In the recognition of osteosarcoma magnetic resonance images (MRI), the probability of a pixel belonging to a class is not only related to its own features, but also closely correlated with the information distribution of the surrounding pixels. However, it is currently unable to recognize the osteosarcoma lesions and surrounding issues simultaneously. In order to solve the problem, we propose a fully automated approach to osteosarcoma MRI segmentation and recognition. It uses Conditional Random Field (CRF) model to incorporate multiple features, especially the texture context features, which are based on the relative position of pixels' texture and make a significant difference in more accurately determining which class a pixel belongs to. Further, we propose to model the mutual constraint relations between the targets (bone tumor, soft tissue, etc.) features in the osteosarcoma MRI, and train the tagging samples with the Joint-boost algorithm. Our experimental results show that the proposed method is effective and encouraging. It is especially superior for recognizing tumors with irregular shape and structure, which are identified with low accuracy in other methods.