Automatic construction of image transformation algorithms using feature based genetic image network

Image processing and recognition technologies are becoming increasingly important. Automatic construction methods for image transformation algorithms proposed to date approximate adequate image transformation from original images to their target images using a combination of several known image processing filters by evolutionary computation techniques. In this paper, we introduce the adaptive image processing filters that process according to the features of an input image. The processing of the adaptive filters is decided based on the local features of an input image. We implement them to feed-forward genetic image network (FFGIN) that is one of the automatic construction methods for image transformations. Then we apply our method to the problems of segmentation of organs and tissues in medical images. Experimental results show that our method constructs the effective segmentation algorithms that extract multiple regions respectively.

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Yuta Nakano, Shinichi Shirakawa, Noriko Yata, Tomoharu Nagao
• Keywords: Artificial Intelligence, CEC 2010, Image, Image Processing, Image Processing Filters