Patents by Inventor PALLAB ROY
PALLAB ROY 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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Patent number: 10966602Abstract: A computer-implemented method includes obtaining an image of a retinal fundus. A plurality of features is extracted from the image of the retinal fundus. The plurality of features includes at least one feature based on anatomical domain knowledge of the retinal fundus and at least one response of a pre-trained deep convolutional neural network to at least a portion of the image of the retinal fundus. The retinal fundus is determined to belong to a left eye or a right eye, based on an analysis of the plurality of features.Type: GrantFiled: September 9, 2019Date of Patent: April 6, 2021Assignee: International Business Machines CorporationInventors: Rajib Chakravorty, Rahil Garnavi, Pallab Roy
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Publication number: 20200000331Abstract: A computer-implemented method includes obtaining an image of a retinal fundus. A plurality of features is extracted from the image of the retinal fundus. The plurality of features includes at least one feature based on anatomical domain knowledge of the retinal fundus and at least one response of a pre-trained deep convolutional neural network to at least a portion of the image of the retinal fundus. The retinal fundus is determined to belong to a left eye or a right eye, based on an analysis of the plurality of features.Type: ApplicationFiled: September 9, 2019Publication date: January 2, 2020Inventors: Rajib Chakravorty, Rahil Garnavi, Pallab Roy
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Patent number: 10405739Abstract: A computer-implemented method includes obtaining an image of a retinal fundus. A plurality of features is extracted from the image of the retinal fundus. The plurality of features includes at least one feature based on anatomical domain knowledge of the retinal fundus and at least one response of a pre-trained deep convolutional neural network to at least a portion of the image of the retinal fundus. The retinal fundus is determined to belong to a left eye or a right eye, based on an analysis of the plurality of features.Type: GrantFiled: February 19, 2016Date of Patent: September 10, 2019Assignee: International Business Machines CorporationInventors: Rajib Chakravorty, Rahil Garnavi, Pallab Roy
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Patent number: 10307050Abstract: An embodiment of the invention receives by an interface a retinal image from a patient, and identifies by a feature extraction device vessel fragments in the retinal image. The vessel fragments include at least a portion of a major vessel and at least a portion of a branch connected to a major vessel. A processor computes estimated blood flow velocities in the vessel fragments with a blood flow velocity estimation model and determines actual blood flow velocities in the vessel fragments. An analysis engine compares the actual blood flow velocities in the vessel fragments to the estimated blood flow velocities in the vessel fragments. The analysis engine detects a candidate plaque affected vessel fragment when the estimated blood flow velocities in the vessel fragments differs from the actual blood flow velocities in the vessel fragments by a predetermined amount.Type: GrantFiled: April 11, 2017Date of Patent: June 4, 2019Assignee: International Business Machines CorporationInventors: Rahil Garnavi, Kerry J. Halupka, Stephen M. Moore, Pallab Roy, Suman Sedai
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Patent number: 10229499Abstract: A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score.Type: GrantFiled: December 29, 2017Date of Patent: March 12, 2019Assignee: International Business Machines CorporationInventors: Seyedbehzad Bozorgtabar, Rahil Garnavi, Pallab Roy, Suman Sedai
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Patent number: 10223788Abstract: A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score.Type: GrantFiled: February 24, 2017Date of Patent: March 5, 2019Assignee: International Business Machines CorporationInventors: Seyedbehzad Bozorgtabar, Rahil Garnavi, Pallab Roy, Suman Sedai
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Patent number: 10169872Abstract: A computer-implemented method obtains at least one image from which severity of a given pathological condition presented in the at least one image is to be classified. The method generates a hybrid image representation of the at least one obtained image. The hybrid image representation comprises a concatenation of a discriminative pathology histogram, a generative pathology histogram, and a fully connected representation of a trained baseline convolutional neural network. The hybrid image representation is used to train a classifier to classify the severity of the given pathological condition presented in the at least one image. One non-limiting example of a pathological condition whose severity can be classified with the above method is diabetic retinopathy.Type: GrantFiled: February 7, 2017Date of Patent: January 1, 2019Assignee: International Business Machines CorporationInventors: Rahil Garnavi, Dwarikanath Mahapatra, Pallab Roy, Suman Sedai, Ruwan B. Tennakoon
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Patent number: 10098533Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.Type: GrantFiled: December 27, 2017Date of Patent: October 16, 2018Assignee: International Business Machines CorporationInventors: Rajib Chakravorty, Rahil Garnavi, Dwarikanath Mahapatra, Pallab Roy, Suman Sedai
