Morphological Variation Classification of Red Blood Cells using Neural Network Model in the Peripheral Blood Images


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 6, No. 10, pp. 2707-2715, Oct. 1999
10.3745/KIPSTE.1999.6.10.2707,   PDF Download:

Abstract

Recently, there have been many researches to automate processing and analysing images in the medical field using image processing technique, a fast communication network, and high perfomance hardware, In this paper, we propose a system to be able to analyze morphological abnormality of red-blood cells for peripheral blood image using image processing techniques. To do this, we segment red-blood cells in the blood image acquired from microscope with CCD camera and then extract UNL Fourier features to classify them into 15 classes. We reduce the number of multi-variate features using PCA to construct a more efficient classifier. Our system has the best performance in recognition rate, compared with two other algorithms. LVQ3 and k-NN. So, we show that it can be applied to a pathological guided system.


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Cite this article
[IEEE Style]
K. K. Su and K. P. Koo, "Morphological Variation Classification of Red Blood Cells using Neural Network Model in the Peripheral Blood Images," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 6, no. 10, pp. 2707-2715, 1999. DOI: 10.3745/KIPSTE.1999.6.10.2707.

[ACM Style]
Kim Kyung Su and Kim Pan Koo. 1999. Morphological Variation Classification of Red Blood Cells using Neural Network Model in the Peripheral Blood Images. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 6, 10, (1999), 2707-2715. DOI: 10.3745/KIPSTE.1999.6.10.2707.