Patents by Inventor Yousef Al-Kofahi
Yousef Al-Kofahi has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240161486Abstract: In one aspect, a method for inspecting features of an image using an image inspection controller that includes a processor communicatively coupled to a memory is described. The method includes receiving, at the processor, an input image, performing, on the input image, one of a semantic segmentation process and an object classification process to generate an output image, and prompting a user to select between approving the displayed output image, and at least one of i) performing an additional semantic segmentation process on the displayed output image, and ii) performing an additional object classification process on the displayed output image.Type: ApplicationFiled: September 20, 2023Publication date: May 16, 2024Applicant: Molecular Devices, LLCInventors: Yousef Al-Kofahi, Michael MacDonald, Asha Singanamalli, Mohammed Yousefhussien, Will Marshall
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Patent number: 11798270Abstract: In one aspect, a method for inspecting features of an image using an image inspection controller that includes a processor communicatively coupled to a memory is described. The method includes receiving, at the processor, an input image, performing, on the input image, one of a semantic segmentation process and an object classification process to generate an output image, and prompting a user to select between approving the displayed output image, and at least one of i) performing an additional semantic segmentation process on the displayed output image, and ii) performing an additional object classification process on the displayed output image.Type: GrantFiled: March 30, 2021Date of Patent: October 24, 2023Assignee: Molecular Devices, LLCInventors: Yousef Al-Kofahi, Michael MacDonald, Asha Singanamalli, Mohammed Yousefhussien, Will Marshall
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Patent number: 11407179Abstract: A system monitoring an additive manufacturing (AM) machine recoat operation includes an automatic defect recognition subsystem having a predictive model catalog each applicable to a product and to one recoat error indication having a domain dependent feature, the predicative models representative of a recoat error indication appearance at a pixel level of an image captured during recoat operations. The system includes an online monitoring subsystem having an image classifier unit that classifies recoat error indications at the pixel level based on predictive models selected on their metadata, a virtual depiction unit that creates a virtual depiction of an ongoing AM build from successive captured image, and a processor unit to monitor the build for recoat error indications, classify a detected indication, and provide a determination regarding the severity of the detected indication on the ongoing build. A method and a non-transitory computer-readable medium are also disclosed.Type: GrantFiled: March 20, 2019Date of Patent: August 9, 2022Assignee: GENERAL ELECTRIC COMPANYInventors: Joanna Mechelle Jayawickrema, Thomas Spears, Yousef Al-Kofahi, Ali Can
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Patent number: 11301977Abstract: An image inspection computing device is provided. The device includes a memory device and at least one processor. The at least one processor is configured to receive at least one sample image of a first component, wherein the at least one sample image of the first component does not include defects, store, in the memory, the at least one sample image, and receive an input image of a second component. The at least one processor is also configured to generate an encoded array based on the input image of the second component, perform a stochastic data sampling process on the encoded array, generate a decoded array, and generate a reconstructed image of the second component, derived from the stochastic data sampling process and the decoded array. The at least one processor is further configured to produce a residual image, and identify defects in the second component.Type: GrantFiled: April 10, 2020Date of Patent: April 12, 2022Assignee: General Electric CompanyInventors: Alberto Santamaria-Pang, Yousef Al-Kofahi, Aritra Chowdhury, Shourya Sarcar, Michael John MacDonald, Peter Arjan Wassenaar, Patrick Joseph Howard, Bruce Courtney Amm, Eric Seth Moderbacher
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Publication number: 20210334607Abstract: In one aspect, a method for inspecting features of an image using an image inspection controller that includes a processor communicatively coupled to a memory is described. The method includes receiving, at the processor, an input image, performing, on the input image, one of a semantic segmentation process and an object classification process to generate an output image, and prompting a user to select between approving the displayed output image, and at least one of i) performing an additional semantic segmentation process on the displayed output image, and ii) performing an additional object classification process on the displayed output image.Type: ApplicationFiled: March 30, 2021Publication date: October 28, 2021Applicant: Molecular Devices, LLCInventors: Yousef Al-Kofahi, Michael MacDonald, Asha Singanamalli, Mohammed Yousefhussien, Will Marshall
