Su Mu-Chun, Cheng Chun-Yen, Wang Pa-Chun
Department of Computer Science & Information Engineering, National Central University, Jhongli 32001, Taiwan.
General Hospital, Taipei 10656, Taiwan.
ScientificWorldJournal. 2014 Jan 30;2014:796371. doi: 10.1155/2014/796371. eCollection 2014.
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.
本文提出了一种用于识别五种白细胞类型的新型白细胞分类系统。我们提出了一种新的分割算法，用于从涂片图像中分割白细胞。所提出的分割算法的核心思想是在HSI颜色空间中找到白细胞的判别区域。颜色位于由椭圆形区域描述的判别区域内的像素将被视为白细胞的细胞核和细胞质颗粒。然后，通过进一步的形态学处理，我们可以从涂片图像中分割出白细胞。从分割后的细胞中提取三种特征（即几何特征、颜色特征和基于局部二值模式的纹理特征）。这些特征被输入到三种不同类型的神经网络中以识别白细胞的类型。为了测试所提出的白细胞分类系统的有效性，总共使用了450张白细胞图像。最高总体正确识别率可达99.11%。仿真结果表明，所提出的白细胞分类系统与一些现有系统相比具有很强的竞争力。
