Abstract: A method is described, for processing images of persons or objects to generate an identity preservative feature descriptor learnt for each person or object. The method includes obtaining an image of a person or object, extracting at least one spatial attribute of the person or object from the obtained image, and extracting at least one appearance feature of the person or object from the image by using a mapping function to translate image pixels into appearance attributes represented by at least one numerical feature. The method also includes combining the at least one spatial attribute and the at least one appearance feature to generate the unique feature descriptor representing the person or the object, to assign the unique feature descriptor to the image to enable feature descriptors representing the same person or object to be compared to feature descriptors representing different people or objects given a predefined mathematical pseudo-distance metric according to a least a distance from each other.

Abstract: A method is described, for processing images of persons or objects to generate an identity preservative feature descriptor learnt for each person or object. The method includes obtaining an image of a person or object, extracting at least one spatial attribute of the person or object from the obtained image, and extracting at least one appearance feature of the person or object from the image by using a mapping function to translate image pixels into appearance attributes represented by at least one numerical feature. The method also includes combining the at least one spatial attribute and the at least one appearance feature to generate the unique feature descriptor representing the person or the object, to assign the unique feature descriptor to the image to enable feature descriptors representing the same person or object to be compared to feature descriptors representing different people or objects given a predefined mathematical pseudo-distance metric according to a least a distance from each other.

Abstract: Methods and apparatus are disclosed to label radiology images are disclosed. An example apparatus, includes: means for obtaining user input identifying a vertebra on a spinal image; means for generating first annotations on the spinal image based on: 1) the user input; 2) connected regions on the spinal image; and 3) a number of viewable vertebrae on the spinal image; means for generating second annotations on the spinal image based on: 1) the error; 2) second connected regions on the spinal image; and 3) the number of viewable vertebrae on the spinal image, the second connected regions on the spinal image being determined based on second contextual-information features that account for the error; means for displaying the spinal image including the second annotations; means for validating the second annotations in association with the spinal image; and means for storing the second annotations in association with the spinal image.

Abstract: Methods and apparatus are disclosed to label radiology images are disclosed. An example apparatus, includes: means for obtaining user input identifying a vertebra on a spinal image; means for generating first annotations on the spinal image based on: 1) the user input; 2) connected regions on the spinal image; and 3) a number of viewable vertebrae on the spinal image; means for generating second annotations on the spinal image based on: 1) the error; 2) second connected regions on the spinal image; and 3) the number of viewable vertebrae on the spinal image, the second connected regions on the spinal image being determined based on second contextual-information features that account for the error; means for displaying the spinal image including the second annotations; means for validating the second annotations in association with the spinal image; and means for storing the second annotations in association with the spinal image.

Abstract: Methods and apparatus are disclosed to label radiology images. An example system includes a processor and a database. In response to receiving a first user input via a data input, the processor is to generate first annotations on a spinal image. The processor is to cause the spinal image having the first annotations to be displayed at a user interface. The processor is to prompt a user to provide second user input identifying an error in the first annotations. In response to receiving the second user input, the processor is to generate second annotations on the spinal image. The second annotations is to correct the error in the first annotations.

Abstract: Systems, methods and computer program products to annotate axial-view spine images with the desired characteristics of not requiring additional views of the spine or cross-referencing features are provided. In one aspect, the disclosed method does not require external training from a manually-labeled data set while being applicable to different imaging modalities and acquisition protocols. In one aspect, the disclosed method achieves near real-time results using integral kernels when implemented on a Graphics Processing Unit.

Abstract: Systems, methods and computer program products to annotate axial-view spine images with the desired characteristics of not requiring additional views of the spine or cross-referencing features are provided. In one aspect, the disclosed method does not require external training from a manually-labeled data set while being applicable to different imaging modalities and acquisition protocols. In one aspect, the disclosed method achieves near real-time results using integral kernels when implemented on a Graphics Processing Unit.

