Medical Images Edge Detection Based on Mathematical Morphology

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 reconstruction. Conventionally, edge is detected according to 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, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of medical images with salt-and-pepper noise. The simulation results shows that the novel mathematical morphological edge detection algorithm is more efficient for image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms. It has been observed that the proposed morphological edge detection algorithm performs better than sobel, prewitt, roberts and canny’s edge detection algorithm. In this paper the comparative analysis of various image edge detection techniques is presented using MATLAB 8.

Image Restoration Using M-Flann

In this paper a Modified Functional Link Artificial. Neural Network (M-FLANN) is proposed which is simpler than a Multilayer Perceptron (MLP). It have been implemented for image restoration in this paper. Its computational complexity and speed and generalization ability to cancel Gaussian noise is compared with that of MLP. In contrast to a feed forward ANN structure i.e. a multiplayer perceptron (MLP) the M-FLANN is basically a single layer structure in which non-linearity is introduced by enhancing the input pattern with nonlinear function expansion. With the proper choice of functional expansion in a FLANN problem of denoising of an image. In the single layer functional link ANN (FLANN) the need of hidden layer is eliminated.The novelty of the FLANN structure is that it requires much less computation than that of MLP. In the presence of additive white Gaussian noise, salt and pepper noise, Random variable impulse noise and mixed noise in the image the performance of the proposed network is compared with that of MLP in this thesis. The Performance of the of algorithm is evaluated for six different situations i.e. for single layer neural network, MLP and four different types of expansion in FLANN and comparison in terms of computational complexity also carried ou