Calgary Scientific Inc.

Ross Mitchell

Sylvia Drabycz

Torin Arni Taerum

Hongmei Zhu

Sonny Chan

Mario Costa Sousa

Charles Robert Pinnegar

Faramarz Famil Samavati

Alan Law

Yunyan Zhang

The present invention relates to a method and system for processing a volumetric dataset for providing close-up visualization of a subset therefrom. A volumetric dataset is processed based on a dual access gradient quantization data structure. The data structure is generated in a fast pre-processing stage and provides substantially immediate access to a quantized gradient using either an index or an arbitrary normalized vector. The method provides a clear, enlarged high-resolution image of a user selected region of interest at interactive rates and allows the user to freely move and visualize the region of interest within the volumetric dataset and with any orientation.

Super resolution contextual close-up visualization of volumetric data

The present invention relates to a method and system for processing time series signal data or image signal data indicative of a characteristic of an object. Received signal data are transformed into second signal data within a Stockwell domain based upon a sparse approximation of a S-transform of the signal data. The second signal data are then processed within the Stockwell domain to extract features therefrom. The processing includes determination of local spectra at predetermined locations; determination of voices at predetermined frequencies; and filtering of the signal data using a filter function in dependence upon frequency and time or space. The signal processing method and system according to the invention enables signal processing based on the S-transform using a desktop computer or workstation.

Method and system for signal processing using a sparse approximation of the S-transform

The present invention relates to a method and system for segmenting texture of multi-dimensional data indicative of a characteristic of an object. Received multi-dimensional data are transformed into second multi-dimensional data within a Stockwell domain based upon a polar S-transform of the multi-dimensional data. Principal component analysis is then applied to the second multi-dimensional data for generating texture data characterizing texture around each data point of the multi-dimensional data. Using a classification process the data points of the multi-dimensional data are partitioned into clusters based on the texture data. Finally, a texture map is produced based on the partitioned data points. The present invention provides image texture segmentation based on the polar S-transform having substantially reduced redundancy while keeping maximal data variation.

Image texture segmentation using polar S-transform and principal component analysis

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Calgary, Canada

Ross Mitchell