Dr. Khan M. Siddiqui Profile

Dr. Khan M. Siddiqui

Chief Technical Officer at higi SH LLC

SPIE Involvement:

Author

Publications (19)

Proceedings Article | 16 February 2012 Paper

Linking DICOM pixel data with radiology reports using automatic semantic annotation

Sayan Pathak, Woojin Kim, Indeera Munasinghe, Antonio Criminisi, Steve White, Khan Siddiqui

Proceedings Volume 8319, 83190D (2012) https://doi.org/10.1117/12.912391

KEYWORDS: Radiology, Computed tomography, Liver, Kidney, Medical imaging, Associative arrays, Web services, Image processing, Visualization, Algorithm development

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Proceedings Article | 16 February 2012 Paper

Comparative analysis of semantic localization accuracies between adult and pediatric DICOM CT images

Duncan Robertson, Sayan Pathak, Antonio Criminisi, Steve White, David Haynor, Oliver Chen, Khan Siddiqui

Proceedings Volume 8319, 83190N (2012) https://doi.org/10.1117/12.912428

KEYWORDS: Computed tomography, Data modeling, Kidney, Liver, Databases, Abdomen, Heart, Feature extraction, Image registration, Detection and tracking algorithms

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Proceedings Article | 12 March 2011 Paper

Robust linear registration of CT images using random regression forests

Ender Konukoglu, Antonio Criminisi, Sayan Pathak, Duncan Robertson, Steve White, David Haynor, Khan Siddiqui

Proceedings Volume 7962, 79621X (2011) https://doi.org/10.1117/12.878085

KEYWORDS: Image registration, Image processing algorithms and systems, Computed tomography, Image segmentation, Optimization (mathematics), Databases, Stochastic processes, Medical imaging, Detection and tracking algorithms, 3D image processing

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Proceedings Article | 25 February 2011 Paper

Validating automatic semantic annotation of anatomy in DICOM CT images

Sayan Pathak, Antonio Criminisi, Jamie Shotton, Steve White, Duncan Robertson, Bobbi Sparks, Indeera Munasinghe, Khan Siddiqui

Proceedings Volume 7967, 796704 (2011) https://doi.org/10.1117/12.878355

KEYWORDS: Computed tomography, Visualization, Lung, Databases, Liver, Detection and tracking algorithms, Kidney, Heart, Spleen, Feature extraction

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Proceedings Article | 11 March 2010 Paper

An automatic system to detect and extract texts in medical images for de-identification

Yingxuan Zhu, P. D. Singh, Khan Siddiqui, Michael Gillam

Proceedings Volume 7628, 762803 (2010) https://doi.org/10.1117/12.855588

KEYWORDS: Medical imaging, Medical research, Prototyping, Optical character recognition, Image segmentation, Computing systems, Detection and tracking algorithms, Optimization (mathematics), Edge detection, Electrical engineering

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Proceedings Article | 1 April 2008 Paper

Automated discovery of meniscal tears on MR imaging: a novel high-performance computer-aided detection application for radiologists

Bharath Ramakrishna, Nabile Safdar, Khan Siddiqui, Woojin Kim, Weimin Liu, Ganesh Saiprasad, Chein-I Chang, Eliot Siegel

Proceedings Volume 6915, 691531 (2008) https://doi.org/10.1117/12.773167

KEYWORDS: CAD systems, Computer aided diagnosis and therapy, Magnetic resonance imaging, Injuries, Diagnostics, Imaging systems, Image processing, Computer aided design, Computing systems

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Proceedings Article | 17 March 2008 Paper

A concurrent computer aided detection (CAD) tool for articular cartilage disease of the knee on MR imaging using active shape models

Bharath Ramakrishna, Ganesh Saiprasad, Nabile Safdar, Khan Siddiqui, Chein-I Chang, Eliot Siegel

Proceedings Volume 6915, 69153G (2008) https://doi.org/10.1117/12.773157

KEYWORDS: Cartilage, Computer aided diagnosis and therapy, Solid modeling, Magnetic resonance imaging, Injuries, 3D modeling, Interfaces, Image processing, Bone, Image segmentation

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Proceedings Article | 13 March 2008 Paper

Role of computer aided detection (CAD) integration: case study with meniscal and articular cartilage CAD applications

Nabile Safdar, Bharath Ramakrishna, Ganesh Saiprasad, Khan Siddiqui, Eliot Siegel

Proceedings Volume 6919, 69190J (2008) https://doi.org/10.1117/12.779304

KEYWORDS: Cartilage, Injuries, CAD systems, Computer aided diagnosis and therapy, Computer aided design, Diagnostics, Detection and tracking algorithms, Magnetic resonance imaging, Image segmentation, Solid modeling

