Dr. Alok Sarwal Profile

Dr. Alok Sarwal

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Publications (9)

Proceedings Article | 27 May 2005 Paper

Terrain classification for a UGV

Alok Sarwal, Chris Baker, Mark Rosenblum

Proceedings Volume 5804, (2005) https://doi.org/10.1117/12.603970

KEYWORDS: Image segmentation, Sensors, Data acquisition, Cameras, Human-machine interfaces, Image classification, LIDAR, RGB color model, Computing systems, Light sources and illumination

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Proceedings Article | 19 May 2005 Paper

Head-worn display-based augmented reality system for manufacturing

Alok Sarwal, Chris Baker, Dragan Filipovic

Proceedings Volume 5800, (2005) https://doi.org/10.1117/12.603142

KEYWORDS: Cameras, Head-mounted displays, Eye, Calibration, Manufacturing, Head, Augmented reality, Visualization, 3D metrology, Heads up displays

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Proceedings Article | 11 November 2004 Paper

System for intelligent machine fault analysis

Alok Sarwal, Dragan Filipovic

Proceedings Volume 5605, (2004) https://doi.org/10.1117/12.580746

KEYWORDS: Cameras, Data acquisition, Intelligence systems, Manufacturing, Analog electronics, Human-machine interfaces, Imaging systems, Optics manufacturing, Signal processing, Photonic integrated circuits

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Proceedings Article | 30 September 2003 Paper

Terrain classification

Alok Sarwal, David Simon, Venkat Rajagopalan

Proceedings Volume 5083, (2003) https://doi.org/10.1117/12.487150

KEYWORDS: LIDAR, Sensors, Polarization, Classification systems, Optical filters, Humidity, Data modeling, Cameras, Image classification, Forward looking infrared

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Proceedings Article | 6 November 1996 Paper

Multilevel lines of communication extraction

Aleksandar Zavaljevski, Atam Dhawan, Alok Sarwal, David Kelch, James Riddell

Proceedings Volume 2821, (1996) https://doi.org/10.1117/12.257173

KEYWORDS: Lab on a chip, Image segmentation, Roads, Image processing, Sensors, Composites, Multispectral imaging, Data modeling, Telecommunications, Spectral models

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Proceedings Article | 12 May 1995 Paper

Three-dimensional reconstruction of coronary arteries using estimation techniques

Alok Sarwal, Atam Dhawan, Yateen Chitre

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208707

KEYWORDS: Reconstruction algorithms, Chemical elements, Arteries, Angiography, Image segmentation, 3D modeling, Algorithm development, 3D image processing, Computer programming, 3D image reconstruction

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Proceedings Article | 12 May 1995 Paper

Classification of mammographic microcalcifications using wavelets

Yateen Chitre, Atam Dhawan, Myron Moskowitz, Alok Sarwal, Christine Bonasso, Suresh Narayan

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208678

KEYWORDS: Wavelets, Neural networks, Breast cancer, Statistical analysis, Image segmentation, Mammography, Prototyping, Calcium, Tissues, Cancer

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Proceedings Article | 12 May 1995 Paper

Early detection of postoperative residual tumor using image subtraction

Suresh Narayan, Atam Dhawan, Jamal Taha, Mary Gaskill-Shipley, Michael Lamba, Alok Sarwal, Yateen Chitre

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208753

KEYWORDS: Tumors, Magnetic resonance imaging, Image enhancement, Gadolinium, Brain, Surgery, Tissues, Neuroimaging, Image contrast enhancement, Signal detection

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The detection after surgery of residual tumor from magnetic resonance (MR) images is difficult due to the low contrast level of the images. Gadolinium-enhanced MR imaging has been found valuable in detecting residual enhancing tumor when performed within 72 hours after surgery. The patient is scanned by the MR scanner with and without infusion of gadolinium, a contrast agent. Usually, the estimation of post-operative tumor volume is done by visual comparison of the T1 MR images obtained with and without gadolinium infusion. The T1 MR images, in most cases, without contrast demonstrates areas of hyper intensities (high brightness levels), consistent with hemorrhage. These hyper intense areas often make it difficult to detect residual tumor in post contrast images. This is due to the presence of both acute hemorrhage and gadolinium enhancement which have high brightness levels in T1 MR images. Even in MR images taken within 72 hours after surgery, detection of tumor enhancement in areas of increased T1 signal produced by blood products or by postoperative changes can be difficult when performed by the naked eye. Due to these problems, the quantification of residual tumor becomes a subjective issue among neuro-radiologists. Thus to reduce errors produced by the human factor, an automated procedure to detect residual tumor is required. We have developed a technique to differentiate tumor enhancement from postoperative changes and blood products on MR imaging. The technique involves fusion of pre- and post-gadolinium MR images performed in the immediate postoperative period. Computerized slice based substraction is then done on the corresponding fused images of the two sets. The subtraction process results in a composite slice, which is examined for differences between pre- and post-gadolinium studies. The presented technique was tested on 14 cases in which MR images were obtained from brain tumor patients within 72 hours after surgery. The subtraction technique easily distinguished residual enhancing tumor from postoperative surgical changes and was simple to perform. The technique proposed and developed has given good results and will be used in clinical trial and diagnosis. Future potentials of the technique are discussed and illustrative cases presented.

Proceedings Article | 1 March 1992 Paper

Rule-based automatic segmentation for 3D coronary arteriography

Alok Sarwal, Paul Truitt, Fusun Ozguner, Qian Zhang, Dennis Parker

Proceedings Volume 1708, (1992) https://doi.org/10.1117/12.58597

KEYWORDS: Binary data, 3D modeling, Image segmentation, Angiography, Arteries, Robotics, Data modeling, Artificial intelligence, Machine vision, Visual process modeling

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Showing 5 of 9 publications

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