Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia (ALL). As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution, which significantly affects the absorption and reflection of light, the spectral feature is proved to be important for leukocytes classification and identification. This paper proposes an accurate identification method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging (HSI) technology which combines the spectral information. The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm. Then, the spectral features are extracted and combined with the spatial features. Based on this, the support vector machine (SVM) is applied for classification of five types of leukocytes and abnormal leukocytes. Compared with different classification methods, the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes, improving the accuracy in the classification and identification of leukocytes. This paper only selects one subtype of ALL for test, and the proposed method can be applied for detection of other leukemia in the future.

中文翻译：

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基于显微高光谱成像技术的异常白细胞空间光谱识别

异常白细胞的筛查和诊断对于免疫疾病和急性淋巴细胞白血病 (ALL) 的诊断至关重要。由于异常白细胞的恶化主要是由于染色质分布的变化，显着影响了光的吸收和反射，因此光谱特征被证明对白细胞的分类和鉴定具有重要意义。本文提出了一种基于显微高光谱成像（HSI）技术结合光谱信息的健康和异常白细胞的准确识别方法。通过形态分水岭算法获得细胞核和细胞质的分割。然后，提取光谱特征并将其与空间特征结合。基于此，支持向量机（SVM）用于五种白细胞和异常白细胞的分类。与不同的分类方法相比，该方法利用光谱特征突出了健康白细胞和异常白细胞之间的差异，提高了白细胞分类和识别的准确性。本文仅选取一种ALL亚型进行检测，该方法未来可应用于其他白血病的检测。