In this chapter, we have presented a promising framework for automatic staining pattern classification of HEp-2 cells to support the diagnosis of specific autoimmune diseases. The LLDC framework can extract more discriminative information and consequently gives better HEp-2 cells classification performance than many existing coding methods. The LLDC method is based on local distance vector which captures discriminative information via image-to-class distance. Furthermore, local distance vector improves the classification performance by making adjustments only to the classes found in the local kwv nearest neighbors around the local features. It can avoid disturbance from isolated classes. Additionally, the distance patterns and the original local features are proven to be complementary to each other. Therefore their concatenation can achieve better classification performance. Experimental results on the ICPR2012 dataset and the ICIP2013 training dataset validate that the proposed LLDC framework can provide superior performance for staining pattern classification, compared with the some improved coding methods.
Compared with traditional coding methods, the LLDC framework is time consuming as it needs to transform original local features to local distance vectors one by one and it is an integration of two kinds of image representations. In the future, we plan to design a new model to reduce the algorithm’s complexity while improving the accuracy.