A Neural-Network-Based Approach to White Blood Cell Classification
This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminati...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/796371 |
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| author | Mu-Chun Su Chun-Yen Cheng Pa-Chun Wang |
| author_facet | Mu-Chun Su Chun-Yen Cheng Pa-Chun Wang |
| author_sort | Mu-Chun Su |
| collection | DOAJ |
| description | This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems. |
| format | Article |
| id | doaj-art-9245796f2b07429ebef26de360de9718 |
| institution | DOAJ |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-9245796f2b07429ebef26de360de97182025-08-20T03:20:36ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/796371796371A Neural-Network-Based Approach to White Blood Cell ClassificationMu-Chun Su0Chun-Yen Cheng1Pa-Chun Wang2Department of Computer Science & Information Engineering, National Central University, Jhongli 32001, TaiwanDepartment of Computer Science & Information Engineering, National Central University, Jhongli 32001, TaiwanGeneral Hospital, Taipei 10656, TaiwanThis paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.http://dx.doi.org/10.1155/2014/796371 |
| spellingShingle | Mu-Chun Su Chun-Yen Cheng Pa-Chun Wang A Neural-Network-Based Approach to White Blood Cell Classification The Scientific World Journal |
| title | A Neural-Network-Based Approach to White Blood Cell Classification |
| title_full | A Neural-Network-Based Approach to White Blood Cell Classification |
| title_fullStr | A Neural-Network-Based Approach to White Blood Cell Classification |
| title_full_unstemmed | A Neural-Network-Based Approach to White Blood Cell Classification |
| title_short | A Neural-Network-Based Approach to White Blood Cell Classification |
| title_sort | neural network based approach to white blood cell classification |
| url | http://dx.doi.org/10.1155/2014/796371 |
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