Abstract: Methods and apparatus are disclosed to methods and apparatus to generate three-dimensional spinal renderings. An example computer-implemented method includes receiving initial user input identifying a vertebra on a spinal image. In response to receiving the initial user input, simultaneously detecting inter-vertebral discs and vertebral bodies. The computer-implemented method includes displaying the detected inter-vertebral discs and vertebral bodies.

Abstract: Methods and apparatus are disclosed to automatically detect vertebrae boundaries in a spine image. A method to detect a vertebra in a spine image is described, the method comprising generating a rectangle approximation of a reference vertebra. The method also including identifying a mask similar to the rectangle approximation and labeling a mask region in the mask. The method also including comparing the mask region to the rectangle approximation and detecting a vertebra in the spine image based on the comparison.

Abstract: Methods and apparatus are disclosed to methods and apparatus to generate three-dimensional spinal renderings. An example computer-implemented method includes receiving initial user input identifying a vertebra on a spinal image. In response to receiving the initial user input, simultaneously detecting inter-vertebral discs and vertebral bodies. The computer-implemented method includes displaying the detected inter-vertebral discs and vertebral bodies.

Abstract: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes using a filter coefficient based on a plurality of cardiac images to characterize motion of a heart; computing an information-theoretic metric from the filter coefficient; and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.

Abstract: Example methods, apparatus and articles of manufacture to track endocardial motion are disclosed. A disclosed example method includes segmenting a plurality of cardiac images of a left ventricle to form respective ones of a plurality of segmented images, updating a plurality of models based on the plurality of segmented images to form respective ones of a plurality of motion estimates for the left ventricle, computing a plurality of probabilities for respective ones of the plurality of models, and computing a weighted sum of the plurality of motion estimates based on the plurality of probabilities, the weighted sum representing a predicted motion of the left ventricle.

Abstract: Methods and apparatus are disclosed to label radiology images. An example system includes a processor and a database. In response to receiving a first user input via a data input, the processor is to generate first annotations on a spinal image. The processor is to cause the spinal image having the first annotations to be displayed at a user interface. The processor is to prompt a user to provide second user input identifying an error in the first annotations. In response to receiving the second user input, the processor is to generate second annotations on the spinal image. The second annotations is to correct the error in the first annotations.

Abstract: Methods and apparatus are disclosed to automatically detect vertebrae boundaries in a spine image. A method to detect a vertebra in a spine image is described, the method comprising generating a rectangle approximation of a reference vertebra. The method also including identifying a mask similar to the rectangle approximation and labeling a mask region in the mask. The method also including comparing the mask region to the rectangle approximation and detecting a vertebra in the spine image based on the comparison.

Abstract: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes using a filter coefficient based on a plurality of cardiac images to characterize motion of a heart; computing an information-theoretic metric from the filter coefficient; and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.

Abstract: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes adapting a state of a state-space model based on a plurality of cardiac images to characterize motion of a heart, computing an information-theoretic metric from the state of the state-space model, and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.

Abstract: Image tracking as described herein can include: segmenting a first image into regions; determining an overlap of intensity distributions in the regions of the first image; and segmenting a second image into regions such that an overlap of intensity distributions in the regions of the second image is substantially similar to the overlap of intensity distributions in the regions of the first image. In certain embodiments, images can depict a heart at different points in time and the tracked regions can be the left ventricle cavity and the myocardium. In such embodiments, segmenting the second image can include generating first and second curves that track the endocardium and epicardium boundaries, and the curves can be generated by minimizing functions containing a coefficient based on the determined overlap of intensity distributions in the regions of the first image.

Abstract: Image tracking as described herein can include: segmenting a first image into regions; determining an overlap of intensity distributions in the regions of the first image, and segmenting a second image into regions such that an overlap of intensity distributions in the regions of the second image is substantially similar to the overlap of intensity distributions in the regions of the first image. In certain embodiments, images can depict a heart at different points in time and the tracked regions can be the left ventricle cavity and the myocardium. In such embodiments, segmenting the second image can include generating first and second curves that track the endocardium and epicardium boundaries, and the curves can be generated by minimizing functions containing a coefficient based on the determined overlap of intensity distributions in the regions of the first image.