生物医学信号处理与控制

生物医学信号处理与控制(中文10000字,英文7200字)
摘 要  
文 章 信 息  
宫颈涂片显微图像的病理检查仍然是宫颈癌诊断的主要方法。图像的准确分割和分类是分析的两个重要阶段。首先，应用Mean-Shift聚类算法来获得细胞核分割的感兴趣区域（ROI）。然后应用灵活的数学形态学来分裂重叠的细胞核，以获得更好的准确性和稳健性。对于图像的分类，提取基于形状的特征，基于颜色空间的纹理特征和Gabor特征，并将它们放在一起以获得更好的分类性能。最优特征集是通过链式代理遗传算法（CAGA），P值和最大相关性 - 最小多重共线性（MRmMC）获得的。所提出的分割和分类方法在362个宫颈涂片图像上进行了测试。实验结果表明，所提出的分割方法可以对宫颈细胞核进行分割，具有较高的分割效果（敏感性：94.25％士1.03％，特异性93.45％士1.14％）。基于具有Gabor特征的CAGA特征选择方法对于正常，未涉及和异常图像具有最高的分类性能（超过96％的准确度）。该方法可以自动有效地分割显微图像的细胞核。从实验结果来看，基于CAGA的Gabor特征和特征选择显然有助于提高分类性能。
Automatic cell nuclei segmentation and classification of cervical Pap smear images [资料来源：https://www.doc163.com]
abstract
Pathological examination of microscopic image of Pap smear slide remains the main method for cervical cancer diagnosis. The accurate segmentation and classification of images are two important phases of the analysis. Firstly, the Mean-Shift clustering algorithm is applied to obtain regions of interest (ROI) for cell nuclei segmentation. Then the flexible mathematical morphology is applied to split overlapped cell nuclei for better accuracy and robustness. For classification of the images, features based on shape, textural features based on color space and Gabor features are extracted and put together to obtain better classification performance. The optimal feature set is obtained by chain-like agent genetic algorithm (CAGA), P-value and maximum relevance-minimum multicollinearity (MRmMC). The proposed segmentation and classification methods were tested on 362 cervical Pap smear images. Experimental results showed that the cervical cell nuclei can be segmented by the proposed segmentation method with high effective segmentation results (Sensitivity: 94.25%士 1.03% and Specificity 93.45%士 1.14%). The feature selection method based on CAGA with Gabor features has the highest classification performance for normal, uninvolved and abnormal images (more than 96% accuracy). The proposed method can automatically and effectively segment cell nuclei of microscopic images. From the experimental results, Gabor features and feature selection based on CAGA are apparently helpful for improving the

[资料来源：http://Doc163.com]