Extraction of the Liver Tumor in CT Images by Real Coded Genetic Algorithm (RGA)

K. Nakamichi, S. Karungaru, M. Fukumi, T. Akashi, Y. Mitsukura, and M. Yasutomo (Japan)


RealCoded Genetic Algorithm, Medical Imaging, CT Images, Liver tumor, Extraction, Segmentation, Cancer


In Japan, diet-linked diseases are increasing owing to the reasons of diversified and meat-oriented diet. Disease of an internal organ and brain disease are those instances. Furthermore regardless of race, increase in fatalities by cancer, cardiac disease and cerebropathy is a social issue. Medical equipments as the CT, MRI and ultrasonography (US) is used for these sick discoveries. The demand for Medical Equipments has increased. The doctor's load is increasing as it increases. The purpose of this work is the construction of an automatic diagnosis support system for CT images in order to reduce the doctor's load. Toward this end, in this paper, a method to extract liver tumors in CT images using a real-coded genetic algorithm is proposed. Conventionally, a threshold is necessary to extract an object from an image. However, such a method is not effective for CT images because gray scale values are different in each image. Therefore, in this paper, we propose the method for extracting the tumor in the liver from the CT image without the need of a threshold. In this method, a polygon enclosure of the liver tumor is extracted using a GA.

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