Abstract: Methods and systems related to dynamic visualization of a representation of a three dimensional object are provided. In some embodiments, a computer system accesses a volumetric dataset representing the object. The computer system applies a set of one or more light rays/vectors to the volumetric data-set representing the object and generates a plurality of intermediate images, each intermediate image of the plurality of intermediate images corresponding to each of the one or more light vectors applied to the volumetric data-set representing an object. The computer system generates an accumulated image based on the plurality of intermediate images. The computer system determines whether a user has manipulated the accumulated image within a predetermined amount of time and in accordance with a determination that the user has manipulated the accumulated image within a predetermined amount of time, updates the accumulated image based on a user manipulation.

Abstract: Methods and systems related to image segmentation are disclosed. In some examples, a computer system obtains segmentation parameters based on a selection of a region of a displayed image. The selection of the region is associated with a select signal generated by an input device. In response to obtaining the segmentation parameters, the computer system processes the image based on the segmentation parameters. The computer system further adjusts the segmentation parameters based on one or more move signals generated by the input device. The move signal is associated with moving of a representation of the input device within the image. The computer system processes the image based on the one or more adjusted segmentation parameters and displays an image of the selected region based on the processing of the image using the adjusted segmentation parameters.

Abstract: Methods and systems related to dynamic visualization of a representation of a three dimensional object are provided. In some embodiments, a computer system accesses a volumetric dataset representing the object. The computer system applies a set of one or more light rays/vectors to the volumetric data-set representing the object and generates a plurality of intermediate images, each intermediate image of the plurality of intermediate images corresponding to each of the one or more light vectors applied to the volumetric data-set representing an object. The computer system generates an accumulated image based on the plurality of intermediate images. The computer system determines whether a user has manipulated the accumulated image within a predetermined amount of time and in accordance with a determination that the user has manipulated the accumulated image within a predetermined amount of time, updates the accumulated image based on a user manipulation.

Abstract: Methods and apparatus for bit prediction and data compression are provided. In the bit prediction method and apparatus, a specific Suffix Trie is used to maintain a statistical model to predict the next bit in a bit-stream. The statistic model provides the probability for each next or following bit in the bit stream, where an entropy encoder/decoder further encodes/decodes the predicted bit. The algorithm simplicity and its high performance relies on the combination of the specific method of Suffix Trie construction and growing, and the specific way to compute, update and propagate the probability across Suffix Trie nodes. The specific and particular method to grow the Suffix Trie in conjunction with the specific method to compute, update, and propagate the probability across Suffix Trie nodes are key aspects and subject matter of the present invention.

Abstract: Processes and systems in computer enabled imaging for the mapping of volume rendering colors upon polygonal model objects applied to computer enabled volume rendering by means of mapping or encoding the color of volume rendering data upon polygonal model objects located inside volumetric data. Exemplary processes and systems including assigning the rendering result of voxels at or near the surface of the polygonal object.

Abstract: Processes and systems for computer enabled volume data rendering, and more particularly for volume rendering of multiple classificated volume datasets using an Interpolation-Classification (IC) order are provided. Further, an octree min/max can be used for volume rendering with the multiple classifications and at the same time applying the IC order to visualize the multiple classifications volume rendering.

Abstract: Methods and systems related to image segmentation are disclosed. In some examples, a computer system obtains segmentation parameters based on a selection of a region of a displayed image. The selection of the region is associated with a select signal generated by an input device. In response to obtaining the segmentation parameters, the computer system processes the image based on the segmentation parameters. The computer system further adjusts the segmentation parameters based on one or more move signals generated by the input device. The move signal is associated with moving of a representation of the input device within the image. The computer system processes the image based on the one or more adjusted segmentation parameters and displays an image of the selected region based on the processing of the image using the adjusted segmentation parameters.

