In valve modeling from medical scan data, chordae are modeled as a dense structure. Rather than attempting to provide the same number of chordae (e.g., 25) as found in a human valve, hundreds or thousands of chordae connectors are used. Since solving for lengths of so many chordae may be computationally intensive, the lengths of only a few are solved, and the lengths of the rest of the chordae are derived from the lengths of the few.

A method generates and uses a correspondence probability map for visualization of two image datasets. The method includes obtaining two image datasets and obtaining an image registration algorithm that includes a correspondence model. The method further includes registering the two image datasets to generate a displacement vector field and generating a correspondence probability map, using the correspondence model, based on the two image datasets. The method further includes using the correspondence probability map to visualize the two image datasets. A computing system (120) includes a memory device (124) configured to store instructions, including a visualization module (130), and a processor (122) that executes the instructions, which causes the processor to generate and employ a correspondence probability map for visualization of two image datasets.

A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image for subsequently determining regions of change within the master image. An alert condition is determined if change within one or more of the regions of change is sufficient to meet defined criteria. If a minimum field area threshold is met, a modulation coefficient is applied to the defined criteria that determines the alert condition. The coefficient reduces area-based thresholds used in health detection in a manner proportional to the size of the agricultural field of interest.

Methods and systems for providing various skin-related metrics and tracking the effects of skincare and cosmetic products are described. The system may acquire user images via optical scanning methods and analyze the images before and after application of a product to provide quantitative feedback to the user of beneficial or adverse effects of the product. The system may track response of the skin based on changes in inflammation, dryness, elasticity, pH levels, and/or microbiomes and correlate these changes with user information including ethnicity, location, and lifestyle to generate models that are capable of predicting a user's response to certain ingredients and/or predicting long-tern effects of certain ingredients on the skin.

A medical image processing apparatus according to the present invention includes a moving direction identification unit configured to identify a moving direction of an observed region of a subject depicted in a plurality of volume data collected by a medical diagnostic apparatus, each volume data of the plurality of volume data being collected for each time phase; and a display direction setting unit configured to set a display direction of the plurality of volume data based on the identified moving direction.

A method includes displaying a first image including a living body on a display apparatus, accepting specifying of a first area on the first image, extracting a first feature point group from an area in the first image, the area being located in a distance more than a threshold from the first area, acquiring a second image including the living body, the second image being captured at different timing from the first image, extracting a second feature point group corresponding to the first feature point group, from the second image, generating, by a processor, transformation information based on a positional relationship between the first feature point group and the second feature point group, for carrying out an image registration between the second image and the first image, executing transformation processing by applying the transformation information to the second image, and displaying a third image generated by the transformation processing.

An imaging system and method for microbial growth detection, counting or identification. One colony may be contrasted in an image that is not optimal for another type of colony. The system and method provides contrast from all available material through space (spatial differences), time (differences appearing over time for a given capture condition) and color space transformation using image input information over time to assess whether microbial growth has occurred for a given sample.

A computer-implemented method and system for predicting orthodontic results based on landmark detection includes: acquiring a crown point cloud, a set of tooth point clouds, and tooth labels; extracting global dentition features and local tooth features, performing feature fusion on the global dentition features, tooth labels of individual teeth, and the local tooth features of the individual teeth to obtain fused features of the individual teeth, and extracting landmarks of the individual teeth and offset vectors from points in the tooth point clouds to the landmarks; fusing the landmarks of the individual teeth and the tooth point clouds, extracting tooth attention features, acquiring dentition attention features, and fusing the dentition attention features, the global dentition features, and the local tooth features to obtain a point cloud with fused landmarks; and acquiring pre- and post-treatment rigid transformation parameters, and obtaining a post-treatment crown model prediction result.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

A method of automatically evaluating resection accuracy, is provided. The method includes preoperatively obtaining a first image of a volume of patient tissue using an imaging modality configured according to a scanning parameter, and storing the scanning parameter. An identifier of a target region corresponding to a target portion of the volume is received and stored in association with the first image. A second image is obtained of a resected tissue sample from the volume, using the imaging modality configured according to the scanning parameter. Based on a comparison of the first image and the second image, a determination is made whether the entire target region is represented in the second image. The method includes controlling an output device to present an indication, based on the determination, of whether the tissue sample contains the entire target portion.