# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 2 Related Work
### Metamorphic Testing in IPA
Some researchers have integrated MT with structural testing. A self-checked testing approach is proposed by [PERSON] et al. [35] for the detection of subtle faults in the implementation. An image processing program is used to recreate a 3D structure of a biological cell. The reconstructed image is compared with the original image (used as a test oracle) through the pattern recognition component. The proposed approach integrates MT with structural testing for fault detection. The effectiveness of MT is verified by test coverage criterion. Statement coverage, branch coverage and def-use coverage are used to manually test the source code (full source code is not tested). Furthermore, the effectiveness of proposed approach is validated through mutation testing. [PERSON] et al. [36] proposed a method for MR refinement. Discrete dipole approximation program (ADDA) program is used to test the proposed scheme. Test cases are generated through the ray tracing technique. The effectiveness of MT is validated through mutation testing. It is observed that the MRs defined in this program are weak because of the unknown test output relation. Therefore, more MRs are required to adequately test the ADDA program. [PERSON] et al. proposed a framework to evaluate the effectiveness of MT [18]. Test cases are generated through random testing. The authors have developed an iterative method to check the adequacy of MR. MT adequacy is checked through program coverage, mutation analysis and mutation tests for testing MRs. The proposed framework is explained through an image processing application that is used to construct a 3D biological cell. The effectiveness of proposed scheme is demonstrated through a case study in which a complex Monte Carlo program is tested. Table 1 shows the advantages and disadvantages of the metamorphic testing method in IPAs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Operation** & **Input Generation** & **Advantages** & **Disadvantages** \\
**Paper** & **Performed** & **Method** & & \\ \hline
[7] & \begin{tabular}{c} Dilation, Erosion \\ \end{tabular} & \begin{tabular}{c} Random Input \\ Generation Method \\ \end{tabular} & \
\begin{tabular}{c} Effectiveness of MRs are \\ identified through \\ mutation testing \\ \end{tabular} &
\begin{tabular}{c} Structural testing is not used to check \\ the adequacy of source test cases \\ images are compared pixel by pixel \\ \end{tabular} \\ \hline
[16] & \begin{tabular}{c} Sobel Edge \\ Detection \\ \end{tabular} & \begin{tabular}{c} Random Input \\ Generation Method \\ \end{tabular} & \
\begin{tabular}{c} Edge detection algorithm is \\ used to check the \\ the effectiveness of MRs \\ \end{tabular} &
\begin{tabular}{c} Structural testing is not used to check \\ the adequacy of source test cases \\ images are compared pixel by pixel \\ \end{tabular} \\ \hline
[17] & \begin{tabular}{c} Image Region \\ Growth \\ \end{tabular} & \begin{tabular}{c} Segmental Symbolic \\ Evaluation Method \\ \end{tabular} & \
\begin{tabular}{c} Effectiveness of MRs are \\ checked through mutation \\ testing \\ \end{tabular} &
\begin{tabular}{c} Structural testing is not used to check \\ the adequacy of source test cases \\ images are compared pixel by pixel \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of papers related to metamorphic testing in IPAs.