Semantic Annotation of Medical Color Image Based on PCA and BP Neural Network

Objective To design endoscope image semantic annotation classifier aimed at the needs of computer-aided diagnosis,as well as the problems that it was more difficult to achieve semantic retrieval and less involved in medical color image classification.Methods The classifier was developped based on C#/C++ programming language,Windows 7,.NET and Visual-Studio(VS2008) platform,through the CBIR feature extraction,principal component analysis(PCA) to reduce the dimensions,and then BP neural network training classification.Results The classification accuracy of seven types of medical endoscopic color image was above 80%,while the training time was only a few seconds or tens of milliseconds.Conclusion The combination of principal component analysis and BP neural network overcomes the considerable gap between the low-level features and advanced semantic.The dimensions reducing significantly reduces the system memory,improves the training speed and achieves a better annotation results.