# An Efficient Automatic Midsagittal Plane Extraction in Brain MRI
## 3 Results and Discussion
### Evaluation and Comparison on Real Datasets  
The proposed method has also been compared with [PERSON] et al. method on real brain MRIs. Both techniques have been tested on 125 real head scans of the NFBS database [48] and 18 real head volumes of the IBSR database [49]. No GT for MSP is available for NFBS. Therefore, only visually comparison of the MSP extraction results has been reported for both the algorithms. The first row of Figure 12 shows some of the input image slices orthogonal to MSP.  
Extracted MSP (green lines) using the proposed algorithm is displayed in the second row of Figure 12. Blue lines in the third row of Figure 12 displays the MSP extraction results of [PERSON] et al. algorithm. The same pattern of the results in detecting MSP is shown by [PERSON] et al. algorithm in real brain volumes. It estimates the orientation of the MSP more accurately but fails to correctly calculate the offset of MSP. On the other hand, the proposed algorithm detects the MSP more precisely and consistently.  
Figure 12: Visual comparison of the proposed algorithm with [PERSON] et al. algorithm for extracting the symmetric axis (MSP) from real head volumes (the NFBS database [48]). First row, second row, and third row display the input images, the proposed algorithm results, and [PERSON] et al. algorithm results, respectively.  
The IBSR database contains the manual delineation (GT) of brain hemispheres. For illustration, the input image from a brain volume is shown in Figure 13a with its manual delineation (mask) of the left (dark) and the right (bright) brain regions (Figure 13b). For comparison purpose, the boundary of the right region is superimposed on the input image with the white pixels using a morphological gradient and the binary skeletonization [51], as shown in Figure 13c. Therefore, this image is used as a GT for evaluation of MSP extraction results. For the precise and accurate result, the intersection line (between the estimated MSP and the corresponding orthogonal slice) should be overlapped or coincided with the white pixels boundary in the GT image.  
The first row in Figure 14 consists of all the input images with GT delineation of the right brain. Since the MSP divides the brain into two roughly symmetrical regions, the detected MSP (green line) by the proposed algorithm is almost completely overlapped with the boundary of the GT delineation of the right brain, as shown in the second row of Figure 14. In contrary, the MSP (blue line) estimated by [PERSON] et al. algorithm is deteriorated significantly from the real boundary of the GT delineation of the right brain. Note that we did not compare results of brain tumors datasets with [PERSON] et al. algorithm because they did not report results on such datasets in their paper.  
In short, all the promising results given by the proposed algorithm indicates that the developed technique has the highest accuracy and consistency in extracting the MSP. Finally, the results of automatic MSP detection and tilt correction (recenter and reorientation) in brain MRIs are displayed  
Figure 14: Visual comparison of the proposed algorithm with [PERSON] et al. algorithm for extracting the symmetric axis (MSP) from real head volumes of the IBSR database [49]. First row, second row, and third row display the input images, the proposed algorithm results, and [PERSON] et al. algorithm results, respectively.  
Figure 13: Superimposition of the ground truth (GT) image on the input image of the IBSR dataset [49]: (a) input image; (b) GT image; and (c) boundary of the right brain region is imposed on the input image with the white pixels.  
in Figure 15, where the first row represents the tilted slices of the three distinct brain volumes. After computing the parameters of the MSP and affine transformation, we reoriented and recentered the brain volumes. The corrected volume images are displayed in the second row of Figure 15.