Dr. Jamshid Dehmeshki Profile

Dr. Jamshid Dehmeshki

at Kingston Univ

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Author

Publications (15)

Proceedings Article | 16 April 2014 Paper

Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

Maryam Hajiesmaeili, Jamshid Dehmeshki, Bashir Bagheri Nakhjavanlo, Tim Ellis

Proceedings Volume 9159, 91591D (2014) https://doi.org/10.1117/12.2064402

KEYWORDS: Image segmentation, Magnetic resonance imaging, Brain, 3D image processing, Neuroimaging, Head, Optical spheres, Tumors, Medical imaging, Fuzzy logic

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Proceedings Article | 9 March 2010 Paper

An adaptive 3D region growing algorithm to automatically segment and identify thoracic aorta and its centerline using computed tomography angiography scans

F. Ferreira, J. Dehmeshki, H. Amin, M. Dehkordi, A. Belli, A. Jouannic, S. Qanadli

Proceedings Volume 7624, 76242T (2010) https://doi.org/10.1117/12.844220

KEYWORDS: Image segmentation, Lung, Computed tomography, Angiography, Computer aided diagnosis and therapy, Image processing algorithms and systems, Arteries, Image processing, Tomography, Detector development

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Proceedings Article | 9 March 2010 Paper

Modeling uncertainty in classification design of a computer-aided detection system

Rahil Hosseini, Jamshid Dehmeshki, Sarah Barman, Mahdi Mazinani, Salah Qanadli

Proceedings Volume 7624, 76242V (2010) https://doi.org/10.1117/12.844178

KEYWORDS: Fuzzy logic, Computer aided design, Classification systems, Computer aided diagnosis and therapy, Fuzzy systems, CAD systems, Image segmentation, Lung, Computing systems, Solid modeling

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Proceedings Article | 12 May 2004 Paper

Multiresolution active contour model applied on lung and colon images

Jamshid Dehmeshki, Musib Siddique, Wing Wong, Irina Chis Ster

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535489

KEYWORDS: Lung, Computer programming, Colon, Wavelets, Computed tomography, Image resolution, Image segmentation, Wavelet transforms, Image processing, Medical imaging

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Proceedings Article | 12 May 2004 Paper

Automatic identification of colonic polyp in high-resolution CT images

Jamshid Dehmeshki, Hamdan Amin, Wing Wong, Mandana Ebadian Dehkordi, Nahid Kamangari, Mary Roddie, John Costello

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535339

KEYWORDS: Colon, Image segmentation, Fuzzy logic, Computed tomography, Colorectal cancer, Feature extraction, Cancer, Photonic integrated circuits, Virtual colonoscopy, Classification systems

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Proceedings Article | 4 May 2004 Paper

Computer-assisted detection (CAD) of pulmonary nodules on thoracic CT scans using image processing and classification techniques

Jamshid Dehmeshki, Manlio Valdivieso-Casique, Musib Siddique, Mandana Dehkordi, John Costello, Mary Roddie

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.535431

KEYWORDS: Computed tomography, CAD systems, Computer aided diagnosis and therapy, Lung, Image processing, Image classification, Scanners, Image analysis, Medical imaging, Feature extraction

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Proceedings Article | 7 September 1998 Paper

Automatic delineation and three-dimensional visualization of the lungs

Jamshid Dehmeshki, Fraser Hatfield

Proceedings Volume 3409, (1998) https://doi.org/10.1117/12.324127

KEYWORDS: Lung, Image segmentation, Expectation maximization algorithms, Computed tomography, Visualization, Tissues, Blood vessels, 3D visualizations, Tomography, Chest

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Proceedings Article | 7 September 1998 Paper

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

Fraser Hatfield, Jamshid Dehmeshki

Proceedings Volume 3409, (1998) https://doi.org/10.1117/12.324128

KEYWORDS: Image segmentation, Brain, Neural networks, Magnetic resonance imaging, Visualization, Tissues, 3D modeling, 3D visualizations, Head, Neuroimaging

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Proceedings Article | 30 October 1997 Paper

Stochastic approach to texture analysis using probabilistic neural networks and Markov random fields

Jamshid Dehmeshki, Mohammad Daemi, Fraser Hatfield, Mehdi Rashidi

Proceedings Volume 3164, (1997) https://doi.org/10.1117/12.292761

KEYWORDS: Stochastic processes, Neural networks, Autoregressive models, Feature extraction, Image classification, Evolutionary algorithms, Algorithms, Remote sensing, Image analysis, Medical imaging

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Proceedings Article | 30 October 1997 Paper

Automatic vision system for analysis of microscopic behavior of flow and transport in porous media

Mehdi Rashidi, Jamshid Dehmeshki, Eric Dickenson, Mohammad Daemi

Proceedings Volume 3164, (1997) https://doi.org/10.1117/12.292771

KEYWORDS: Velocity measurements, CCD cameras, Particles, Image processing, Chemical analysis, Microfluidics, Crystals, 3D image processing, Microfluidic imaging, Motion measurement

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Proceedings Article | 24 October 1997 Paper

