Knowledge-based image understanding and classification system for medical image database

With the advent of Computer Radiographs(CR) and Digital Radiograph s(DR), image understanding and classification in medical image databases have attracted considerable attention. In this paper, we propose a knowledge-based image understanding and classification system for medical image databases. An object-oriented knowledge model has been introduced and the idea that content features of medical images must hierarchically match to the related knowledge model is used. As a result of finding the best match model, the input image can be classified. The implementation of the system includes three stages. The first stage focuses on the match of the coarse pattern of the model class and has three steps: image preprocessing, feature extraction, and neural network classification. Once the coarse shape classification is done, a small set of plausible model candidates are then employed for a detailed match in the second stage. Its match outputs imply the result models might be contained in the processed images. Finally, an evaluation strategy is used to further confirm the results. The performance of the system has been tested on different types of digital radiographs, including pelvis, ankle, elbow and etc. The experimental results suggest that the system prototype is applicable and robust, and the accuracy of the system is near 70% in our image databases.