Skip to main content

Victor Murray

University of New Mexico, College of Engineering - Electrical and Computer Engineering, Faculty Member

Followers

14

Following

4

Co-authors

4

Public Views

Christos C Kannas

University of Cyprus

Marios S Pattichis

University of New Mexico

Christos P Loizou

Cyprus University of Technology

Efthyvoulos Kyriacou

Cyprus University of Technology

Efthyvoulos Kyriacou

Cyprus University of Technology

Styliani Petroudi

Claudio Gasperini

Interests

Uploads

Papers by Victor Murray

AM-FM texture image analysis in multiple sclerosis brain white matter lesions

by Christos P Loizou, Marios S Pattichis, Victor Murray, and Pantzaris Marios

XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, 2010

In this study we investigate the use of multiscale Amplitude Modulation-Frequency Modulation (AM-... more In this study we investigate the use of multiscale Amplitude Modulation-Frequency Modulation (AM-FM) methods for analyzing brain white matter lesions that are associated with multiple sclerosis MRI lesions imaged at 0 and 6-12 months. We use the instantaneous amplitude (IA) and the instantaneous frequency (IF) to assess disease progression. The IA and the IF were calculated in transverse sections of T2-weighted magnetic resonance (MR) images acquired from 38 symptomatic untreated subjects between the first and the second ...

Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection

by Stephen Russell, Marios S Pattichis, and Victor Murray

IEEE Transactions on Medical Imaging, 2010

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM)... more In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 ?? 40 pixels containing four types of lesions commonly associated with diabetic retinopathy (DR) and two types of normal retinal regions that were manually selected by a trained analyst. The region types included microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns. The cumulative distribution functions of the instantaneous amplitude, the instantaneous frequency magnitude, and the relative instantaneous frequency angle from multiple scales are used as texture feature vectors. We use distance metrics between the extracted feature vectors to measure interstructure similarity. Our results demonstrate a statistical differentiation of normal retinal structures and pathological lesions based on AM-FM features. We further demonstrate our AM-FM methodology by applying it to classification of retinal images from the MESSIDOR database. Overall, the proposed methodology shows significant capability for use in automatic DR screening.

Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy

Image Processing, IEEE …, Jan 1, 2010

AM-FM representations for the characterization of carotid plaque ultrasound images

… Conference of the …, Jan 1, 2009

J. Vander Sloten, P. Verdonck, M. Nyssen, J. Haueisen (Eds.): ECIFMBE 2008, IFMBE Proceedings 22,... more

Robust multiscale AM-FM demodulation of digital images

Image Processing, 2007. …, Jan 1, 2007

New AM-FM analysis methods for retinal image characterization

Signals, Systems and …, Jan 1, 2008

Multiscale AM-FM analysis of pneumoconiosis x-ray images

… (ICIP), 2009 16th …, Jan 1, 2009

Multiscale am-fm methods for diabetic retinopathy lesion detection

Medical Imaging, …, Jan 1, 2010

AM-FM demodulation methods for reconstruction, analysis and motion estimation in video signals

Retrieval and classification of pneumoconiosis chest radiograph images using multiscale AM-FM methods

Signals, Systems and …, Jan 1, 2009

AM-FM texture image analysis of the intima and media layers of the carotid artery

by Victor Murray and Christos P Loizou

… NetworksICANN 2009, Jan 1, 2009

Classification of surface electromyographic signals using AM-FM features

… and Applications in …

In order to effectively control a prosthetic system, considerable attempts have been made in rece... more In order to effectively control a prosthetic system, considerable attempts have been made in recent years to improve the classification accuracy of surface electromyographic (SEMG) signals. However, the extraction of effective features is still a primary challenge for the classification of SEMG signals. This study tried to solve the problem by applying the multifractal analysis. It was found that the SEMG signals were characterized by multifractality during forearm movements and different types of forearm movements were related to different multifractal singularity spectra. To quantitatively evaluate the multifractal singularity spectra of the SEMG signals, the areas of the singularity spectrum curves were calculated by integrating the spectrum curves with respect to the singularity strengths. Our results showed that there were several separate clusters resulting from singularity spectrum areas of different forearm movements when two channels of SEMG signals were used in this experimental research, which demonstrated that the multifractal analysis approach was suitable for identifying different types of forearm movements. By comparing with other feature extraction techniques, the multifractal singularity spectrum approach provided higher classification accuracy in terms of the classification of SEMG signals.

