Automatic Rib Segmentation in CT Data
نویسندگان
چکیده
A supervised method is presented for the detection and segmentation of ribs in computed tomography (ct) data. In a first stage primitives are extracted that represent parts of the centerlines of elongated structures. Each primitive is characterized by a number of features computed from local image structure. For a number of training cases, the primitives are labeled by a human observer into two classes (rib vs. nonrib). This data is used to train a classifier. Now, primitives obtained from any image can be labeled automatically. In a final stage the primitives classified as ribs are used to initialize a seeded region growing process to obtain the complete rib cage. The method has been tested on 20 images. Of the primitives, 96.9% is classified correctly. The results of the final segmentation are satisfactory.
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