This disclosure relates generally to brain segmentation of MRI images
Segmentation is one step in the analysis of MRI (magnetic resonance imaging) images of brains. A three-dimensional MRI image of a brain typically is a three-dimensional array of voxels, where each voxel has a value (intensity) that represents the response of the matter in that voxel to the MRI imaging process. The segmentation step determines which of the voxels are brain matter and which are not. After the MRI image has been segmented, the voxels that are brain matter may then be further analyzed.
Segmentation typically is often performed based on the intensity of voxels in the MRI image. For example, in one approach, clusters of similar intensity voxels are grouped, on the assumption that similar intensity voxels represent the same type of matter. However, this is not always a good assumption.
For example, noise and intensity variations in the MRI imaging process may lead to erroneous results. Bias field variance may lead to less intense voxels on one side of the MRI image compared to the other, even for the same type of brain matter. In addition, different physiological regions may have voxels of similar intensity, and clustering voxels based on intensity would not distinguish between these different regions. For example, brain gray matter is less intense than brain white matter, but it may be similar in intensity to non-brain structures. Clustering based on intensity alone may not successfully segment the brain gray matter.
Thus, there is a need for better approaches for brain segmentation of MRI images.
In one aspect, multiscale filtering is used to normalize the intensities of voxels in an MRI image. A multiscale filter is applied to an MM image. A multiscale filter applies filters of different scales (e.g., using kernels of different sizes) to the image. In one approach, a 1×1×1 filter, a 3×3×3 filter, a 5×5×5 filter, etc. are applied and a weighted sum of these component filtered images is calculated to produce the multiscale filtered image. Luma aberrations (i.e., intensity variations) are corrected based on the comparison of the multiscale filtered image to the original image. In one approach, the intensity of the image is increased for voxels in the original image that are dimmer than in the multiscale filtered version and decreased for voxels that are brighter than the multiscale filtered version.
In another aspect, additional features are created based on multiscale gradients. These may be used in combination with other features to segment the MRI image. An MRI image is filtered using filters of different scales k. The gradient with respect to k is calculated. For each voxel, this is the variation in intensity of that voxel with respect to k. Voxels with positive gradients may represent brain gray matter bordered by brain white matter. Voxels with negative gradients may represent brain white matter bordered by brain grain matter.
The two techniques may be combined. Normalization may first be applied to an MRI image. Multiscale gradients for the normalized version may then be calculated.
Other aspects include components, devices, systems, improvements, methods, processes, applications, computer readable mediums, and other technologies related to any of the above.
Embodiments of the disclosure have other advantages and features which will be more readily apparent from the following detailed description and the appended claims, when taken in conjunction with the examples in the accompanying drawings, in which:
The figures and the following description relate to preferred embodiments by way of illustration only. It should be noted that from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the principles of what is claimed.
The process 100 of
Process 100 proceeds as follows. A multiscale filter 110 is applied to the input image A, producing the filtered image B. In one approach, the multiscale filter 110 uses filters Fk of different scales, where k is the index for scale. The input image A is filtered by each filter Fk to produce a component filtered image Bk, and a weighted sum of the Bk yields the aggregate filtered image B. Mathematically,
B=Σ
k
w
k
*B
k (1)
The different scale filters Fk may take different forms.
Multiscale filtering does not have to be based on convolution. Space-invariant filtering may be used, for example. If the scale is implemented by filters of different sizes, then each component image Bk for a voxel x will be based on the intensity values of voxels within a certain neighborhood of x, where the size of the neighborhood varies with the scale k.
Because image B is the multiscale filtered version of image A, B(x) represents some sort of “average” intensity in the local neighborhood of voxel x while A(x) is the intensity of just voxel x. In the rest of process 100, the images A and B are compared, and the intensity of image A is corrected for luma aberrations based on this comparison.
Returning to
In
The normalization mask G is then applied 135 to the original image A to yield the luma-corrected image H=G*A where the multiplication is voxel-based. That is, H(x)=G(x)*A(x).
In a different aspect,
An analogous situation occurs for gray matter bordered by white matter. When the scale is small, the voxels in the neighborhood are mostly gray matter so the filtered intensity remains lower. As the neighborhood increases in size, more white matter voxels are included. The filtered intensity increases, and there is a positive gradient of intensity with respect to scale k.
Thus, positive-gradient voxels and negative-gradient voxels and their corresponding scales k may be used as additional features for segmenting an MRI image into brain matter and not brain matter, or into gray and white brain matter.
Although the detailed description contains many specifics, these should not be construed as limiting the scope of the invention but merely as illustrating different examples. It should be appreciated that the scope of the disclosure includes other embodiments not discussed in detail above. Various other modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope as defined in the appended claims. Therefore, the scope of the invention should be determined by the appended claims and their legal equivalents.
Alternate embodiments are implemented in computer hardware, firmware, software, and/or combinations thereof. Implementations can be implemented in a computer program product tangibly embodied in a computer-readable storage device for execution by a programmable processor; and method steps can be performed by a programmable processor executing a program of instructions to perform functions by operating on input data and generating output. Embodiments can be implemented advantageously in one or more computer programs that are executable on a programmable computer system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. Each computer program can be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language can be a compiled or interpreted language. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, a processor will receive instructions and data from a read-only memory and/or a random access memory. Generally, a computer will include one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM disks. Any of the foregoing can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits), FPGAs and other forms of hardware.