Abstract: Some demonstrative embodiments include apparatuses, systems and/or methods of determining one or more parameters of a lens, For example, a computing device may be configured to process at least one depth map including depth information captured via a lens; and to determine one or more parameters of the lens based on the depth information.

Abstract: Some demonstrative embodiments include apparatuses, systems and/or methods of determining one or more parameters of a lens, For example, a computing device may be configured to process at least one depth map including depth information captured via a lens; and to determine one or more parameters of the lens based on the depth information.

Abstract: Some demonstrative embodiments include apparatuses, systems and/or methods of determining one or more parameters of a refractive error of a tested eye. For example, a computing device may be configured to process depth mapping information to identify depth information of a tested eye; and to determine one or more parameters of a refractive error of the tested eye based on the depth information of the tested eye.

Abstract: Some demonstrative embodiments include apparatuses, systems and/or methods of determining one or more parameters of a refractive error of a tested eye. For example, a computing device may be configured to process depth mapping information to identify depth information of a tested eye; and to determine one or more parameters of a refractive error of the tested eye based on the depth information of the tested eye.

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: Some demonstrative embodiments include apparatuses, systems and/or methods of determining one or more parameters of a lens, For example, a computing device may be configured to process at least one depth map including depth information captured via a lens; and to determine one or more parameters of the lens based on the depth information.

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: A method is provided for generating a three dimensional frame. The method comprises the steps of: retrieving information that relates to a plurality of images of a target captured by two image capturing devices; determining data that will be applied for analyzing objects of interests included in the captured images; calculating disparity between groups of corresponding frames, wherein each of said groups comprises frames taken essentially simultaneously by the two image capturing devices; determining an initial estimation of a disparity range for the frames included in the groups of the corresponding frames; evaluating a disparity range value for each proceeding group based on information retrieved on a dynamic basis from frames included therein, and changing the value of said disparity range when required; and applying a current value of the disparity range in a stereo matching algorithm, and generating a three-dimensional frame for each proceeding group of corresponding frames.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data, computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data, computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: There is provided a computer-implemented method for detecting a fatty liver, comprising: receiving imaging data of a computed tomography (CT) scan performed using a single source CT Scanner with settings selected for imaging of non-fatty-liver pathology, segmenting a region of the liver by creating a binary image by applying binary segmentation to a sub-set of pixels of the imaging data according to a first set-of-rules, and mapping the region of liver of the binary image to the segmented region of the portion of the liver of the imaging data, calculating liver parameter(s) for the segmented region of the liver from Hounsfield unit (HU) value(s), and detecting the presence of a fatty liver by analyzing the calculated liver parameter(s) according to a second set-of-rules.

Abstract: There is provided a computer-implemented method for detecting a fatty liver, comprising: receiving imaging data of a computed tomography (CT) scan performed using a single source CT Scanner with settings selected for imaging of non-fatty-liver pathology, segmenting a region of the liver by creating a binary image by applying binary segmentation to a sub-set of pixels of the imaging data according to a first set-of-rules, and mapping the region of liver of the binary image to the segmented region of the portion of the liver of the imaging data, calculating liver parameter(s) for the segmented region of the liver from Hounsfield unit (HU) value(s), and detecting the presence of a fatty liver by analyzing the calculated liver parameter(s) according to a second set-of-rules.

Abstract: A method is provided for generating a three dimensional frame. The method comprises the steps of: retrieving information that relates to a plurality of images of a target captured by two image capturing devices; determining data that will be applied for analyzing objects of interests included in the captured images; calculating disparity between groups of corresponding frames, wherein each of said groups comprises frames taken essentially simultaneously by the two image capturing devices; determining an initial estimation of a disparity range for the frames included in the groups of the corresponding frames; evaluating a disparity range value for each proceeding group based on information retrieved on a dynamic basis from frames included therein, and changing the value of said disparity range when required; and applying a current value of the disparity range in a stereo matching algorithm, and generating a three-dimensional frame for each proceeding group of corresponding frames.

Abstract: There is provided a computerized method for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion; segmenting the bone portion from the imaging data; computing at least one grade based on pixel associated values from the bone portion; and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan; wherein the grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: A computerized method for model-less segmentation and registration of ultrasound (US) with computed tomography (CT) images of an organ with a fluid filled chamber. The method is based on correlating between the US image(s) and the CT image(s) by processing the US image(s) by iteratively expanding the CT image segment so that the expanded CT image segment is correlated with the visual boundaries of the US image segment; transforming the CT image(s) according to an estimated US transducer position and estimated US beam direction related to the US image(s) so that at least one of shape and volume of the organ in the CT image is adapted with at least one of shape and volume of the organ of the US image, to form a CT image representation which is correlated with US image(s).