Dr. Raja S. Alomari Profile

Dr. Raja S. Alomari

Teaching Assistant at Univ at Buffalo

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Publications (7)

Proceedings Article | 23 February 2012 Paper

Local binary patterns for stromal area removal in histology images

Raja Alomari, Subarna Ghosh, Vipin Chaudhary, Omar Al-Kadi

Proceedings Volume 8315, 831524 (2012) https://doi.org/10.1117/12.911007

KEYWORDS: CAD systems, Binary data, Tissues, Tumors, Image segmentation, Computer aided diagnosis and therapy, Data modeling, Cancer, RGB color model, Statistical analysis

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Nuclei counting in epithelial cells is an indication for tumor proliferation rate which is useful to rank tumors and select an appropriate treatment schedule for the patient. However, due to the high interand intra- observer variability in nuclei counting, pathologists seek a deterministic proliferation rate estimate. Histology tissue contains epithelial and stromal cells. However, nuclei counting is clinically restricted to epithelial cells because stromal cells do not become cancerous themselves since they remain genetically normal. Counting nuclei existing within the stromal tissue is one of the major causes of the proliferation rate non-deterministic estimation. Digitally removing stromal tissue will eliminate a major cause in pathologist counting variability and bring the clinical pathologist a major step closer toward a deterministic proliferation rate estimation. To that end, we propose a computer aided diagnosis (CAD) system for eliminating stromal cells from digital histology images based on the local binary patterns, entropy measurement, and statistical analysis. We validate our CAD system on a set of fifty Ki-67-stained histology images. Ki-67-stained histology images are among the clinically approved methods for proliferation rate estimation. To test our CAD system, we prove that the manual proliferation rate estimation performed by the expert pathologist does not change before and after stromal removal. Thus, stromal removal does not affect the expert pathologist estimation clinical decision. Hence, the successful elimination of the stromal area highly reduces the false positive nuclei which are the major confusing cause for the less experienced pathologists and thus accounts for the non-determinism in the proliferation rate estimation. Our experimental setting shows statistical insignificance (paired student t-test shows ρ = 0.74) in the manual nuclei counting before and after our automated stromal removal. This means that the clinical decision of the expert pathologist is not affected by our CAD system which is what we want to prove. However, the usage of our CAD system substantially account for the reduced inter- and intra- proliferation rate estimation variability and especially for less-experienced pathologists.

Proceedings Article | 23 February 2012 Paper

Automating proliferation rate estimation from Ki-67 histology images

Heba Al-Lahham, Raja Alomari, Hazem Hiary, Vipin Chaudhary

Proceedings Volume 8315, 83152A (2012) https://doi.org/10.1117/12.911009

KEYWORDS: Image segmentation, Cancer, Breast cancer, CAD systems, Feature extraction, RGB color model, Mathematical morphology, Computer aided design, Tissues, Solid modeling

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Proceedings Article | 4 March 2011 Paper

Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis

Subarna Ghosh, Raja' Alomari, Vipin Chaudhary, Gurmeet Dhillon

Proceedings Volume 7963, 796303 (2011) https://doi.org/10.1117/12.878055

KEYWORDS: Image segmentation, Spine, Magnetic resonance imaging, Computed tomography, Data modeling, Gold, Diagnostics, Bone, Data analysis, Nerve

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Proceedings Article | 4 March 2011 Paper

Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter- and intra-context features with a two-level classifier

Jaehan Koh, Raja Alomari, Vipin Chaudhary, Gurmeet Dhillon

Proceedings Volume 7963, 796304 (2011) https://doi.org/10.1117/12.878332

KEYWORDS: Diagnostics, Magnetic resonance imaging, Computer aided diagnosis and therapy, Spatial resolution, Image segmentation, Statistical analysis, Spine, Surgery, Imaging devices, Image resolution

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Proceedings Article | 9 March 2010 Paper

Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI

Raja' Alomari, Jason Corso, Vipin Chaudhary, Gurmeet Dhillon

Proceedings Volume 7624, 76241A (2010) https://doi.org/10.1117/12.842199

KEYWORDS: Magnetic resonance imaging, Computer aided diagnosis and therapy, Spine, Binary data, Diagnostics, Solid modeling, Feature extraction, X-rays, CAD systems, Neural networks

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Proceedings Article | 3 March 2009 Paper

Localization of tissues in high-resolution digital anatomic pathology images

Raja Alomari, Ron Allen, Bikash Sabata, Vipin Chaudhary

Proceedings Volume 7260, 726016 (2009) https://doi.org/10.1117/12.811430

KEYWORDS: Tissues, Image segmentation, Neural networks, Image resolution, Pathology, RGB color model, Feature extraction, Machine learning, Binary data, Medical imaging

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Proceedings Article | 27 March 2008 Paper

Design of a benchmark dataset, similarity metrics, and tools for liver segmentation

Suryaprakash Kompalli, Mohammed Alam, Raja Alomari, Stanley Lau, Vipin Chaudhary

Proceedings Volume 6915, 691537 (2008) https://doi.org/10.1117/12.772940

KEYWORDS: Liver, Image segmentation, Computed tomography, Surgery, Gold, Error analysis, Diagnostics, Tissues, Evolutionary algorithms, Data modeling

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Showing 5 of 7 publications

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