Embodiments discussed herein facilitate determining a diagnosis and/or prognosis for prostate cancer based at least in part on three-dimensional (3D) pathomic feature(s). One example embodiment comprises a computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: accessing a three-dimensional (3D) optical image volume comprising a prostate gland of a patient; segmenting the prostate gland of the 3D optical image volume; extracting one or more features from the segmented prostate gland, wherein the one or more features comprise at least one 3D pathomic feature; and generating, via a model based at least on the one or more features, one or more of the following based at least on the extracted one or more features: a classification of the prostate gland as one of benign or malignant, a Gleason score associated with the prostate gland, or a prognosis for the patient.

Embodiments discussed herein facilitate determining a diagnosis and/or prognosis for prostate cancer based at least in part on three-dimensional (3D) pathomic feature(s). One example embodiment comprises a computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: accessing a three-dimensional (3D) optical image volume comprising a prostate gland of a patient; segmenting the prostate gland of the 3D optical image volume; extracting one or more features from the segmented prostate gland, wherein the one or more features comprise at least one 3D pathomic feature; and generating, via a model based at least on the one or more features, one or more of the following based at least on the extracted one or more features: a classification of the prostate gland as one of benign or malignant, a Gleason score associated with the prostate gland, or a prognosis for the patient.

A method for generating semantic information may include obtain a scanning image. The scanning image may include a plurality of pixels representing an anatomical structure. The method may also include obtain a trained segmentation model. The method may further include determine a location probability distribution of the anatomical structure in the scanning image based on the trained segmentation model. The method may also include generate a segmentation result related to the anatomical structure based on the location probability distribution. The method may further include save the segmentation result into a tag of a digital imaging and communications in medicine (DICOM) file.

A method of determining macrotexture of an object is disclosed which includes obtaining a plurality of stereo images from an object by an imaging device, generating a coordinate system for each image of the plurality of stereo images, detecting one or more keypoints each having a coordinate in each image of the plurality of stereo images, wherein the coordinate system is based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, generating a sparse point cloud based on the one or more keypoints, reconstructing a 3D dense point cloud of the object based on the generated sparse point cloud and based on neighboring pixels of each of the one or more keypoints and calculating the coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.

A method of determining macrotexture of an object is disclosed which includes obtaining a plurality of stereo images from an object by an imaging device, generating a coordinate system for each image of the plurality of stereo images, detecting one or more keypoints each having a coordinate in each image of the plurality of stereo images, wherein the coordinate system is based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, generating a sparse point cloud based on the one or more keypoints, reconstructing a 3D dense point cloud of the object based on the generated sparse point cloud and based on neighboring pixels of each of the one or more keypoints and calculating the coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.

The disclosed devices, systems and methods relate to various devices, systems and methods related to objectively analyzing digital images of turfgrass to rate various parameters and to objectively measure overall quality. The system establishes thresholds and may execute a series of steps to determine green coverage, color, density, and uniformity. The system can scale images to determine uniformity.

First and second sets of appearance attributes are obtained. The first set is associated with a target material. It comprises measured appearance attributes (54) of the target material. The second set is associated with a candidate material. It comprises candidate appearance attributes that are based on appearance attributes associated with one or more reference materials. A geometric model of at least one virtual object (72) is obtained, the geometric model defining a three-dimensional surface geometry. A scene comprising the at least one virtual object (72) is visualized. First and second portions of the virtual object (72) are visualized using the first and second sets of appearance attributes, respectively. Each of the first and second sets of appearance attributes comprises texture attributes in the form of a plurality of sets of image data. The image data in one of the sets may be based on texture attributes of the other set, or the image data in the second set may be synthesized from image data associated with a plurality of constituent materials of the candidate material.

First and second sets of appearance attributes are obtained. The first set is associated with a target material. It comprises measured appearance attributes (54) of the target material. The second set is associated with a candidate material. It comprises candidate appearance attributes that are based on appearance attributes associated with one or more reference materials. A geometric model of at least one virtual object (72) is obtained, the geometric model defining a three-dimensional surface geometry. A scene comprising the at least one virtual object (72) is visualized. First and second portions of the virtual object (72) are visualized using the first and second sets of appearance attributes, respectively. Each of the first and second sets of appearance attributes comprises texture attributes in the form of a plurality of sets of image data. The image data in one of the sets may be based on texture attributes of the other set, or the image data in the second set may be synthesized from image data associated with a plurality of constituent materials of the candidate material.

A computer-implemented method for identifying an effect pigment, the method comprising executing, on at least one processor of at least one computer, steps of: a) acquiring sample image data describing a digital image of a layer comprising a sample effect pigment b) determining, based on the sample image data, sparkle point data describing a sample distribution of sparkle points defined by the digital image, wherein the sample distribution is defined in an N-dimensional color space, wherein N is an integer value equal to or larger than 3; c) determining, based on the sparkle point data, sparkle point transformation data describing a transformation of the sample distribution into an (N−1)-dimensional color space; d) determining, based on the sparkle point transformation data, sparkle point distribution geometry data describing a geometry of the sample distribution; e) acquiring reference distribution geometry data describing a geometry of a reference distribution of sparkle points in the (N−1)-dimensional color space; f) acquiring reference distribution association data describing an association between the reference distribution and an identifier of the reference distribution; g) determining, based on the sparkle point distribution geometry data and the reference distribution geometry data and the reference distribution association data, sample pigment identity data describing an identity of the sample effect pigment.

A computer-implemented method for identifying an effect pigment, the method comprising executing, on at least one processor of at least one computer, steps of: a) acquiring sample image data describing a digital image of a layer comprising a sample effect pigment b) determining, based on the sample image data, sparkle point data describing a sample distribution of sparkle points defined by the digital image, wherein the sample distribution is defined in an N-dimensional color space, wherein N is an integer value equal to or larger than 3; c) determining, based on the sparkle point data, sparkle point transformation data describing a transformation of the sample distribution into an (N−1)-dimensional color space; d) determining, based on the sparkle point transformation data, sparkle point distribution geometry data describing a geometry of the sample distribution; e) acquiring reference distribution geometry data describing a geometry of a reference distribution of sparkle points in the (N−1)-dimensional color space; f) acquiring reference distribution association data describing an association between the reference distribution and an identifier of the reference distribution; g) determining, based on the sparkle point distribution geometry data and the reference distribution geometry data and the reference distribution association data, sample pigment identity data describing an identity of the sample effect pigment.