# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 2 Related Work
### Tumor Detection in MRI Brain Images  
In this category, we have selected those research papers which have proposed either an enhanced or improved edge detection algorithm for detecting edges in MRI brain images or the papers which have proposed enhanced algorithms for the detection of brain tumor. Some of the researchers have proposed enhanced algorithm for the detection of edges in MRI images. An improved canny edge detection algorithm is proposed for the detection of brain tumor in MRI images by [PERSON] et al. [22]. A Laplacian of Gaussian (LoG) filter is used to identify the regions with fast intensity change. In the proposed algorithm, modified kernel and modified gradient magnitude is used for image smoothing. Results show that the improved canny edge detection algorithm shows more details for detecting the type of brain tumor instead of traditional canny edge detection algorithm. Another improved canny edge detection algorithm is proposed by [PERSON] et al. [23]. In the proposed algorithm, a median filter is used to suppress the noise with considerably less blurring. Balance Contrast Enhancement Technique (BCET) is used where the contrast of the image can be stretched or compressed without changing the histogram pattern of input image. Afterwards, FCM clustering method is applied, and in the end, traditional canny edge detection algorithm is used to detect the detailed edges. The improved algorithm is then compared with traditional edge detection algorithms. [PERSON] et al. proposed an improved edge detection algorithm for the detection of edges in MRI and CT scans [24]. They have used Gabor wavelet Transform to remove the noise. Gabor transform is integrated with K-Means and Fuzzy C-Means clustering algorithms. Traditional canny edge detection is used for edge detection. The results are calculated on the basis of Figure of Merit (FOM) and Misclassification Rate (MCR). An improved Sobel edge detection is proposed by [PERSON] et al. [25]. The algorithm can detect less false edges as compared to conventional Sobel edge detection algorithm for brain tumor segmentation of MRI images. Closed contour algorithm is used to detect different regions for tumor detection. Results are evaluated on the basis of three parameters such as gray level uniformity measure (GU), Q-parameter, and relative ultimate measurement accuracy (RUMA). In the proposed algorithm, a median filter is used to suppress the noise with considerably less blurring [26]. Balance Contrast Enhancement Technique (BCET) is used where the contrast of the image can be stretched or compressed without changing the histogram pattern of input image. Afterwards, FCM clustering method is applied and, in the end, traditional canny edge detection algorithm is used to detect the detailed edges. The results are evaluated on the basis of figure of merit (FOM), sensitivity, and accuracy. [PERSON] et al. proposed an improved edge detection algorithm named \"Luminance edge detection algorithm\" for MRI brain images based on image quality parameters [27]. Mean filter is used for image smoothing. Then, pixel intensity is changed using gradient method. Remove the pixel that is not considered an edge. Classify the pixels on the basis of thresholding and then interpolate the pixels. The results are evaluated through SSIM and PSNR parameters and then compared with conventional edge detection algorithms (Sobel, Canny, Prewitt). [PERSON] proposed a novel algorithm for the detection of edges in medical images (MRI brain images, blood cells) having salt and pepper noise [28]. Renyi entropy is used to find the global threshold value whereas Kapur entropy is used to find the local threshold value. Then, edge detection procedure is applied. The results are compared with conventional edge detection algorithms such as Sobel, canny, LoG, and Prewitt. Another improved edge detection algorithm is proposed by [PERSON] et al. [29]. The edges are detected using Chebyshev's Orthogonal Polynomial. Averaging filter is used to remove noise in filtering process.