Hemorrhage detection using edge-based contour with fuzzy clustering from brain computed tomography images

摘要

The paper presents a segmentation method for extracting the hemorrhage out of CT (computed tomography) images of brain by using the features of fuzzy clustering together with the level-set segmentation method. The fuzzy clustering is utilized for initialization of level-set function that evolves to extract the desired hemorrhagic region. In addition, the fuzzy clustering has also been utilized for estimating the parameters which control the propagation of level set function. The proposed method eradicates the requirement of manual initialization and re-initialization process which is very much time inefficient, as required by majority of conventional level-set segmentation methods and thus speeding up the process related with evolution of function associated with level-set. The proposed method has been implemented over a dataset containing 300 CT images of brain with hemorrhages of various sizes and shapes and the performance of proposed method is compared with existing techniques like fuzzy c- mean (FCM) clustering and region growing. The results of this method are observed to have highest values related with similarity indices such as overlap metric, accuracy, specificity and sensitivity with values as 87.46%, 85.40%, 98.79% and 79.91% respectively for given dataset of 300 images.