A study on the feasibility of active contours on automatic CT bone segmentation

Abstract

Automatic bone segmentation of computed tomography (CT) images is an important step in image-guided surgery that requires both high accuracy and minimal user interaction. Previous attempts include global thresholding, region growing, region competition, watershed segmentation, and parametric active contour (AC) approaches, but none claim fully satisfactory performance. Recently, geometric or level-set-based AC models have been developed and appear to have characteristics suitable for automatic bone segmentation such as initialization insensitivity and topology adaptability. In this study, we have tested the feasibility of five level-set-based AC approaches for automatic CT bone segmentation with both synthetic and real CT images: namely, the geometric AC, geodesic AC, gradient vector flow fast geometric AC, Chan-Vese (CV) AC, and our proposed density distance augmented CV AC (Aug. CV AC). Qualitative and quantitative evaluations have been made in comparison with the segmentation results from standard commercial software and a medical expert. The first three models showed their robustness to various image contrasts, but their performances decreased much when noise level increased. On the contrary, the CV AC's performance was more robust to noise, yet dependent on image contrast. On the other hand, the Aug. CV AC demonstrated its robustness to both noise and contrast levels and yielded improved performances on a set of real CT data compared with the commercial software, proving its suitability for automatic bone segmentation from CT images.

Fig 1

Typical CT bone images with challenging obstacles for accurate segmentation such as a weak edges, b gaps and texture areas, and c blurred interbone regions as indicated with boxes.

Fig 2

All possible positions of the curve. When it is on the boundary of the object, the “fitting” term is minimized.

Fig 3

Real CT data set.

Fig 4

Bone regions delineated by a medical expert, used as ground truths.

Fig 5

Topology adaptability and initialization sensitivity. Left to right: initialization, geometric–geodesic–GVF-Geo, CV, and Aug. CV ACs. The latter two models succeed with all three types of initialization, whereas the others fail with the cross-type.

Fig 6

Performance at different contrast levels. Upper row: contrast = 13%, and lower row: contrast = 1%. The ground truth is shown in Figure 4b.

Fig 7

Plots of error means vs contrast level. Mean value is calculated over 16 samples at each contrast level.

Fig 8

Performance at different noise levels. Upper row: SNR = 30 dB, lower row: 10 dB. The ground truth is shown in Figure 4b.

Fig 9

Plots of error means vs SNR. Mean value is calculated over 16 samples at each SNR.

Fig 10

Two-sample segmentation results, whose corresponding ground truths are shown in Figure 4. The last two AC models generate visually satisfactory results, whereas the others fail.

Fig 11

Qualitative comparison with the commercial software. The contours from the 3D-DOCTOR software contain cross-over parts (see the arrows), whereas those from the AC models do not.