Medical Images Edge Detection Based on Mathematical Morphology

Abstract

Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms.

Deep imaging of the shell elliptical galaxy NGC 3923 with MegaCam

“…The dilatation operation is defined like the erosion, only the minimum is substituted by the maximum. Moreover, these methods implement a noise-smoothing step (e.g., Gasteratos et al 1998or Yu-qian et al 2006. Here the similarity with minimum masking ends.…”

Edge Detection in Multispectral Images Based on Structural Elements

“…Erosion operator is explained below for A, B sets: = min{A(x+s, y+t)−B(s,t)} (2) Erosion makes small or makes the around of an image thin. Like dilation operator the how and the amount of the erosion is controlled by the structure elements.…”

“…Morphology edge detection algorithm uses basic operator such as closing, opening, dilation, erosion described below: (5) Ed (A) is an image edge which is achieved by using the subtract of dilation image from the main image and Ee (A) is the subtraction of the main image from erosion image [2]. With noticing the former relations we observe that dilation and closing make the shape of the image big where as erosion and opening makes the shape of the image small so we can use these exclusivity for finding the edges [3].Morphology gradient of the image is like below: (6) Fig (3) presents result of the comparison between this method and other methods for Lena image (Fig.3 (a)) and one sample of multi spectral image (Fig.3 (b)) shown.…”

Closing and Opening based on discrete t-norms. Applications to natural Image Analysis

“…Initial results in edge detection, showed there, improved, at least at naked eye, those obtained using the nilpotent t-norms. The mathematical morphology has been already used in medical image analysis ( [9], [10]). Therefore, discrete fuzzy mathematical morphology could have a wide range of applications in this research area.…”