This paper presents a comparative study of automatic thresholding algorithms for segmenting trabecular bone volume in x-ray microtomography (μ CT).
First, a preprocessing stage was established, which considered noise reduction by applying anisotropic diffusion filtering and contrast enhancement by using morphological top-hats. Next, four automatic thresholding algorithms were implemented: clustering, maximum entropy, moment preservation, and concavity-based. These approaches analyze the preprocessed 3Dμ CT image histogram to optimize some parameters to find the best gray-level threshold. Thirty-eight vertebra bone samples were acquired from 19 normal Wistar rats, specifically the L3 and L4 vertebrae. The μ CT images were acquired with a microfocus x-ray device at 100 slices/sample. Next, three human operators segmented the entire 3D μ CT images manually to establish ground-truth segmentations so as to associate the segmentation problem with perceptual grouping. The normalized probabilistic Rand index (NPRI) was used to quantify the agreement between each computerized segmentation and the corresponding set of three ground-truth segmentations. Hence, the NPRI value should tend toward unity for an acceptable performance. Finally, a statistical analysis was done to determine which thresholding approach achieved the best performance. Besides, 3D morphometric indices were also measured.
The Games-Howell test (α = 0.05) was used to compare the equality of means from the NPRI results considering the four thresholding algorithms (multiple comparisons). This statistical analysis indicated that the clustering and moment preservation techniques performed similarly, with NPRI values of 0.594 ± 0.126 and 0.607 ± 0.127, respectively.
The main advantage of computerized segmentation is that it is fully automatic; that is, no interaction with the user is required. Thus, the method could be considered objective. Besides, the proposed preprocessing stage plays an important role in enhancing theμ CT image quality to achieve better separation between the background volume and the trabecular bone volume.
The authors would like thank the Brazilian agencies CNPq, CAPES, and FAPERJ for the financial support.
II. THEORETICAL FRAMEWORK
II.A. 3D μCT data organization
II.B. Image integration
II.C. Image filtering
II.D. Contrast enhancement
II.D.1. Morphological white top-hat
II.D.2. Selection of structuring element size
II.E. Image thresholding
II.E.2. Clustering technique
II.E.3. Maximum entropy technique
II.E.4. Moment preservation technique
II.E.5. Concavity-based technique
III. EXPERIMENTS AND RESULTS
III.A. Bone samples and μCTimage acquisition
III.B. Ground-truth segmentations and performance assessment
III.C. Statistical analysis of thresholding methods
III.D. Morphometric image analysis
III.E. Utility computing
- Medical imaging
- Computed tomography
- Medical image segmentation
- Cluster analysis
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