Abstract: Processes and systems in computer enabled imaging for the mapping of volume rendering colors upon polygonal model objects applied to computer enabled volume rendering by means of mapping or encoding the color of volume rendering data upon polygonal model objects located inside volumetric data. Exemplary processes and systems including assigning the rendering result of voxels at or near the surface of the polygonal object.

Abstract: Processes and systems for computer enabled volume data rendering, and more particularly for volume rendering of multiple classificated volume datasets using an Interpolation-Classification (IC) order are provided. Further, an octree min/max can be used for volume rendering with the multiple classifications and at the same time applying the IC order to visualize the multiple classifications volume rendering.

Abstract: An adaptive image volume rendering system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree structure, wherein each sub-volume is associated with one node on the octree. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes. The ray energy reflected by each sub-volume is estimated using a modified Phong illumination model, constituting a pixel value at the ray origin on the 2-D image plane. Finally, the system interpolates pixel values at a plurality of selected locations and generates a 2-D image of the 3-D dataset.

Abstract: An adaptive image volume rendering system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree structure, wherein each sub-volume is associated with one node on the octree. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes. The ray energy reflected by each sub-volume is estimated using a modified Phong illumination model, constituting a pixel value at the ray origin on the 2-D image plane. Finally, the system interpolates pixel values at a plurality of selected locations and generates a 2-D image of the 3-D dataset.

Abstract: The adaptive MIP ray casting system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree data structure with each sub-volume being associated with one node of the octree data structure. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes and identifies the maximum data value along the ray path. The maximum data value is then converted into a pixel value on the 2-D image plane. Finally, the system interpolates pixel values at those locations where no pixel value is generated by ray casting and thereby generates a 2-D image of the 3-D dataset.

Abstract: The adaptive MIP ray casting system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree data structure with each sub-volume being associated with one node of the octree data structure. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes and identifies the maximum data value along the ray path. The maximum data value is then converted into a pixel value on the 2-D image plane. Finally, the system interpolates pixel values at those locations where no pixel value is generated by ray casting and thereby generates a 2-D image of the 3-D dataset.

Abstract: An adaptive image volume rendering system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree structure, wherein each sub-volume is associated with one node on the octree. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes. The ray energy reflected by each sub-volume is estimated using a modified Phong illumination model, constituting a pixel value at the ray origin on the 2-D image plane. Finally, the system interpolates pixel values at a plurality of selected locations and generates a 2-D image of the 3-D dataset.

Abstract: Method and apparatus in computer enabled imaging for user control of the type of lighting applied to computer enabled volume rendering by means of an extended transfer function, by adding to the transfer function an additional user controlled parameter which explicitly specifies the type of lighting which is to be applied for all correspondent sample values.

Abstract: An adaptive image volume rendering system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree structure, wherein each sub-volume is associated with one node on the octree. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes. The ray energy reflected by each sub-volume is estimated using a modified Phong illumination model, constituting a pixel value at the ray origin on the 2-D image plane. Finally, the system interpolates pixel values at a plurality of selected locations and generates a 2-D image of the 3-D dataset.

Abstract: An adaptive image volume rendering system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree structure, wherein each sub-volume is associated with one node on the octree. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes. The ray energy reflected by each sub-volume is estimated using a modified Phong illumination model, constituting a pixel value at the ray origin on the 2-D image plane. Finally, the system interpolates pixel values at a plurality of selected locations and generates a 2-D image of the 3-D dataset.

Abstract: An adaptive image volume rendering system first fragments a 3-D dataset into multiple sub-volumes and constructs an octree structure, wherein each sub-volume is associated with one node on the octree. The system then establishes a 2-D image plane and selectively launches a plurality of rays towards the 3-D dataset, each ray adaptively interacting with a subset of the sub-volumes. The ray energy reflected by each sub-volume is estimated using a modified Phong illumination model, constituting a pixel value at the ray origin on the 2-D image plane. Finally, the system interpolates pixel values at a plurality of selected locations and generates a 2-D image of the 3-D dataset.