基于内容的彩色细胞图像检索

Content based color cell image retrieval

提出了一种结合颜色、纹理和形状特征的细胞病理图像检索方法 .首先 ,运用K 均值聚类方法提取出细胞核 ,并且采用多域值分割算法去除细胞图像中的背景区域 .提取的特征包括颜色、纹理和形状等 ,这些特征能很好地表征单核细胞的主要特性 .由于提取的特征数值范围以及物理意义不尽相同 ,对特征进行了归一化处理 .最后提出了相关的反馈系统 .该系统可以自动地调整不同特征的权值 ,提高了图像检索的准确率 .运用该方法进行细胞图像的检索更符合人的视觉感觉要求 ,比仅仅提取一个特征的方法更加准确 .

A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. This system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.