[DEF & CIT] “Segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as colour, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s)”. (Source: Wikipedia). [DIV] The segmentation dataset that is the result of a segmentation can be superimposed with the dataset « to be segmented ». If a segmentation has for data several datasets, then they must all be superimposed together. Segmentations can be categorised according to several semantic axis: the first focuses on the kind of approach being used, e.g. boundary-based or region-based; the second focuses on the anatomical structures being segmented, e.g. lesions, brain etc. A third axis will be added later, focusing on the dimensionality of the Segmentation dataset produced.

http://neurolog.unice.fr/ontoneurolog/v3.0/ontoneurolog-dataset-processing.owl#segmentation

[DEF & CIT] “Segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as colour, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s)”. (Source: Wikipedia). [DIV] The segmentation dataset that is the result of a segmentation can be superimposed with the dataset « to be segmented ». If a segmentation has for data several datasets, then they must all be superimposed together. Segmentations can be categorised according to several semantic axis: the first focuses on the kind of approach being used, e.g. boundary-based or region-based; the second focuses on the anatomical structures being segmented, e.g. lesions, brain etc. A third axis will be added later, focusing on the dimensionality of the Segmentation dataset produced.

segmentation

segmentation

http://neurolog.unice.fr/ontoneurolog/v3.0/ontoneurolog-dataset-processing.owl#dataset-processing