Efficient morphological-based edge detection algorithm for computed tomography cardiac images

Medical imaging has been extensively used for disease monitoring, treatment planning, diagnosis and computer aided surgery. Often, the acquired images are raw in nature, thus making them prone to being complex and noisy. A series of preprocessing and information extraction steps are therefore necessary in order for the relevant information to reach the medical practitioner. To this end, image denoising and edge detection play a vital role as a precursor to more advanced techniques in the medical image processing field. In this paper we have proposed an innovative mathematical morphology-based image denoising and edge detection method, for pre-processing of computed tomography (CT) images of the human heart. The morphological edge detection algorithm together with six different shaped structuring elements are implemented to preserve and detect the edges of the CT image while effectively suppressing noise, all at low computational cost. The experimental results affirm our approach's efficiency and capability in denoising and detecting salient edges from corrupted and complex medical images.