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Publication number: 20210319544Abstract: An image inspection computing device is provided. The device includes a memory device and at least one processor. The at least one processor is configured to receive at least one sample image of a first component, wherein the at least one sample image of the first component does not include defects, store, in the memory, the at least one sample image, and receive an input image of a second component. The at least one processor is also configured to generate an encoded array based on the input image of the second component, perform a stochastic data sampling process on the encoded array, generate a decoded array, and generate a reconstructed image of the second component, derived from the stochastic data sampling process and the decoded array. The at least one processor is further configured to produce a residual image, and identify defects in the second component.Type: ApplicationFiled: April 10, 2020Publication date: October 14, 2021Inventors: Alberto Santamaria-Pang, Yousef Al-Kofahi, Aritra Chowdhury, Shourya Sarcar, Michael John MacDonald, Peter Arjan Wassenaar, Patrick Joseph Howard, Bruce Courtney Amm, Eric Seth Moderbacher
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Patent number: 10789451Abstract: The present disclosure relates to a computer-implemented system and its associated method for single channel whole cell segmentation of a sample image of a biological sample. The biological sample may be stained with one or more non-nuclear cell marker stains, and the system and the method are configured to transform the sample image of the biological sample stained with the one or more non-nuclear cell marker stains into a segmented image having one or more cells with delineated nuclei and cytoplasm regions.Type: GrantFiled: November 16, 2017Date of Patent: September 29, 2020Assignee: Global Life Sciences Solutions USA LLCInventors: Yousef Al-Kofahi, Mirabela Rusu
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Publication number: 20200298498Abstract: A system monitoring an additive manufacturing (AM) machine recoat operation includes an automatic defect recognition subsystem having a predictive model catalog each applicable to a product and to one recoat error indication having a domain dependent feature, the predicative models representative of a recoat error indication appearance at a pixel level of an image captured during recoat operations. The system includes an online monitoring subsystem having an image classifier unit that classifies recoat error indications at the pixel level based on predictive models selected on their metadata, a virtual depiction unit that creates a virtual depiction of an ongoing AM build from successive captured image, and a processor unit to monitor the build for recoat error indications, classify a detected indication, and provide a determination regarding the severity of the detected indication on the ongoing build. A method and a non-transitory computer-readable medium are also disclosed.Type: ApplicationFiled: March 20, 2019Publication date: September 24, 2020Inventors: Joanna Mechelle JAYAWICKREMA, Thomas SPEARS, Yousef AL-KOFAHI, Ali CAN
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Publication number: 20190147215Abstract: The present disclosure relates to a computer-implemented system and its associated method for single channel whole cell segmentation of a sample image of a biological sample. The biological sample may be stained with one or more non-nuclear cell marker stains, and the system and the method are configured to transform the sample image of the biological sample stained with the one or more non-nuclear cell marker stains into a segmented image having one or more cells with delineated nuclei and cytoplasm regions.Type: ApplicationFiled: November 16, 2017Publication date: May 16, 2019Inventors: Yousef Al-Kofahi, Mirabela Rusu
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Patent number: 10083340Abstract: The disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue specimen sample. A training set of cells is examined by an expert to determine which cells that include “good” segmentation and which cells include “poor” segmentation. A training model is build based on the image data of the cells in the training set. Image data from cells in a test specimen is obtained and that image data is compared to the training model to determine on a cell-by-cell basis which cells in the test specimen include “good” segmentation and which cells include “poor” segmentation. The accumulated data on the cells analyzed in the test specimen can be utilized to determine an overall segmentation quality score for the area of the test specimen in which the individual cells are located in the test specimen.Type: GrantFiled: January 26, 2016Date of Patent: September 25, 2018Assignee: GE Healthcare Bio-Sciences Corp.Inventors: Raghav Krishna Padmanabhan, Edward John Moler, Yousef Al-Kofahi, Alberto Santamaria-Pang, Brion Daryl Sarachan, Qing Li
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Patent number: 10019796Abstract: Disclosed are novel computer-implemented methods for creating a blood vessel map of a biological tissue. The methods comprise the steps of, accessing image data corresponding to multi-channel multiplexed image of a fluorescently stained biological tissue manifesting expression levels of a primary marker and at least one auxiliary marker of blood vasculature, and extracting features of blood vessels using the primary marker as an input to create a single channel segmentation of the blood vessels. The method further comprises the steps of extracting features of blood vessels using the auxiliary marker to create auxiliary channels as a second input and apply multi-channel blood vessel enhancement. Blood vessel maps are created using the features and tracing the blood vasculature by iteratively extending the centerlines of the initial segmentation using statistical models and geometric rules.Type: GrantFiled: September 20, 2016Date of Patent: July 10, 2018Assignee: GENERAL ELECTRIC COMPANYInventors: Yousef Al-Kofahi, Anup Sood, Fiona Ginty, Qing Li, Christopher James Sevinsky, Alberto Santamaria-Pang
