The morphology of a bone is closely associated with its biomechanical response. Thus, much research has been focused on analyzing the effects of variation of bone morphology with subject-specific models. Subject-specific models, which are generally achieved from 3D imaging devices like CT and MRI, incorporate more of the detailed information that makes a model unique. Hence, it may predict individual responses more accurately. Despite these powerful characteristics, specific models are not easily parameterized to the extent possible with statistical models because of their morphologic complexities. Thus, it is still proven challenging to analyze morphologic variations of subject-specific models across changes due to aging or disease. The aim of this article is to propose a generic and robust parametric morphing method for a subject-specific bone structure. We demonstrate this by using the proposed method on a model of a human proximal femur. Automatic segmentation algorithms are also presented to parameterize the specific model efficiently. A total of 48 femur models were evaluated for defining morphing vector fields. Also, several anatomical and mechanical functions of femur were considered as morphing constraints, and the NURBS interpolating technique was applied in the method to guarantee the generality of our morphed results.