Dr. Awais Mansoor Profile

Dr. Awais Mansoor

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Publications (8)

Proceedings Article | 13 March 2019 Paper

Cranial localization in 2D cranial ultrasound images using deep neural networks

Pooneh Tabrizi, Awais Mansoor, Rawad Obeid, Anna Penn, Marius George Linguraru

Proceedings Volume 10950, 1095026 (2019) https://doi.org/10.1117/12.2512283

KEYWORDS: Head, Neural networks, Copper, Ultrasonography, Fetus, Brain, Medicine, Error analysis, Traumatic brain injury, Biometrics

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Proceedings Article | 21 December 2018 Paper

LungAIR: an automated technique to predict hospitalization due to LRTI using fused information

Awais Mansoor, Gustavo Nino, Geovanny Perez, Marius George Linguraru

Proceedings Volume 10975, 109750H (2018) https://doi.org/10.1117/12.2508006

KEYWORDS: Lung, Chest imaging, Image segmentation, Opacity, Oxygen, Lung imaging, Pathology, Tissues, Chest

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Proceedings Article | 27 February 2018 Presentation + Paper

Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks

Pooneh Tabrizi, Awais Mansoor, Elijah Biggs, James Jago, Marius George Linguraru

Proceedings Volume 10575, 105751Z (2018) https://doi.org/10.1117/12.2295206

KEYWORDS: Kidney, Neural networks, Ultrasonography, 3D image processing, Diagnostics

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Proceedings Article | 3 March 2017 Presentation + Paper

Joint deep shape and appearance learning: application to optic pathway glioma segmentation

Awais Mansoor, Ien Li, Roger Packer, Robert Avery, Marius George Linguraru

Proceedings Volume 10134, 101341O (2017) https://doi.org/10.1117/12.2255580

KEYWORDS: Tissues, Image segmentation, Visualization, Magnetic resonance imaging, Neural networks, Visual optics, Computer aided diagnosis and therapy, Computing systems, Tumors, Optic nerve

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Proceedings Article | 24 February 2017 Presentation + Paper

Marginal shape deep learning: applications to pediatric lung field segmentation

Awais Mansoor, Juan Cerrolaza, Geovany Perez, Elijah Biggs, Gustavo Nino, Marius George Linguraru

Proceedings Volume 10133, 1013304 (2017) https://doi.org/10.1117/12.2254412

KEYWORDS: Principal component analysis, Image segmentation, Lung, Statistical modeling, Chest imaging, Image classification, Medical imaging modalities, Statistical analysis, Optimization (mathematics), Machine learning, Pattern recognition, Object recognition, Data modeling

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Proceedings Article | 24 March 2016 Paper

Semi-automatic assessment of pediatric hydronephrosis severity in 3D ultrasound

Juan Cerrolaza, Hansel Otero, Peter Yao, Elijah Biggs, Awais Mansoor, Roberto Ardon, James Jago, Craig Peters, Marius George Linguraru

Proceedings Volume 9785, 97851Q (2016) https://doi.org/10.1117/12.2216830

KEYWORDS: Kidney, Ultrasonography, Computer aided diagnosis and therapy, CAD systems, Image segmentation, Imaging systems, 3D image processing, Diagnostics, Computing systems, Computer aided design

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Proceedings Article | 24 March 2016 Paper

Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images

Awais Mansoor, Rafael Casas, Marius Linguraru

Proceedings Volume 9785, 978514 (2016) https://doi.org/10.1117/12.2216958

KEYWORDS: Computed tomography, Image segmentation, Chest, Probability theory, Heart, Cancer, Tissues, Image classification, Machine learning, Lung, Failure analysis, Image registration, Feature extraction

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Proceedings Article | 11 February 2009 Paper

Compression of multispectral fluorescence microscopic images based on a modified set partitioning in hierarchal trees

Awais Mansoor, J. Paul Robinson, Bartek Rajwa

Proceedings Volume 7245, 72450J (2009) https://doi.org/10.1117/12.806038

KEYWORDS: Image compression, Multispectral imaging, 3D image processing, Luminescence, Digital imaging, Image quality, Wavelets, Image processing, Imaging systems, Electronic imaging

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Modern automated microscopic imaging techniques such as high-content screening (HCS), high-throughput screening, 4D imaging, and multispectral imaging are capable of producing hundreds to thousands of images per experiment. For quick retrieval, fast transmission, and storage economy, these images should be saved in a compressed format. A considerable number of techniques based on interband and intraband redundancies of multispectral images have been proposed in the literature for the compression of multispectral and 3D temporal data. However, these works have been carried out mostly in the elds of remote sensing and video processing. Compression for multispectral optical microscopy imaging, with its own set of specialized requirements, has remained under-investigated. Digital photography{oriented 2D compression techniques like JPEG (ISO/IEC IS 10918-1) and JPEG2000 (ISO/IEC 15444-1) are generally adopted for multispectral images which optimize visual quality but do not necessarily preserve the integrity of scientic data, not to mention the suboptimal performance of 2D compression techniques in compressing 3D images. Herein we report our work on a new low bit-rate wavelet-based compression scheme for multispectral fluorescence biological imaging. The sparsity of signicant coefficients in high-frequency subbands of multispectral microscopic images is found to be much greater than in natural images; therefore a quad-tree concept such as Said et al.'s SPIHT1 along with correlation of insignicant wavelet coefficients has been proposed to further exploit redundancy at high-frequency subbands. Our work propose a 3D extension to SPIHT, incorporating a new hierarchal inter- and intra-spectral relationship amongst the coefficients of 3D wavelet-decomposed image. The new relationship, apart from adopting the parent-child relationship of classical SPIHT, also brought forth the conditional "sibling" relationship by relating only the insignicant wavelet coefficients of subbands at the same level of decomposition. The insignicant quadtrees in dierent subbands in the high-frequency subband class are coded by a combined function to reduce redundancy. A number of experiments conducted on microscopic multispectral images have shown promising results for the proposed method over current state-of-the-art image-compression techniques.

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