Abstract: A method of sending a medical data file from an external device to a computer system and storing the data file associated with a patient's identity, comprising: a) the computer system receiving information that a data file to be sent to the computer system from the external device (or from one of a set of external devices) is to be associated with the patient's identity; b) sending the data file from the external device to the computer system, with the data file identified as coming from the external device; c) the computer system finding the patient's identity from the identification of the image file as coming from the external device, and from the information that a data file to be associated with the patient's identity was to be sent from that external device; and d) the computer system storing the data file, associating the data file with the patient's identity.

Abstract: Spatial segmentation of lymph nodes in a 3-D medical image is automatically determined, based on a set of inputs provided by a user which define a low number of initial conditions for segmentation. In some embodiments, the automation comprises producing a lymph node segmentation from the 3-D image based on a 2-D image slice and a representative line segment on that slice. In some embodiments, segmentation comprises a two tiered approach (2-D segmentation, followed by 3-D segmentation) based on adaptation of the level set framework to the particular conditions of lymph node segmentation.

Abstract: A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.

Abstract: A system and method for performing DSA (digital subtraction angiography), which does not require a non-enhanced or “mask” image to be obtained.

Abstract: A system and method for automatic detection of an object feature, such as a lesion, across a plurality of sets of image data, taken from the same subject, which may optionally be a human patient but which may also optionally be any type of animal or a non-biological subject.

Abstract: A method and system which displays a single image after a plurality of windows have been applied to image data according to one or more organ masks. The method is accomplished by collecting a radiological scan from an imaging device to form image data; obtaining an anatomically significant mask for said image data; determining a portion of said image data as background; applying a plurality of windows to said image data determined according to said anatomically significant mask and according to said background to determine display values of said image data; and generating a generated display image from said display values.

Abstract: A method for error correction for segmented three dimensional image data. The method includes receiving segmented three dimensional image data, the segmented three dimensional image data being divided into a plurality of slices; correcting at least one contour of the segmented three dimensional image data on at least one slice according to a command from a user to form a corrected contour; and automatically interpolating a correction represented by the corrected contour to a plurality of slices of the segmented three dimensional image data.

Abstract: A system and method for performing DSA (digital subtraction angiography), which does not require a non-enhanced or “mask” image to be obtained.

Abstract: A system and method for automatic detection of an object feature, such as a lesion, across a plurality of sets of image data, taken from the same subject, which may optionally be a human patient but which may also optionally be any type of animal or a non-biological subject.

Abstract: A method for error correction for segmented three dimensional image data. The method includes receiving segmented three dimensional image data, the segmented three dimensional image data being divided into a plurality of slices; correcting at least one contour of the segmented three dimensional image data on at least one slice according to a command from a user to form a corrected contour; and automatically interpolating a correction represented by the corrected contour to a plurality of slices of the segmented three dimensional image data.