Abstract: Described is a method for segmenting an anatomical part, including identifying a landmark on a contour/surface of a 2D/3D model of the anatomical part in an ultrasound image, assigning a weight to the landmark, identifying different appearance patterns of a region around the landmark based on a previously stored training set; and applying a filter to the different appearance patterns of the region around the landmark in order to identify contours of the anatomical part.

Abstract: Systems, apparatuses, and methods for diagnosing ureteropelvic junction obstruction. A set of biomarkers may be extracted from each of one or more time-activity curves associated with diuresis renography and/or functional magnetic resonance urography of one or more kidneys of a patient. One or more calculations can be performed based on the set of biomarkers to identify uretero-pelvic junction obstruction and a classification of severity or criticality thereof.

Abstract: Image segmentation of a structure of complex shape proceeds by acquiring by circuitry, a plurality of training images of the structure. A shape model of the structure is formed by fusing the plurality of training images. The shape model is partitioned by circuitry into a predetermined number of portions, an appearance and shape of each portion being determined by a classifier.

Abstract: Systems, apparatuses, and methods for diagnosing ureteropelvic junction obstruction. A set of biomarkers may be extracted from each of one or more time-activity curves associated with diuresis renography and/or functional magnetic resonance urography of one or more kidneys of a patient. One or more calculations can be performed based on the set of biomarkers to identify uretero-pelvic junction obstruction and a classification of severity or criticality thereof.

Abstract: Image segmentation of a structure of complex shape proceeds by acquiring by circuitry, a plurality of training images of the structure. A shape model of the structure is formed by fusing the plurality of training images. The shape model is partitioned by circuitry into a predetermined number of portions, an appearance and shape of each portion being determined by a classifier.

Abstract: Described is a method for segmenting an anatomical part, including identifying a landmark on a contour/surface of a 2D/3D model of the anatomical part in an ultrasound image, assigning a weight to the landmark, identifying different appearance patterns of a region around the landmark based on a previously stored training set; and applying a filter to the different appearance patterns of the region around the landmark in order to identify contours of the anatomical part.

Abstract: An image analysis device includes circuitry that receives one or more input images and detects a plurality of anatomical landmarks on the one or more input images using a pre-determined face model. The circuitry extracts a plurality of geometric and local texture features based on the plurality of anatomical landmarks. The circuitry selects one or more condition-specific features from the plurality of geometric and local texture features. The circuitry classifies the one or more input images into one or more conditions based on the one or more condition-specific features.

Abstract: Discussed herein is an image processing device and a corresponding method for assessing inflammatory bowel diseases. A plurality of dynamic MRI sequence of images of a bowel that include peristalsis information of the bowel and static MRI image(s) of the bowel that identify at areas of inflammation, stenosis, and increased mural thickness of the bowel are processed. A displacement of the bowel caused by respiration of the patient is conducted on the dynamic MRI sequences by performing a non-rigid registration. A peristaltic activity is determined by performing motion flow analysis on the dynamic sequences of images. A peristaltic activity of the static MRI image(s) based on a weighted sum of two closest dynamic MRI sequences of images is computed and the static image is combined with the closest dynamic MRI sequence of images by performing a multi-modal registration.

Abstract: An image analysis device includes circuitry that receives one or more input images and detects a plurality of anatomical landmarks on the one or more input images using a pre-determined face model. The circuitry extracts a plurality of geometric and local texture features based on the plurality of anatomical landmarks. The circuitry selects one or more condition-specific features from the plurality of geometric and local texture features. The circuitry classifies the one or more input images into one or more conditions based on the one or more condition-specific features.

Abstract: An image analysis device includes circuitry that receives one or more input images and detects a plurality of anatomical landmarks on the one or more input images using a pre-determined face model. The circuitry extracts a plurality of geometric and local texture features based on the plurality of anatomical landmarks. The circuitry selects one or more condition-specific features from the plurality of geometric and local texture features. The circuitry classifies the one or more input images into one or more conditions based on the one or more condition-specific features.

Abstract: Discussed herein is an image processing device and a corresponding method for assessing inflammatory bowel diseases. A plurality of dynamic MRI sequence of images of a bowel that include peristalsis information of the bowel and static MRI image(s) of the bowel that identify at areas of inflammation, stenosis, and increased mural thickness of the bowel are processed. A displacement of the bowel caused by respiration of the patient is conducted on the dynamic MRI sequences by performing a non-rigid registration. A peristaltic activity is determined by performing motion flow analysis on the dynamic sequences of images. A peristaltic activity of the static MRI image(s) based on a weighted sum of two closest dynamic MRI sequences of images is computed and the static image is combined with the closest dynamic MRI sequence of images by performing a multi-modal registration.

Abstract: An image analysis device includes circuitry that receives one or more input images and detects a plurality of anatomical landmarks on the one or more input images using a pre-determined face model. The circuitry extracts a plurality of geometric and local texture features based on the plurality of anatomical landmarks. The circuitry selects one or more condition-specific features from the plurality of geometric and local texture features. The circuitry classifies the one or more input images into one or more conditions based on the one or more condition-specific features.