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Automatic image segmentation and classification based on direction texton technique for hemolytic anemia in thin blood smears

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Abstract

This paper proposes an automatic method for cell segmentation and classification of erythrocytes in thin blood smears with hemolytic anemia. First, to remove the background and noises in the blood images, the proposed method detects a series of changes on the edges and analyzes the edge changes by using the 8-connection chain codes technique to recognize isolated erythrocytes. For segmenting the overlapping erythrocytes, the 8-connection chain codes technique obtains the edge direction of the cells to effectively figure out the points of high concavity. Then, the adapted high concavity information is used to separate overlapping erythrocytes and to extract features from each segmented erythrocyte. After segmenting, all the erythrocytes can be treated equally and the differences between adjacent chain codes of each erythrocyte can be calculated. Furthermore, the proposed method extracts the variation of eight directions from each individual erythrocyte as their features for classifying into four main hemolytic anemia types. Finally, classification process identifies abnormal erythrocytes and the types of hemolytic anemia by using a trained bank of classifiers, utilizing the proposed method to calculate the quantity of erythrocytes and recognize the types of hemolytic anemia effectively.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, No. 129 Sec. 3, San-mind Road, Taichung, 404, Taiwan, R.O.C

    Hung-Ming Chen & Ya-Ting Tsao

  2. Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan, R.O.C

    Shin-Ni Tsai

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  1. Hung-Ming Chen
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  2. Ya-Ting Tsao
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  3. Shin-Ni Tsai
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Correspondence to Hung-Ming Chen.

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Chen, HM., Tsao, YT. & Tsai, SN. Automatic image segmentation and classification based on direction texton technique for hemolytic anemia in thin blood smears. Machine Vision and Applications 25, 501–510 (2014). https://doi.org/10.1007/s00138-013-0585-y

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  • DOI: https://doi.org/10.1007/s00138-013-0585-y

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