The Fels method is a well-known method for assessing the skeletal maturity from hand-wrist X-ray images. This method estimates the skeletal maturity age by manually grading multiple indicators for different hand-wrist bones. Due to the large number of indicators that need to be measured, this is a time-consuming task, especially with large databases of X-ray images. Furthermore, it can be a very subjective task that depends on the observer. Therefore, the need for automation of this process is in high demand. In this study, we have proposed a semi-automatic method to grade a sub-set of Fels indicators. This method is composed of four main steps of pre-processing, ROI extraction, segmentation, and Fels indicator grading. The most challenging step of the algorithm is to segment different bones in the Fels regions of interest (wrist, Finger I, III and V ROIs) which have been done using local Otsu thresholding and active contour filtering. The result of segmentation is evaluated visually on a subset of Fels study data set.