Abstract
Medical image processing technique provides an objective and quantitative approach for characterizing the pathological tissue images. In this paper, the fractal dimension was used to quantify the image textures for differentiating the normal and cancerous cells in the liver tissue image. Several image enhancement methods and one edge detection method were applied for accentuating the objects of interest before fractal dimension estimation. From the results, it is shown that the edge-based histogram equalization would be the best one among all these image enhancement methods. In addition, any two fractal dimensions were combined as the input features to the learning vector quantization network for tissue classification. From the results obtained from ten normal and ten cancer cases, the accuracy was demonstrated to be more than 90%. Above all, the user-friendly graphical user interface was also developed in this study.
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© 2000 Springer-Verlag London
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Cheng, KS., Sun, R., Chow, NH. (2000). Cancerous Liver Tissue Differentiation Using LVQ. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_9
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_9
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
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