Samsung Electronics Co., Ltd.

Medicsight Plc

Xujiong Ye

Gregory Gibran Slabaugh

Abdel Douiri

Muhammad Musib Siddique

Gareth Richard Beddoe

Richard Graham Boyes

Xinyu Lin

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.

System and method for detection of lesions in three-dimensional digital medical image

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.

Medical image segmentation

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.

Medical image processing

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Middlesex, United Kingdom

Xujiong Ye