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.

生物材料的显微高光谱成像分析技术是生物光谱学研究的前沿.烧伤、深度创伤病人治疗过程中，需要确定移植于患者创面的真皮替代物有没有进入正常的血管化进程，这是评价填充修复材料优劣的关键，也是患者创面恢复的重要指标.提出并实现了一种基于G-SA-SVM的快速血管化鉴别方法.该方法以显微高光谱成像技术为基础，首先对采集的高光谱数据进行光谱维和空间维的空白校正处理，然后对数据进行特征自适应性Gamma校正，最后利用模拟退火优化参数的支持向量机算法(SA-SVM)进行识别处理，有效定位红细胞，进而快速定位血管.实验结果表明，本文提出的G-SA-SVM算法误判率更低，识别精度更高，可以用于微血管新生的评价和鉴定.

使用自行研制的推帚式显微高光谱成像系统采集了正常、白血病人血液涂片的显微高光谱图像数据。通过对正常、白血病人血液显微高光谱数据进行处理,获得了人血液的单波段图像,并提取了部分血液细胞的典型透射率光谱曲线。分析这些曲线发现,病变细胞透射率普遍高于正常细胞,特别是在541.3nm附近透射率高出了50%左右。通过对血液涂片的图像和光谱特征进行分析表明,经过一定的改进,可以将显微高光谱成像系统作为一种新的检测手段,辅助医学研究人员对人血液进行分析。