Ricoh Company, Ltd.

Srinidhi Srinivasa

Krishna Prasad Agara Venkatesha Rao

Mithun Das Gupta

A cluster-based approach and template-based approach are combined to segment brain matter from a three-dimensional MRI image of voxels. The morphological information captured by the template-based approach may be used to refine the segmentation produced by the cluster-based approach. Conversely, the 'similarity' information captured by the cluster-based approach may be used to refine the segmentation produced by the template-based approach.

Brain segmentation using clustering and templates

In one aspect, a computer-implemented method corrects a multi-sphere head model used in dipole localization for a set of magnetic field sensors (MEG sensors) by replacing ghost spheres with replacement spheres that are not ghost spheres. One type of ghost sphere completely encloses the brain volume but is so large that a center of the sphere is outside the brain volume. Another type of ghost sphere lies entirely outside the brain volume. Various approaches for correcting ghost spheres are disclosed.

Multi-sphere head model for dipole localization

Multi-sphere head model for dipole localization without ghost spheres

A system assists users in time and frequency analysis of magnetoencephalography (MEG) signals. In one aspect, a system includes an analysis module, a configuration module and a user interface. The analysis module performs a time and frequency analysis of the MEG signal, for example a short time Fourier transform (STFT) or a continuous wavelet transform (CWT) analysis. The analysis is parameterized by a parameter set that affects the time and frequency resolution of the analysis, for example window size and overlap size for STFT or center frequency and decay parameter for CWT. The configuration module automatically determines or assists the user to determine correct values for the parameter set.

Intelligent parameterization of time-frequency analysis of encephalography signals

A surface model is generated from a three-dimensional volume model of a person's head. The person's head is modelled as a three-dimensional volume model of loss values (i.e., absorption values). Wave vectors are launched towards the volume model. Each wave vector is characterized by a wavelength and a capture direction (direction of propagation). The launched wave vectors are absorbed by the volume and the point at which they are absorbed (referred to as the intersection point) is determined. The surface model of the person's head is generated from the intersection points of the wave vectors.

Volume surface generator using variable wave vectors

Volume surface generator using wave vectors

The location of the optical axis of a plenoptic imaging system is determined. In one approach, the plenoptic image is noise filtered, down-sampled, low-pass filtered, and again noise filtered. Slices through this image that have the highest power are used to determine the location of the optical axis. Once the location of the optical axis is determined, corrections for aberrations such as distortion can be applied.

Optical center detection in plenoptic imaging systems

One aspect determines centroids of a plenoptic image, which correspond to the center view of the plenoptic image. A search band spanning a range of different values along a search direction (e.g., range of &#x394;y) is determined. Multiple slices of the plenoptic image are taken at different values of the search coordinate (i.e., different y values). Because the plenoptic image has a periodic structure imposed by the periodicity of the microlens array, each slice will have a strong frequency component at this fundamental frequency. A slice corresponding to the centroid location (i.e., value of y at the centroid) is selected based on analysis of these frequency components. In one approach, the slice with the weakest component at this fundamental frequency is selected as the centroid. A similar approach can be performed in the orthogonal direction to obtain the two-dimensional coordinates of the centroids.

Automatic centroid determination of microlens arrays in plenoptic imaging systems

Embodiments segment received image data. The received image data may include pixels that have multiple channels of intensity values. The image data is decomposed into albedo and shading components. This may be accomplished using a minimization that enforces a relationship between albedo, shading, and intensity values. The minimization may also include an albedo regularizer to infer albedo in part based on chromaticity and albedo of surrounding pixels. Superpixels are generated based on contiguous regions of pixels having similar image data across channels. These superpixels are then merged based in part on the determined albedo and shading components as well as based on the image data. The channels of image data may include infrared image data used to modify visible channels of the image to create pseudo-image data, which may be used in place of image data for albedo-shading decomposition, superpixel generation, or superpixel merging.

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Bengaluru, India

Srinidhi Srinivasa