01-10-2015 дата публикации
Номер: US20150279034A1
Принадлежит:
Image processing techniques may include a methodology for normalizing medical image and/or voxel data captured under different acquisition protocols and a methodology for suppressing selected anatomical structures from medical image and/or voxel data, which may result in improved detection and/or improved rendering of other anatomical structures. The technology presented here may be used, e.g., for improved nodule detection within computed tomography (CT) scans. While presented here in the context of nodules within the lungs, these techniques may be applicable in other contexts with little modification, for example, the detection of masses and/or microcalcifications in full field mammography or breast tomosynthesis based on the suppression of glandular structures, parenchymal and vascular structures in the breast. 1. A method of obtaining one or more image components from data representing one or more images or image volumes , the method comprising:normalizing and pre-processing the data to obtain processed data;extracting features from the processed data to obtain a set of extracted features; andperforming model-based prediction using at least one model based on the set of extracted features to predict one or more components based on a set of target data.2. The method of claim 1 , further comprising: obtaining a prediction output with one or more of the components removed.3. The method of claim 1 , further comprising:subtracting one or more components predicted by the model-based prediction from the data to obtain data with the one or more components removed.4. The method of claim 1 , wherein the data comprises a radiographic CT series claim 1 , and wherein one or more components comprise only vascular components;5. The method of claim 1 , wherein the data comprises a radiographic CT series claim 1 , and wherein one or more of the components comprise only nodular structures.6. The method of claim 1 , further comprising inserting simulated nodules claim 1 , measured ...
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