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With regard to the improvement of image quality, image enhancement is an important process to assist human with better perception. This paper presents an automatic image enhancement method based on Artificial Bee Colony (ABC) algorithm. In this method, ABC algorithm is applied to find the optimum parameters of a transformation function, which is used in the enhancement by utilizing the local and global information of the image. In order to solve the optimization problem by ABC algorithm, an objective criterion in terms of the entropy and edge information is introduced to measure the image quality to make the enhancement as an automatic process. Several images are utilized in experiments to make a comparison with other enhancement methods, which are genetic algorithm-based and particle swarm optimization algorithm-based image enhancement methods.

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This paper presents nonlinear adaptive filters which are applied to gradient vector fields. The general convergence index of a gradient vector to the pixel of interest or the line of interest is defined and the convergence degree which is the output of the filter is defined as the average of convergence indices over the region of support. Three kinds of filters are proposed. The fundamental characteristics of the proposed filters are given.

SUMMARYIn this paper, we propose a method to improve the accuracy of classifiers by replacing the connection between the output layer and the immediately preceding hidden layer with an optimal linear transformer. This approach is intended to improve the performance of a breast cancer image diagnosis assistance system. The proposed classifier is composed of a three-layer MLP (multilayer perceptron) and a Mahalanobis classifier. The MLP has only one output unit, and produces output for two categories. If it is assumed that the value from the hidden layer immediately preceding the output layer forms a multivariable normal distribution for each class, that is, a Gaussian distribution, then the optimal linear transformer is a classifier based on the generalized Mahalanobis distance. Thus, the optimal classification is realized in the MLP after learning in which the generalized Mahalanobis distance with the hidden layer immediately preceding the output layer as the input is examined, and classification is performed on the basis of the likelihood. The proposed breast cancer image diagnosis assistance system, the system using only the conventional Mahalanobis classifier, and the system using only the conventional MLP classifier are compared. The best results are given by the proposed method, and it is shown that the performance of the breast image diagnosis assistance system can be improved.