Edge detection in medical images using morphological operators

Edge detection is an important research area in digital image processing with severalapplications. Edges characterize boundaries and therefore a problem of fundamentalimportance in image processing. The aim of edge detection in an image is to reducethe amount of data and filter out useless information, while preserving the importantstructural properties in an image.Mathematical morphology provides a systematic approach to analyze the geometriccharacteristics of signals or images, and has been used widely in many applicationssuch as boundary detection, noise removal, image enhancement and imagesegmentation etc. The advantages of morphological approaches over linearapproaches are 1) direct geometric interpretation, 2) simplicity, and 3) efficiency inhardware implementation.The main purpose of this thesis is to provide an overview of mathematicalmorphology and review some edge detection algorithms based on mathematicalmorphology and also propose a method for detecting edges in medical images suchas Computed Tomography and Magnetic Resonance images. Edge detection can bedivided into two phases; the first is the noise removal, and the second is ideal edgedetection. By using an iterative averaged closing-opening operation, impulse noise aswell as Gaussian noise is eliminated from the image. Then, the resulting ideal edgescan be extracted by using a simple morphologic operator.Keywords: Edge detection, Mathematical Morphology, Alternating SequentialFilters,Thinning