Abstract:
The method 1 according to the invention is preferably practiced in real time and directly after a suitable acquisition 3 of the multi-dimensional dataset, which is accessed at step 5 and the images constituting the multi-dimensional dataset are classified at step 8. Preferably, for reducing an amount of data to be processed at step 6 the image data is subjected to a restrictive region of interest determination. At step 9 the classified cardiac images are subjected to a an image thinning operator so that the resulting images comprise a plurality of connected image components which are further analyzed at step 14. After the thinning step 9 a labeling step 11 is performed, where different connected components in the multi-dimensional dataset are accordingly labeled. This step is preferably followed by a region growing step 13, which is constrained by binary threshold used at step 8b. For each connected image component a factor F is computed at step 14. The anatomic structure is segmented at step 16 by selecting the connected image component with factor F meeting a pre-determined criterion. After this, the segmented anatomic structure is stored in a suitable format at step 18. The invention further relates to an apparatus, a working station, a viewing station and a computer program.
Abstract:
The invention relates to a method 1 of image segmentation where in step 2 a prior model representative of a structure conceived to be segmented in an image is accessed. Preferably, the image comprises a medical diagnostic image. Still preferably, the medical diagnostic image is prepared in a DICOM format, whereby supplementary information is stored besides diagnostic data. In these cases the method 1 according to the invention advantageously proceeds to step 3, where the supplementary information is extracted from electronic file 5, comprising for example suitable patient-related information 5a and/or suitable structure-related information 5b. Examples of the patient-related information comprise a patient's age, sex, group, etc., whereas examples of the structure-related information may comprise an anatomic location of the structure, such as rectum, bladder, lung etc, or the suspected / diagnosed pathology of the patient. In an alternative embodiment of the method 1 according to the invention, the supplementary information is provided by a human operator in step 7, where he can enter suitable supplementary information 9a, 9b using a user interface 9. When the supplementary information is loaded, the method 1 according to the invention proceeds to step 4 in which the prior model is being changed using the supplementary information yielding a further model. In step 6 the method 1 performs the image segmentation using the thus obtained further model and in step 8 the results of the segmentation step may be visualized on a suitable viewer.
Abstract:
The method 1 according to the invention may be schematically divided into three major phases. Phase 2 comprises preparatory steps, namely the step 3 of acquiring a suitable dataset, which is then subjected to a suitable binary segmentation at step 4 results of which are being accessed at step 5. The results comprise temporally sequenced binary coded images, whereby image portions corresponding to blood are labeled as unity, the rest is set to zero. The subsequent phase 12 of the method according to the invention is directed to performing the image processing for segmenting a structure. At step 8 a computation is performed whereby a preceding binary coded image 8a corresponding to a phase from the temporal sequence is subtracted from a subsequent binary coded image 8b corresponding to a phase yielding a multi-dimensional temporal feature map 8c. At step 9 spatial positions corresponding to a certain voxel value are derived and are used to segment the structure. A pre-defined deformable shape model is accessed at step 11, which is then deformed at step 14 in accordance with spatial coordinates, derived at step 9. Preferably, the segmentation result is stored at step 16. Finally, during a further phase 22 of the method according to the invention, the segmentation results are displayed at step 18 using suitable display means. Preferably, the segmented surface is overlaid on the original data using a two- , three- or four-dimensional visualization technique. Still preferable, to ease comprehension of the result, the segmented surface is presented as a color-code in a suitable transparency mode. The invention further relates to an apparatus and a computer program for segmenting a structure in a dataset.
Abstract:
The invention relates to a data processing apparatus and a method for providing visualisation parameters controlling the display of a medical image (12). The data processing apparatus comprises a mapping component (16). The mapping component (16) is arranged to receive a current dataset (15) corresponding to the medical image and comprising a content description thereof,to compare the content description of the current dataset (15) witha content description ofa pluralityofstored datasets, to select at least one further dataset out of the plurality of stored datasets, to retrieve stored visualisation parameters corresponding to the at least one further dataset, and to prepare the retrieved visualisation parameters as the visualisation parameters controlling the display of the medical image (12).
Abstract:
The invention relates to a method of image segmentation for delineating a structure associated with a reference structure in an image. For this purpose a segmentation of the reference structure is accessed. The appearance of different tissue types is learned using the model by non parametric robust estimation that employs a fuzzy kNN classifier in two stages (outlier reduction and final estimation). The model is used to provide seed points for the segmentation. The graph cut method is adapted to perform segmentation of the sought structure. The invention further relates to a system and a computer program for image segmentation.