Survey of AM-FM methods for Applications in Medical Imaging

visionquest-bio.com

Automatic system for diabetic retinopathy screening based on AM-FM, partial least squares, and support vector machines

… : From Nano to …, Jan 1, 2010

Diabetic retinopathy (DR) is a disease that affects over 170 million people worldwide. In the Uni... more

Controlador de un monitor VGA con resolución 640x480 sobre un CPLD FLEX 10K

Anales del VIII Workshop IBERCHIP, México, Jan 1, 2002

AM-FM texture image analysis in multiple sclerosis brain white matter lesions

by Christos P Loizou, Marios S Pattichis, Victor Murray, and Pantzaris Marios

XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, 2010

In this study we investigate the use of multiscale Amplitude Modulation-Frequency Modulation (AM-... more In this study we investigate the use of multiscale Amplitude Modulation-Frequency Modulation (AM-FM) methods for analyzing brain white matter lesions that are associated with multiple sclerosis MRI lesions imaged at 0 and 6-12 months. We use the instantaneous amplitude (IA) and the instantaneous frequency (IF) to assess disease progression. The IA and the IF were calculated in transverse sections of T2-weighted magnetic resonance (MR) images acquired from 38 symptomatic untreated subjects between the first and the second ...

Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection

by Stephen Russell, Marios S Pattichis, and Victor Murray

IEEE Transactions on Medical Imaging, 2010

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM)... more In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 ?? 40 pixels containing four types of lesions commonly associated with diabetic retinopathy (DR) and two types of normal retinal regions that were manually selected by a trained analyst. The region types included microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns. The cumulative distribution functions of the instantaneous amplitude, the instantaneous frequency magnitude, and the relative instantaneous frequency angle from multiple scales are used as texture feature vectors. We use distance metrics between the extracted feature vectors to measure interstructure similarity. Our results demonstrate a statistical differentiation of normal retinal structures and pathological lesions based on AM-FM features. We further demonstrate our AM-FM methodology by applying it to classification of retinal images from the MESSIDOR database. Overall, the proposed methodology shows significant capability for use in automatic DR screening.

Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy

Image Processing, IEEE …, Jan 1, 2010

AM-FM representations for the characterization of carotid plaque ultrasound images

… Conference of the …, Jan 1, 2009

J. Vander Sloten, P. Verdonck, M. Nyssen, J. Haueisen (Eds.): ECIFMBE 2008, IFMBE Proceedings 22,... more

Robust multiscale AM-FM demodulation of digital images

Image Processing, 2007. …, Jan 1, 2007

New AM-FM analysis methods for retinal image characterization

Signals, Systems and …, Jan 1, 2008

Multiscale AM-FM analysis of pneumoconiosis x-ray images

… (ICIP), 2009 16th …, Jan 1, 2009

Multiscale am-fm methods for diabetic retinopathy lesion detection

Medical Imaging, …, Jan 1, 2010

AM-FM demodulation methods for reconstruction, analysis and motion estimation in video signals

Retrieval and classification of pneumoconiosis chest radiograph images using multiscale AM-FM methods

Signals, Systems and …, Jan 1, 2009

AM-FM texture image analysis of the intima and media layers of the carotid artery

by Victor Murray and Christos P Loizou

… NetworksICANN 2009, Jan 1, 2009

Classification of surface electromyographic signals using AM-FM features

… and Applications in …

In order to effectively control a prosthetic system, considerable attempts have been made in rece... more In order to effectively control a prosthetic system, considerable attempts have been made in recent years to improve the classification accuracy of surface electromyographic (SEMG) signals. However, the extraction of effective features is still a primary challenge for the classification of SEMG signals. This study tried to solve the problem by applying the multifractal analysis. It was found that the SEMG signals were characterized by multifractality during forearm movements and different types of forearm movements were related to different multifractal singularity spectra. To quantitatively evaluate the multifractal singularity spectra of the SEMG signals, the areas of the singularity spectrum curves were calculated by integrating the spectrum curves with respect to the singularity strengths. Our results showed that there were several separate clusters resulting from singularity spectrum areas of different forearm movements when two channels of SEMG signals were used in this experimental research, which demonstrated that the multifractal analysis approach was suitable for identifying different types of forearm movements. By comparing with other feature extraction techniques, the multifractal singularity spectrum approach provided higher classification accuracy in terms of the classification of SEMG signals.

Survey of AM-FM methods for Applications in Medical Imaging

visionquest-bio.com

Automatic system for diabetic retinopathy screening based on AM-FM, partial least squares, and support vector machines

… : From Nano to …, Jan 1, 2010

Diabetic retinopathy (DR) is a disease that affects over 170 million people worldwide. In the Uni... more

Controlador de un monitor VGA con resolución 640x480 sobre un CPLD FLEX 10K

Anales del VIII Workshop IBERCHIP, México, Jan 1, 2002