Article Figures & Data
Figures
- Fig 1.
Steps in the correspondence analysis algorithm.
A and B, Pair of registered CT (A) and MR imaging (B) datasets.
C, Subvolume SCT containing the segmented cortical bone structures of the original CT image.
D, Result of registration assessment overlaid onto the original MR image.
- Fig 2.
Bone Segmentation in MR Images (triple application of Otsu method).
A, Gray-value histogram of MR image in Figure 1B. Thresholds t1, t2, and t3 are obtained by triple application of the Otsu threshold selection method.
B, inverted MR image after application of the upper threshold t1, which does not clearly separate all of the soft tissues from the lower-intensity classes.
C, Zoomed view of histogram in A.
D, Inverted MR image after application of the upper threshold t3. Soft tissues are effectively removed. What remains are the low-intensity-classes, including bone structures, air, and CSF.
- Fig 3.
Three fused CT–MR patient datasets assessed with our algorithm. Green voxels represent safe regions with high registration accuracy; red voxels represent unsafe regions with low registration accuracy.
A, Patient A.
B, Patient B.
C, Patient C.
- Fig 4.
Segmentation results for a simulated MR volume.
A, Segmentation result for bone regions on the MR image (axial, coronal, and sagittal).
B, True bone regions as provided by the MR simulator (axial, coronal, and sagittal).
- Fig 5.
Validation of the registration assessment results based on simulated CT–MR data shifted purposely. Colored bars represent the percentage of correctly or falsely classified voxels as: safe (green) or unsafe (red). Results show that the algorithm is optimistic (some false-safe voxels exist), but there is practically no false detection (< 0.3%) of unsafe voxels.
A, Horizontal shift by 0–5 mm.
B, Vertical shift by 0–5 mm.
- Fig 6.
Evaluation of the results of the registration assessment algorithm on real data.
A, Color-coded result of the assessment of a well-registered CT–MR image pair.
B, Same CT–MR pair, with a purposely introduced relative horizontal shift of five voxels.
C, Relative horizontal shift of 10 voxels.
- Fig 7.
Algorithm evaluation on a CT–MR image subvolume.
A, Small 3D subvolume extracted from a larger registered CT–MR image pair. Color-coded overlay shows the registration assessment result for bone structures.
B, CT and MR subvolumes are reregistered to each other by using a mutual information algorithm. The reregistered subvolumes show an improved registration assessment result, as the low-accuracy (red) region is greatly diminished.
Tables
Assessment of the bone segmentation algorithm on simulated MR images at different noise and intensity nonuniformity levels
Noise (%)* Intensity Nonuniformity (%)* rbone (%)† 0 0 99.98 0 20 99.93 3 0 99.74 3 20 99.70 5 0 99.55 5 20 99.45 7 0 99.00 7 20 97.28 9 0 14.96 9 20 13.65 Note.—Segmentation fails above 7% noise.
* Percentages refer to the full intensity scale.
† Percentage of bone voxels that were segmented.
