A hybrid method for traumatic brain injury lesion segmentation

<span>Traumatic brain injuries are significant effects of disability and loss life. Physicians employ computed tomography (CT) images to observe the trauma measure its severity for diagnosis treatment. Due overlap hemorrhage normal tissues, segmentation methods sometimes lead false results. The study is more challenging unitize AI field collect by involving patient datasets employing CT scans images. We propose a novel technique free-form object model injury image based on superpixel processing that uses analyzing injuries, quite create high outstanding simple linear iterative clustering (SLIC) method. maintains strategic distance reduced intensity boundaries. contains marked red modify model. contour labelled mark output from our proposed hybrid approach combined edge detection dilation features. diminishes computational costs, show accomplished 96.68% accuracy. segmenting achieved in clustered region construct also presents further directions future research this domain.</span>