Color image analysis of contaminants and bacteria transport in porous media

Mehdi Rashidi, Jamshid Dehmeshki, Mohammad Daemi, Larry Cole, Eric Dickenson

Proceedings Volume 3159, (1997) https://doi.org/10.1117/12.284200

KEYWORDS: Bacteria, Particles, Velocity measurements, Image analysis, Water, Imaging systems, Color imaging, Refractive index, Image filtering, Soil contamination

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Proceedings Article | 21 February 1996 Paper

Identification of quality of coal using an automated image analysis system

Jamshid Dehmeshki, Mohammad Daemi, N. Miles, B. Atkin

Proceedings Volume 2665, (1996) https://doi.org/10.1117/12.232247

KEYWORDS: Image segmentation, Pyrite, Image resolution, Image analysis, Polishing, Statistical analysis, Image processing, Minerals, Image processing algorithms and systems, Image quality

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This paper is concerned with development of an automated and efficient system for quality control of coal. This is achieved by distinguishing between different major maceral groups present in the polished coal blocks when viewed under a microscope. Coal utilization processes can be significantly affected by the distribution of macerals in the feed coal. Manual petrographic analysis of coal requires a highly skilled operator and the results obtained can have a high degree of subjectivity. One way of overcoming these problems is to employ automated image analysis. The system described here consists of two stages: segmentation and interpretation. In the segmentation stage, the aim is to partition the images into different types of macerals. We have implemented a multi-scale segmentation technique in which the result of each process at a given resolution is used to adjust the other process at the next resolution. This approach combines a suitable statistical model for distribution of pixel values within each macerals group and a transition distribution from coarse to fine scale, based on a son-father relationship, which is defined between the nodes in adjacent levels. At each level, segmentation is performed by maximizing the a posteriori probability (MAP) which is achieved by a relaxation algorithm, similar to Besegs work. There are two major reasons for carrying out the segmentation estimation over a hierarchy of resolutions: to speed up the estimation process, and to incorporate large scale characteristics of each pixel. The speed can be further improved by restricting the operation on the pixels which are introduced as mixed in each resolution, by which the number of pixels to be considered are significantly reduced. In the interpretation stage, the coal macerals are identified according to the measurement information on the segmented region and domain knowledge. The paper describes the knowledge base used in this application in some detail. The system has been particularly successful in correctly classifying difficult cases, such as liptinite, vitrinite, semi-fusinite and pyrite.

Proceedings Article | 17 November 1995 Paper

Adaptive segmentation of remotely sensed images based on stochastic models

Jamshid Dehmeshki, Mohammad Daemi, R. Marston, Paul Mather, Zhen Sun

Proceedings Volume 2587, (1995) https://doi.org/10.1117/12.226820

KEYWORDS: Image segmentation, Expectation maximization algorithms, Scanning electron microscopy, Stochastic processes, Statistical analysis, Image classification, Image processing algorithms and systems, Satellites, Image resolution, Earth observing sensors

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Proceedings Article | 3 October 1995 Paper

Determination of major maceral groups in coal by automated image analysis procedures

Jamshid Dehmeshki, Mohammad Daemi, N. Miles, B. Atkin, R. Marston

Proceedings Volume 2588, (1995) https://doi.org/10.1117/12.222724

KEYWORDS: Image segmentation, Expectation maximization algorithms, Scanning electron microscopy, Pyrite, Stochastic processes, Image analysis, Image processing, Image processing algorithms and systems, Polishing, Minerals

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This paper describes development of an automated and efficient system for classifying of different major maceral groups within polished coal blocks. Coal utilization processes can be significantly affected by the distribution of macerals in the feed coal. In carbonization, for example, maceral group analysis is an important parameter in determining the correct coal blend to produce the required coking properties. In coal liquefaction, liptinites and vitrinites convert more easily to give useful products than inertinites. Microscopic images of coal are inherently difficult to interpret by conventional image processing techniques since certain macerals show similar visual characteristics. It is particularly difficult to distinguish between the liptinite maceral and the supporting setting resin. This requires the use of high level image processing as well as fluorescence microscopy in conjunction with normal white light microscopy. This paper is concerned with two main stages of the work, namely segmentation and interpretation. In the segmentation stage, a cooperative, iterative approach to segmentation and model parameter estimation is defined which is a stochastic variant of the Expectation Maximization algorithm. Because of the high resolution of these images under study, the pixel size is significantly smaller than the size of most of the different regions of interest. Consequently adjacent pixels are likely to have similar labels. In our Stochastic Expectation Maximization method the idea that neighboring pixels are similar to one another is expressed by using Gibbs distribution for the priori distribution of regions (labels). We also present a suitable statistical model for distribution of pixel values within each region. In the interpretation stage, the coal macerals are identified according to the measurement information on the segmented region and domain knowledge. Studies show that the system is able to distinguish coals macerals, especially Fusinite from Pyrite or liptinite from mineral which previous attempts have been unable to resolve.

Proceedings Article | 22 August 1995 Paper

Automatic MRI compression and segmentation using a stochastic model

Jamshid Dehmeshki, Orlean Cole, R. Marston, Mohammad Daemi, R. Coxon

Proceedings Volume 2564, (1995) https://doi.org/10.1117/12.217437

KEYWORDS: Image segmentation, Principal component analysis, Magnetic resonance imaging, Stochastic processes, Magnetism, Fuzzy logic, Visualization, Data compression, Data modeling, Signal to noise ratio

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Showing 5 of 15 publications

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