various Image processing techniques are used in this proposed method. The method proceeds in steps like image acquisition, segmentation, feature extraction and then classification. The features: color, shape, size, intensity, texture will be extracted and will be used for the segmentation and classification stage. In first level of classification, the presence of malaria parasite in the Red Blood Corpuscles will be checked and then the stage and type of parasite will be determined using multi-stage Support Vector Machine. The expected results will be more accurate (more than 95%).

DOI : 10.1109/isco.2015.7282336

Anahtar Kelimeler : Image segmentation, Feature extraction, Biomedical imaging, Indexes, Support vector machines, Transforms, Diseases, blood, diseases, feature extraction, image classification, image colour analysis, image segmentation, image texture, medical image processing, optical microscopy, support vector machines, automated malaria parasite, there-stage detection, microscopic blood images, disease, hematological field, image processing, image acquisition, image segmentation, feature extraction, image classification, red blood corpuscles, multistage support vector machine, color feature, image texture, Malaria Parasite, Statistical features, Annular Ring Ratio, SVM

Electroencephalogram based diagnosis of Alzheimer Disease

Konferans Bildirileri

Kaynak :

IEEE Xplore

Yazar(lar) :

Nilesh. N. Kulkarni , V. K. Bairagi

Konu :

Yıl :

2015

V. K. Bairagi

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Özet

Kaydet

Alzheimer Disease is one of the most common & tremendously growing neurological diseases in the world. Several biomarkers tools exist for diagnosis & disease progression in Alzheimer disease which can be assumed as key issues for clinical applications. Electroencephalogram signals (EEG) yields out powerful and relatively cheap tool of diagnosis of different neurological disease. The need in this case is to improve the diagnostic accuracy of the EEG signals. In this paper, a new methodology for diagnosis of Alzheimer disease using EEG signals is proposed; thereby increasing the diagnostic accuracy of the EEG signals & diagnosis. Several features of EEG Signals such as Spectral features, Coherence features & EEG Spectro-temporal modulation energy based features are discussed in detail. From the above discussed features available in the EEG signal, it is concluded that EEG can play an important role in diagnosis of Dementia & Alzheimer Disease.

DOI : 10.1109/isco.2015.7282275

Anahtar Kelimeler : Electroencephalography, Feature extraction, Dementia, Electrodes, Modulation, diseases, electroencephalography, medical disorders, medical signal processing, spectral analysis, electroencephalogram based diagnosis, Alzheimer disease, neurological disease, biomarkers tool, diagnosis progression, disease progression, electroencephalogram signal, diagnostic accuracy, EEG signal, EEG diagnosis, spectral feature, coherence feature, EEG spectro-temporal modulation energy based feature, dementia, Alzheimer Disease, Dementia, EEG, Neuroimaging, Spectro-temporal EEG Amplitude Modulation Energy, Spectral Power Based Features, Coherence based features

Lossless medical image compression by IWT and predictive coding

Konferans Bildirileri

Kaynak :

IEEE Xplore

Yazar(lar) :

T. G. Shirsat , V. K. Bairagi

Konu :

Power, Energy and Industry Applications, Geoscience

Yıl :

2013

V. K. Bairagi

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Özet

Kaydet

The future of healthcare delivery systems and telemedical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing and communication techniques. When dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis. E-health was born with the integration of networks and telecommunications. In recent years healthcare systems rely on images acquired in two dimensional domains in the case of still images, or three dimensional domains for volumetric video sequences and images. Images are acquired with many modalities including X-ray, magnetic resonance imaging, ultrasound, positron emission tomography, computed axial tomography. Medical information is either in multidimensional or multi-resolution form, this creates enormous amount of data. Retrieval, Efficient storage, management and transmission of this voluminous data are highly complex. One of the solutions to reduce this complex problem is to compress the medical data without any loss (i.e. lossless). Since the diagnostics capabilities are not compromised. This technique combines integer transforms and predictive coding to enhance the performance of lossless compression. The proposed techniques can be evaluated for performance using compression quality measures.

DOI : 10.1109/iceets.2013.6533571

Anahtar Kelimeler : Image coding, Predictive coding, Filter banks, Biomedical imaging, Wavelet transforms, data compression, image coding, image sequences, medical image processing, medical information systems, transform coding, wavelet transforms, lossless medical image compression, predictive coding, IWT, health care delivery systems, telemedical applications, medical sensors, wearable technology, mobile computing, communication techniques, remote medical collaborations, remote medical diagnosis, e-health, health care systems, volumetric video sequences, two dimensional domains, three dimensional domains, X-ray imaging, magnetic resonance imaging, positron emission tomography, ultrasound imaging, computed axial tomography, medical information, integer transform coding, integer wavelet transform, IWT, Image compression, Predictive coding

A no-reference image quality assessment

Konferans Bildirileri

Kaynak :

IEEE Xplore

Yazar(lar) :

A. K. Kemalkar , V. K. Bairagi

Konu :

Communication, Networking and Broadcast Technologies, Computing and Processing, Components, Circuits, Devices and Systems

Yıl :

2013

V. K. Bairagi

Yayıncı Web Sitesi

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Özet

Kaydet

This paper presents a no-reference image quality assessment, targeted towards blur distortions based on the study of human blur perception for varying contrast values. A probabilistic framework is developed based on the sensitivity of human blur perception at different contrasts. Utilizing this framework, the probability of detecting blur at each edge in an image is estimated. The blur perception information at each edge is then pooled over the entire image to obtain a final quality score by evaluating the cumulative probability of blur detection. Proposed metric is able to predict relative amount of blurriness in images. Higher metric value represent less blurred image. Results are provided to illustrate the performance of proposed metric. Performance of proposed metric is compared with existing no reference image quality metric for various publically available image databases.

DOI : 10.1109/ice-ccn.2013.6528543

Anahtar Kelimeler : Measurement, Image edge detection, Image coding, Image quality, Databases, Transform coding, Quality assessment, image restoration, probability, visual databases, visual perception, no-reference image quality assessment, blur distortions, varying contrast values, probabilistic framework, human blur perception sensitivity, blur perception information, cumulative probability, blur detection, no-reference image quality metric, image databases, Blur detection, human vision system, no-reference, objective, sharpness metric

The Role of Transforms in Image Compression

Dergi Makalesi

Kaynak :

Springer Link

Dergi:

Journal of The Institution of Engineers (India): Series B

Yazar(lar) :

V. K. Bairagi , A. M. Sapkal , M. S. Gaikwad

Konu :

Electrical and Electronic Engineering, General Computer Science

Yıl :

2013

V. K. Bairagi

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In today’s multimedia wireless communication, major issue is bandwidth needed to satisfy real time transmission of image data. Compression is one of the good solutions to address this issue. Transform based compression algorithms are widely used in the field of compression, because of their de-correlation and other properties, useful in compression. In this paper, comparative study of compression methods is done based on their types. This paper addresses the issue of importance of transform in image compression and selecting particular transform for image compression. A comparative study of performance of a variety of different image transforms is done base on compression ratio, entropy and time factor.

DOI : 10.1007/s40031-013-0049-9

Anahtar Kelimeler : Image transforms, Entropy, Wavelet transform, Lossless compression

ISSN: 2250-2106 2250-2114
Sayı: 2
Cilt: 94
Sayfa: 135-140

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