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. 2012;12(5):5195-211.
doi: 10.3390/s120505195. Epub 2012 Apr 26.

Three-dimensional expansion of a dynamic programming method for boundary detection and its application to sequential magnetic resonance imaging (MRI)

Da-Chuan Cheng  1 , Jui-Teng Lin
Affiliations

Three-dimensional expansion of a dynamic programming method for boundary detection and its application to sequential magnetic resonance imaging (MRI)

Da-Chuan Cheng et al. Sensors (Basel). 2012.

Abstract

This study proposes a fast 3D dynamic programming expansion to find a shortest surface in a 3D matrix. This algorithm can detect boundaries in an image sequence. Using phantom image studies with added uniform distributed noise from different SNRs, the unsigned error of this proposed method is investigated. Comparing the automated results to the gold standard, the best averaged relative unsigned error of the proposed method is 0.77% (SNR = 20 dB), and its corresponding parameter values are reported. We further apply this method to detect the boundary of the real superficial femoral artery (SFA) in MRI sequences without a contrast injection. The manual tracings on the SFA boundaries are performed by well-trained experts to be the gold standard. The comparisons between the manual tracings and automated results are made on 16 MRI sequences (800 total images). The average unsigned error rate is 2.4% (SD = 2.0%). The results demonstrate that the proposed method can perform qualitatively better than the 2D dynamic programming for vessel boundary detection on MRI sequences.

Keywords: boundary detection; dynamic programming, MRI; femoral artery.

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Figures

Figure 1.
Figure 1.
The 3D matrix R contains the feature of an image sequence. The structure of the accumulation matrices (a) C1 and (b) C2 is organized in different orientations.
Figure 2.
Figure 2.
The accumulation matrices C1 and C2. The dimensions of (a) C1 and (b) C2 is same as those of R.
Figure 3.
Figure 3.
Two raw MRA images in one sequence. Each sequence contains 50 images. (a) Image taken in the systolic phase; (b) Image taken in the diastolic phase. The arrow indicates where the SFA is.
Figure 4.
Figure 4.
The phantom images with added noise from different SNRs.
Figure 5.
Figure 5.
The unsigned error plots of different phantom images. SNR is (a) 14 dB, (b) 16 dB, (c) 18 dB, and (d) 20 dB.
Figure 6.
Figure 6.
The contour detection results. SNR = 14 dB, s = 0.01, resize factor = 1.6. Averaged unsigned error = 1.5%.
Figure 7.
Figure 7.
One image is ruined. The proposed method can still hold the contour because of the continuity of the 3D DP method (d2 = 1).
Figure 8.
Figure 8.
The traditional DP fails to hold the contour, as there is no information between image slices.
Figure 9.
Figure 9.
Boundary detection results. Nine sequential resultant images are shown. The vessel boundaries are vague in the first five images.Their exact boundaries are difficult to determine. Based on the continuity consideration in three dimensions, the proposed method can detect the correct vessel boundaries.
Figure 10.
Figure 10.
Bland-Altman plots. The solid line indicates the mean, and dash lines are ± 1.96 SD. (a) The plot of sequence no. 9 (50 images). (1.96 SD = 4.2 mm2); (b) The plot of all 16 sequences (800 images). (1.96 SD = 4.1 mm2).
Figure 11.
Figure 11.
The cross-sectional SFA area changing in time (sequence no. 9). The unit in the y-axis is mm2. The x-axis unit is image number.
Figure 12.
Figure 12.
The software system GUI. The system can read DICOM and other image formats supported by MatLab. However, only the DICOM format offers pixel size information.
See this image and copyright information in PMC

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