# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 4 Experiments and Results
### Self-Evaluation  
Self-evaluation of the proposed method is carried out on Dataset I. The results of experiment 1 (the proposed method without LSE), experiment 2 (the proposed method with LSE based on the edge indicator \(g\)), and experiment 3 (SFCME-LSE) are compared to understand the contributions of the individual components of the SFCME-LSE to the overall performance. Further, we add Gaussian noise to images to evaluate the anti-noise performance of the SFCME-LSE. Images from Dataset I already contain a high amount of noise. However, these original images are viewed noise-free and contaminated by additional noise with a specified signal-to-noise ratio (SNR). The real SNR of noisy images is lower than the specified value, as a result. The means and standard deviations of the numerical metrics for the MAB and LIB are summarized in Table 3.  
From a clinical perspective, the quality of a method might be analyzed intuitively according to the percentage of frames lying below a progressive threshold of the PAD, as shown in Figure 6a. The results obtained in experiment 1 demonstrate the effectiveness of the proposed border initialization technology. There are substantial improvements in the HD from experiment 1 to experiment 3, indicating the convergence of edge-based LSE on the contour. We achieved a modest improvement in the JM from 0.89 using the edge indicator \(g\) to 0.91 using \(g_{new}\) in detecting the MAB, and from 0.80 to 0.81 in detecting the LIB. Additionally, high JMs, low HDs, and small PADs in experiment 3 illustrate to some extent the ability of SFCME-LSE for IVUS image segmentation. Further, the performance of SFCME-LSE is acceptable on images with SNR higher than 30 dB, as illustrated in Table 3 and Figure 6b. In Figure 7, we present Bland-Altman plots and linear regression plots for \(Area\big{(}R_{seg}\big{)}\) and \(Area(R_{truth})\), visualizing the robustness of the algorithm to the size of the vessel cross-sections. Specifically, the linear regression analysis indicates correlation with \(R^{2}=0.95\) (\(p=1.24\times 10^{-214}\)) and \(R^{2}=0.85\) (\(p=1.99\times 10^{-135}\)) for MAB and LIB detection, respectively. Bland-Altman plots indicate a slight bias of 1.33 mm\({}^{2}\) and 0.60 mm\({}^{2}\) for MAB and LIB detection, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{5}{c}{**SFCME-LSE on Images with Different SNR**} \\ \cline{3-13}  & & **1** & **2** & **3** & **70 dB** & **50 dB** & **30 dB** & **27 dB** & **25 dB** & **23 dB** & **20 dB** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} MB \\ \end{tabular} } & JM & 0.87 & 0.89 & 0.91 & 0.87 & 0.87 & 0.87 & 0.87 & 0.84 & 0.79 & 0.66 \\  & (0.06) & (0.07) & (0.07) & (0.13) & (0.12) & (0.12) & (0.12) & (0.16) & (0.25) & (0.33) \\  & HD & 0.90 & 0.65 & 0.65 & 0.89 & 0.88 & 0.87 & 0.89 & 1.02 & 1.17 & 0.62 \\  & (0.49) & (0.44) & (0.43) & (0.80) & (0.79) & (0.81) & (0.81) & (1.05) & (1.17) & (0.37) \\  & PAD & 0.10 & 0.09 & 0.07 & 0.11 & 0.10 & 0.12 & 0.14 & 1.19 & 1.20 & 0.33 \\  & (0.09) & (0.10) & (0.09) & (0.17) & (0.14) & (0.21) & (0.26) & (1.41) & (1.29) & (0.35) \\ \hline \multirow{4}{*}{LIB} & JM & 0.73 & 0.80 & 0.81 & 0.80 & 0.80 & 0.80 & 0.78 & 0.77 & 0.75 & 0.67 \\  & (0.10) & (0.09) & (0.09) & (0.10) & (0.09) & (0.10) & (0.12) & (0.13) & (0.15) & (0.21) \\  & HD & 1.41 & 0.88 & 0.86 & 0.95 & 0.97 & 0.96 & 1.06 & 1.07 & 1.11 & 1.33 \\  & (0.59) & (0.57) & (0.57) & (0.61) & (0.60) & (0.58) & (0.64) & (0.68) & (1.65) & (0.76) \\  & PAD & 0.32 & 0.15 & 0.14 & 0.15 & 0.16 & 0.18 & 0.25 & 1.27 & 1.25 & 0.33 \\  & (0.23) & (0.15) & (0.13) & (0.13) & (0.14) & (0.19) & (0.47) & (1.52) & (1.23) & (0.26) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of the proposed method on Dataset I. The format is mean (standard deviation). Figure 6: **(a)** The cumulative distributions of the percentage of area differences (PADs) in the three experiments.