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Proceedings Article | 30 March 2007 Paper

Automatic CAD of meniscal tears on MR imaging: a morphology-based approach

Bharath Ramakrishna, Weimin Liu, Nabile Safdar, Khan Siddiqui, Woojin Kim, Krishna Juluru, Chein-I Chang, Eliot Siegel

Proceedings Volume 6514, 651437 (2007) https://doi.org/10.1117/12.713288

KEYWORDS: Computer aided diagnosis and therapy, Image segmentation, Magnetic resonance imaging, Image processing, Cartilage, Image processing algorithms and systems, Surgery, Detection and tracking algorithms, Binary data, Tissues

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Proceedings Article | 21 March 2007 Paper

Observer success rates for identification of 3D surface reconstructed facial images and implications for patient privacy and security

Joseph Chen, Khan Siddiqui, Leslie Fort, Ryan Moffitt, Krishna Juluru, Woojin Kim, Nabile Safdar, Eliot Siegel

Proceedings Volume 6516, 65161B (2007) https://doi.org/10.1117/12.717850

KEYWORDS: Digital photography, Photography, 3D image processing, 3D image reconstruction, Computed tomography, Diagnostics, Software, Statistical analysis, Radiology, Facial recognition systems

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Proceedings Article | 21 March 2007 Paper

Volumetric measurements of pulmonary nodules: variability in automated analysis tools

Krishna Juluru, Woojin Kim, William Boonn, Tara King, Khan Siddiqui, Eliot Siegel

Proceedings Volume 6516, 651613 (2007) https://doi.org/10.1117/12.711642

KEYWORDS: Lung, Computed tomography, Visualization, Medicine, Document management, Lung cancer, Picture Archiving and Communication System, Tumors, Medical imaging, Image visualization

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Proceedings Article | 19 March 2007 Paper

Sinogram restoration for ultra-low-dose x-ray multi-slice helical CT by nonparametric regression

Lu Jiang, Khan Siddiqui, Bin Zhu, Yang Tao, Eliot Siegel

Proceedings Volume 6510, 65105L (2007) https://doi.org/10.1117/12.711631

KEYWORDS: X-ray computed tomography, Computed tomography, X-rays, Reconstruction algorithms, Image filtering, Data acquisition, Denoising, Lung cancer, Data modeling, Sensors

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During the last decade, x-ray computed tomography (CT) has been applied to screen large asymptomatic smoking and nonsmoking populations for early lung cancer detection. Because a larger population will be involved in such screening exams, more and more attention has been paid to studying low-dose, even ultra-low-dose x-ray CT. However, reducing CT radiation exposure will increase noise level in the sinogram, thereby degrading the quality of reconstructed CT images as well as causing more streak artifacts near the apices of the lung. Thus, how to reduce the noise levels and streak artifacts in the low-dose CT images is becoming a meaningful topic. Since multi-slice helical CT has replaced conventional stop-and-shoot CT in many clinical applications, this research mainly focused on the noise reduction issue in multi-slice helical CT. The experiment data were provided by Siemens SOMATOM Sensation 16-Slice helical CT. It included both conventional CT data acquired under 120 kvp voltage and 119 mA current and ultra-low-dose CT data acquired under 120 kvp and 10 mA protocols. All other settings are the same as that of conventional CT. In this paper, a nonparametric smoothing method with thin plate smoothing splines and the roughness penalty was proposed to restore the ultra-low-dose CT raw data. Each projection frame was firstly divided into blocks, and then the 2D data in each block was fitted to a thin-plate smoothing splines' surface via minimizing a roughness-penalized least squares objective function. By doing so, the noise in each ultra-low-dose CT projection was reduced by leveraging the information contained not only within each individual projection profile, but also among nearby profiles. Finally the restored ultra-low-dose projection data were fed into standard filtered back projection (FBP) algorithm to reconstruct CT images. The rebuilt results as well as the comparison between proposed approach and traditional method were given in the results and discussions section, and showed effectiveness of proposed thin-plate based nonparametric regression method.

Proceedings Article | 8 March 2007 Paper

Influence of 8-bit versus 11-bit digital displays on observer performance and visual search: a multi-center evaluation

Elizabeth Krupinski, Khan Siddiqui, Eliot Siegel, Rasu Shrestha, Edward Grant, Hans Roehrig, Jiahua Fan

Proceedings Volume 6515, 65150L (2007) https://doi.org/10.1117/12.706293

KEYWORDS: LCDs, Visualization, Chest, Manufacturing, Video, Diagnostics, Radiology, Eye, Statistical analysis, Medicine

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