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Publication number: 20180289252Abstract: An embodiment of the invention receives by an interface a retinal image from a patient, and identifies by a feature extraction device vessel fragments in the retinal image. The vessel fragments include at least a portion of a major vessel and at least a portion of a branch connected to a major vessel. A processor computes estimated blood flow velocities in the vessel fragments with a blood flow velocity estimation model and determines actual blood flow velocities in the vessel fragments. An analysis engine compares the actual blood flow velocities in the vessel fragments to the estimated blood flow velocities in the vessel fragments. The analysis engine detects a candidate plaque affected vessel fragment when the estimated blood flow velocities in the vessel fragments differs from the actual blood flow velocities in the vessel fragments by a predetermined amount.Type: ApplicationFiled: April 11, 2017Publication date: October 11, 2018Applicant: International Business Machines CorporationInventors: Rahil Garnavi, Kerry J. Halupka, Stephen M. Moore, Pallab Roy, Suman Sedai
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Publication number: 20180122068Abstract: A computer-implemented method obtains at least one image from which severity of a given pathological condition presented in the at least one image is to be classified. The method generates a hybrid image representation of the at least one obtained image. The hybrid image representation comprises a concatenation of a discriminative pathology histogram, a generative pathology histogram, and a fully connected representation of a trained baseline convolutional neural network. The hybrid image representation is used to train a classifier to classify the severity of the given pathological condition presented in the at least one image. One non-limiting example of a pathological condition whose severity can be classified with the above method is diabetic retinopathy.Type: ApplicationFiled: February 7, 2017Publication date: May 3, 2018Inventors: Rahil Garnavi, Dwarikanath Mahapatra, Pallab Roy, Suman Sedai, Ruwan B. Tennakoon
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Publication number: 20180116498Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.Type: ApplicationFiled: December 27, 2017Publication date: May 3, 2018Inventors: Rajib Chakravorty, Rahil Garnavi, Dwarikanath Mahapatra, Pallab Roy, Suman Sedai
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Publication number: 20180122071Abstract: A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score.Type: ApplicationFiled: December 29, 2017Publication date: May 3, 2018Inventors: SEYEDBEHZAD BOZORGTABAR, RAHIL GARNAVI, PALLAB ROY, SUMAN SEDAI
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Patent number: 9943225Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.Type: GrantFiled: September 23, 2016Date of Patent: April 17, 2018Assignee: International Business Machines CorporationInventors: Rajib Chakravorty, Rahil Garnavi, Dwarikanath Mahapatra, Pallab Roy, Suman Sedai
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Publication number: 20180084988Abstract: An AMD prediction model utilizes an OCT image estimation model. The OCT image estimation module is created by segmenting an OCT image to generate an OCT projection image for each of multiple biological layers; extracting from each of the generated OCT projection images a first set of features; extracting a second set of features from an input retinal fundus image; for each respective biological layer, registering the input retinal fundus image to the respective OCT projection image by matching at least some of the second set of features with corresponding ones of the first set of features; repeating the above with changes to the input retinal fundus image; and modelling how the changes to the input retinal fundus image are manifest at the correspondingly registered projection images. Estimated OCT projection images can then be generated for the multiple biological layers from a given retinal fundus image.Type: ApplicationFiled: September 23, 2016Publication date: March 29, 2018Inventors: Rajib Chakravorty, Rahil Garnavi, Dwarikanath Mahapatra, Pallab Roy, Suman Sedai
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Publication number: 20180061046Abstract: A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score.Type: ApplicationFiled: February 24, 2017Publication date: March 1, 2018Inventors: SEYEDBEHZAD BOZORGTABAR, RAHIL GARNAVI, PALLAB ROY, SUMAN SEDAI
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Publication number: 20170112372Abstract: A computer-implemented method includes obtaining an image of a retinal fundus. A plurality of features is extracted from the image of the retinal fundus. The plurality of features includes at least one feature based on anatomical domain knowledge of the retinal fundus and at least one response of a pre-trained deep convolutional neural network to at least a portion of the image of the retinal fundus. The retinal fundus is determined to belong to a left eye or a right eye, based on an analysis of the plurality of features.Type: ApplicationFiled: February 19, 2016Publication date: April 27, 2017Inventors: RAJIB CHAKRAVORTY, RAHIL GARNAVI, PALLAB ROY