基于字典学习的正则化鲁棒稀疏表示肿瘤细胞图像识别

Regularized robust coding for tumor cell image recognition based on dictionary learning

针对胃黏膜肿瘤细胞图像的高维性及复杂性的特点,为了提高稀疏表示图像识别的鲁棒性,提出了一种基于字典学习的正则化鲁棒稀疏表示(RRC)肿瘤细胞图像识别方法。该方法首先将所有的原始染色肿瘤细胞图像转化为灰度图像;然后利用具有Fisher判别约束的字典学习(FDDL)方法对肿瘤细胞图像训练样本的全局特征进行字典学习,得到具有类别标签的结构化字典;最后将具有判别性的新字典用于RRC模型进行分类识别。RRC模型是基于最大后验概率准则,将稀疏保真度表示为余项的最大后验概率函数,最终识别问题转化为求解正则化加权范数的优化逼近问题。将提出的识别方法应用于肿瘤细胞图像的最高识别率为92.4%,表明该方法能够有效地实现肿瘤细胞图像的分类。

Aiming at the characteristics of high dimension and complexity of gastric mucosal tumor cell images, a new method based on Fisher Discrimination Dictionary Learning and Regularized Robust Coding （FDDL-RRC） was proposed for the recognition of tumor cell images, so as to improve the robustness of sparse representation for image recognition. Firstly, all the original stained tumor cell images were transformed into gray images, and then the Fisher discrimination dictionary learning method was used to learn the global features of training samples and obtain the structured dictionary with class labels; lastly, the new discriminative dictionary was used to classify the test samples by the model of RRC. The model of RRC was based on Maximum A Posterior （MAP） estimation, and the sparse fidelity was expressed by the MAP function of residuals, so the problem of identification was converted to the optimal regularized weighted norm approximation problem. The highest recognition accuracy rate of the proposed method for tumor cell images can reach 92.4%, which indicates that the presented method can effectively and quickly distinguish the tumor cell images.