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 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: 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.