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Patent number: 9858469Abstract: The present disclosure relates to characterization of biological samples via modular image analysis. By way of example, the analysis may include extracting plurality of regions of interest from the biological sample from a plurality of image data sets. Each region of interest may include a combination of one or more subsets of an image data set. In one example, an image data set may be generated by image segmentation. After each region of interest is extracted, at least one metric of the region of interest may be determined.Type: GrantFiled: December 18, 2014Date of Patent: January 2, 2018Assignee: GE Healthcare Bio-Sciences Corp.Inventors: Yousef Al-Kofahi, Brion Daryl Sarachan
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Patent number: 9785848Abstract: The disclosed subject matter relates to an automated determination the stain quality and segmentation quality of a tissue sample. By way of example, separate image data is acquired of an unstained form of a biological specimen, the biological specimen stained with a nuclei marker and the biological specimen stained with a segmentation marker. A correlation map (Cr) from the separate image data and a ridgeness map (Pr) from the image data of the biological specimen stained with a segmentation marker are each determined. A staining quality score and segmentation quality score are then determined from the correlation map (Cr) and the ridgeness map (Pr).Type: GrantFiled: April 30, 2015Date of Patent: October 10, 2017Assignee: GE HEALTHCARE BIO-SCIENCES CORP.Inventors: Brion Daryl Sarachan, Alberto Santamaria-Pang, Yousef Al-Kofahi, Edward John Moler, Raghav Krishna Padmanabhan, Qing Li
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Publication number: 20170213067Abstract: The disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue specimen sample. A training set of cells is examined by an expert to determine which cells that include “good” segmentation and which cells include “poor” segmentation. A training model is build based on the image data of the cells in the training set. Image data from cells in a test specimen is obtained and that image data is compared to the training model to determine on a cell-by-cell basis which cells in the test specimen include “good” segmentation and which cells include “poor” segmentation. The accumulated data on the cells analyzed in the test specimen can be utilized to determine an overall segmentation quality score for the area of the test specimen in which the individual cells are located in the test specimen.Type: ApplicationFiled: January 26, 2016Publication date: July 27, 2017Inventors: Raghav Krishna Padmanabhan, Edward John Moler, Yousef Al-Kofahi, Alberto Santamaria-Pang, Brion Daryl Sarachan, Qing Li
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Publication number: 20170109880Abstract: Disclosed are novel computer-implemented methods for creating a blood vessel map of a biological tissue. The methods comprise the steps of, accessing image data corresponding to multi-channel multiplexed image of a fluorescently stained biological tissue manifesting expression levels of a primary marker and at least one auxiliary marker of blood vasculature, and extracting features of blood vessels using the primary marker as an input to create a single channel segmentation of the blood vessels. The method further comprises the steps of extracting features of blood vessels using the auxiliary marker to create auxiliary channels as a second input and apply multi-channel blood vessel enhancement.Type: ApplicationFiled: September 20, 2016Publication date: April 20, 2017Inventors: Yousef Al-Kofahi, Anup Sood, Fiona Ginty, Qing Li, Christopher James Sevinsky, Alberto Santamaria-Pang
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Publication number: 20160321512Abstract: The disclosed subject matter relates to an automated determination the stain quality and segmentation quality of a tissue sample. By way of example, separate image data is acquired of an unstained form of a biological specimen, the biological specimen stained with a nuclei marker and the biological specimen stained with a segmentation marker. A correlation map (Cr) from the separate image data and a ridgeness map (Pr) from the image data of the biological specimen stained with a segmentation marker are each determined. A staining quality score and segmentation quality score are then determined from the correlation map (Cr) and the ridgeness map (Pr).Type: ApplicationFiled: April 30, 2015Publication date: November 3, 2016Inventors: Brion Daryl Sarachan, Alberto Santamaria-Pang, Yousef Al-Kofahi, Edward John Moler, Raghav Krishna Padmanabhan, Li Qing
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Publication number: 20160314335Abstract: The present disclosure relates to characterization of biological samples via modular image analysis. By way of example, the analysis may include extracting plurality of regions of interest from the biological sample from a plurality of image data sets. Each region of interest may include a combination of one or more subsets of an image data set. In one example, an image data set may be generated by image segmentation. After each region of interest is extracted, at least one metric of the region of interest may be determined.Type: ApplicationFiled: December 18, 2014Publication date: October 27, 2016Inventors: Yousef Al-Kofahi, Brion Daryl Sarachan
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Patent number: 9135694Abstract: A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.Type: GrantFiled: December 4, 2012Date of Patent: September 15, 2015Assignee: General Electric CompanyInventors: Antti Seppo, Yousef Al-Kofahi, Dirk R. Padfield
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Publication number: 20140153811Abstract: A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.Type: ApplicationFiled: December 4, 2012Publication date: June 5, 2014Applicant: General Electric CompanyInventors: Antti Seppo, Yousef Al-Kofahi, Dirk R. Padfield