Brain Tumor Detection in 3D MRI images using Kapur's Entropy and flood fill algorithm

The Brain tumor is one of the most important factors in mortality, so timely and appropriate detection is necessary to treat the tumor. In this study, 3D images are used to detect tumor. 3D images have depth and therefore, blind spots that may be hidden in 2D images can be seen. This paper presents a threshold method using Kapur’s entropy to detect brain tumors in 3D MRI images. In the proposed method, in order to differentiate the tumor area, the images are normalized in three dimensions, which has the advantage that the brightness level of the tumor is brighter than the rest of brain. In the next step, the 3D image is sliced in 3D and converted into 2D images. By applying two steps of Kapur’s entropy to two-dimensional images of the tumor area with points that have a higher brightness level than the threshold value are separated. To remove Additional areas, a 3D image is first made by stacking 2D images on top of each other, and then the 3D area is extracted using a 3D morphology filter and flood-fill algorithm the advantages of the proposed method is the removal of excess areas while preserving the tumor area and covering all angles of the tumor in three dimensions. To show the efficiency of the proposed method, the BRATS database was used. The evaluation results for detecting tumor were evaluated with similarity, sensitivity and specificity coefficients of 0.9407, 0.9235 and 0.999, respectively, which have better performance than the proposed methods.