Abstract: A computer-implemented method of detecting an object in a three-dimensional medical image comprises determining the values of a plurality of features at each voxel in at least a portion of the medical image. Each feature characterises a respective property of the medical image at a particular voxel. The likelihood probability distribution of each feature is calculated based on the values of the features and prior medical knowledge. A probability map is generated by using Bayes' law to combine the likelihood probability distributions, and the probability map is analysed to detect an object.

Abstract: A segmentation method comprises clustering spatial, intensity and volumetric shape index to automatically segment a medical lesion. The algorithm has the following steps: (1) calculating volumetric shape index (SI) for each voxel in the image; (2) combining the SI features with the intensity range and the spatial position (x, y, z) to form a 5-dimensional feature vector set; (3) grouping the 5-dimensional feature vector set into clusters; (4) employing a modified expectation-maximization algorithm (EM) considering not only spatial but also shape features on an intensity mode map from the clustering algorithm to merge the neighbouring regions or modes. The joint spatial-intensity-shape feature provides rich information for the segmentation of the anatomic structures of interest, such as lesions or tumours.

Abstract: A computer-implemented method of detecting an object in a three-dimensional medical image comprises determining the values of a plurality of features at each voxel in at least a portion of the medical image. Each feature characterises a respective property of the medical image at a particular voxel. The likelihood probability distribution of each feature is calculated based on the values of the features and prior medical knowledge. A probability map is generated by using Bayes' law to combine the likelihood probability distributions, and the probability map is analysed to detect an object.

Abstract: A system and method for characterizing a point in a three-dimensional digital medical image includes determining third order derivatives of the three-dimensional digital medical image at one or more points in the three-dimensional digital medical image. At each of the one or more points, one or more invariant or semi-invariant features that characterize a local geometry at the point is determined using the third order derivatives of the three-dimensional digital medical image. The invariant or semi-invariant features are used for automatic detection of lesions, abnormalities, and other anatomical structures of interest in the three-dimensional digital medical image.

Abstract: A method of detecting a junction between a lesion and a wall in a CT scan image may include determining the boundary (B) of the wall to an internal space (L), identifying critical points (c1, c2) along the boundary, and selecting one critical point at either side of the lesion as a junction point between the wall and the lesion. The critical points may be points of maximum local curvature and/or points of transition between straight and curved sections of the boundary. The critical points may be selected by receiving first and second seed points (p1, p2) at either side of the lesion, moving the seed points to the boundary if they are not already located on the boundary, and finding the closest critical points to the seed points. The seed points may be determined by displacing the determined junction points (j1, j2) from an adjacent slice of the image into the current slice.

Abstract: A method of detecting the extent of a lung nodule in a scan image comprises fine segmenting (14) a region around the nodule into foreground and background, filling holes within foreground areas (16), region growing the foreground (18) to identify an initial region of interest, determining a mask (20) by enlarging the initial region to contain background and to exclude other foreground regions, determining a spatial map (26) of connectivity within the mask to a point within the initial region, region growing (28) within the mask and determining the maximum edge contrast (30) during region growing. The region of maximum edge contrast identifies the extent of the nodule (32). The position of the nodule may a seed point identified by the user (12). Most preferably, an optimum seed point is determined by iterative determination of seed points (22, 24) so that the determined extent of the nodule is substantially independent of the precise location of the seed point identified by the user.

Abstract: A method of detecting a nodule in a three-dimensional scan image comprises calculating a three-dimensional sphericity index for each point in the scan image (310-330), applying a high sphericity threshold to the sphericity index (340) to obtain a candidate nodule region, and then performing region-growing (350) from the candidate region using a relaxed sphericity threshold to determine an extended region including less spherical parts connected to the candidate region. Optionally, spherical filtering may be applied to the image by matching the spherical filter to the extended region.

Abstract: A method of detecting a nodule in a three-dimensional scan image comprises calculating a three-dimensional sphericity index for each point in the scan image (310-330), applying a high sphericity threshold to the sphericity index (340) to obtain a candidate nodule region, and then performing region-growing (350) from the candidate region using a relaxed sphericity threshold to determine an extended region including less spherical parts connected to the candidate region. Optionally, spherical filtering may be applied to the image by matching the spherical filter to the extended region.

Abstract: An enhanced image is based on an original image. For instance, the original image can be processed using a filter to provide the enhanced image. The original and enhanced images can be displayed side by side or alternately to facilitate a comparison of the original and enhanced images. A user can change one or more enhancement parameters associated with the enhanced image. For example, the user can change the one or more enhancement parameters while viewing the original and enhanced images to determine an effect the change has on the enhanced image. The user can compare the effect of the changed enhancement parameters with his or her own analysis of the original image. The changed enhancement parameters may be applied to other parts of the original image so as to provide a more accurate analysis of those parts.

Abstract: In a method of performing CAD analysis of a medical image, the medical image volume is processed in a pre-processing module to generate intermediate data, and the intermediate data is processed by a filtering module to generate CAD result data. The different modules may be executed by different processing nodes or devices, enabling a variety of different system architectures. The intermediate data may be stored in accordance with a standard, such as the Digital Imaging and Communications in Medicine (DICOM) standard. For instance, the intermediate data may be stored in accordance with the DICOM secondary capture image information object definition (SCI IOD) format. The CAD result data may be displayed on a viewer workstation, for example.

Abstract: A medical image is analyzed using an algorithm requiring input parameters. Values of the input parameters are derived from metadata, indicating properties of the medical image. For example, the metadata may indicate the type of image acquisition device or settings used to create the medical image. In another example, the metadata may relate to the patient upon whom the image is based. This allows optimum values to be selected for the parameters.

Abstract: An enhanced image is based on an original image. For instance, the original image can be processed using a filter to provide the enhanced image. The original and enhanced images can be displayed side by side or alternately to facilitate a comparison of the original and enhanced images. A user can change one or more enhancement parameters associated with the enhanced image. For example, the user can change the one or more enhancement parameters while viewing the original and enhanced images to determine an effect the change has on the enhanced image. The user can compare the effect of the changed enhancement parameters with his or her own analysis of the original image. The changed enhancement parameters may be applied to other parts of the original image so as to provide a more accurate analysis of those parts.

Abstract: A medical image is analyzed using an algorithm requiring input parameters. Values of the input parameters are derived from metadata, indicating properties of the medical image. For example, the metadata may indicate the type of image acquisition device or settings used to create the medical image. In another example, the metadata may relate to the patient upon whom the image is based. This allows optimum values to be selected for the parameters.

Abstract: A medical image is analyzed using an algorithm requiring input parameters. Values of the input parameters are derived from metadata, indicating properties of the medical image. For example, the metadata may indicate the type of image acquisition device or settings used to create the medical image. In another example, the metadata may relate to the patient upon whom the image is based. This allows optimum values to be selected for the parameters.

Abstract: An enhanced image is based on an original image. For instance, the original image can be processed using a filter to provide the enhanced image. The original and enhanced images can be displayed side by side or alternately to facilitate a comparison of the original and enhanced images. A user can change one or more enhancement parameters associated with the enhanced image. For example, the user can change the one or more enhancement parameters while viewing the original and enhanced images to determine an effect the change has on the enhanced image. The user can compare the effect of the changed enhancement parameters with his or her own analysis of the original image. The changed enhancement parameters may be applied to other parts of the original image so as to provide a more accurate analysis of those parts.