Retracted: Denoising and segmentation of brain image by proficient blended threshold and conserve edge scrutinize technique

Abstract

AbstractUnusual collection of mass tissue in human body is commonly refereed as tumor. The tumor when it is found in brain. These tumor cells have tendency to multiply and grow in rapid speed. The growth of tumors is generally uncontrollable in nature. Tumor in brain develops along the skull and it evolves to contact with the functioning of the brain. Brain tumor (BT) can be detected at the earlier stages with the help of MRI or CT scan techniques. These scanning techniques are proved to be efficient in detecting the tumor irrespective of its size. Brain tumor being a life‐threatening disease has to be diagnosed at the earliest before it turns to be malignant. The current research work focuses in proposing an efficient image processing techniques by processing the MRI images of human brain which is affected by brain tumor. This process is carried out in two stages as image denoising and image segmentation which are helpful in detecting and localizing the tumor affected region in human brain. Initially, the MRI image is read and preprocessed by converting the input image into a grayscale image and noise removed by involving the proposed method. Later, the proposed image is segmented using sobel edge detection method and the image is enhanced using the image enhancement techniques. It is achieved by using the proficient blended thresholding (PBT) Segmentation method. The performance of the proposed methods is evaluated using PSNR (peak signal noise ratio) and RMSE (root mean square error). The proposed conserve edge scrutinize (CES) filter achieved highest PSNR value. Then segmentation is evaluated by five metrices: Sensitivity, Specificity, Dice, Jaccard, and Accuracy.