Threshold-Based Segmentation for Landmark Detection Using CBCT Images

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The aim of this study is to examine the influence of threshold-based segmentation on the mean error of automatic landmark detection in 3D CBCT images. A GUI was developed for radiologists, allowing manual landmark identification and visualization of CBCT images. After a threshold-based segmentation, a semi-automatic algorithm for landmark detection was designed using the anatomic definition of each landmark. A step of 50 Hounsfield units was used for threshold variation to assess the detection error. 5 CBCT images were used to validate the proposed approach. The measurement of error detection for one patient was influenced by the threshold variation. For this patient, the error changed from 1.49 mm to 10.32 mm at a low threshold value, while for another patient, the error changed from 1.96 mm to 12.28 mm at high a threshold value. In a CBCT scanner, the choice of threshold value for segmentation can be an important factor in causing error in measurements.

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