Segmentation of hand radiographs by using multi-level connected active appearance models

摘要

Robust and accurate segmentation methods are important for the computerized evaluation of medical images. For treatment of rheumatoid arthritis, joint damage assessment in radiographs of hands is frequently used for monitoring disease progression. Current clinical scoring methods are based on visual measurements that are time-consuming and subject to intra and inter-reader variance. A solution may be found in the development of partially automated assessment procedures. This requires reliable segmentation algorithms. Our work demonstrates a segmentation method based on multiple connected active appearance models (AAM) with multiple search steps using different quality levels. The quality level can be regulated by setting the image resolution and the number of landmarks in the AAMs. We performed experiments using two models of different quality levels for shape and texture information. Both models included AAMs for the carpal region, the metacarpals, and all phalanges. By starting an iterative search with the faster, low-quality model, we were able to determine the initial parameters of the second, high-quality model. After the second search, the results showed successful segmentation for 22 of 30 test images. For these images, 70% of the landmarks were found within 1.3 mm difference from manual placement by an expert. The multi-level search approach resulted in a reduction of 50% in calculation time compared to a search using a single model. Results are expected to improve when the model is refined by increasing the number of training examples and the resolution of the models.