Abstract
This paper presents the details of the design and implementation of a computerized approach to phalangeal age assessment. Histogram of a CR (Computed Radiography) hand image to be analyzed is standardized first in the pre-processing stage, together with its location and orientation. Then the PROI (Phalangeal Region Of Interests) is defined and found. After the PROI is located, the image is segmented to binary. Age assessment using neural network, following the feature extraction stage, completes our experiment. The morphological features of the epiphysis of proximal phalanx of the third digit and the epiphysis of the third metacarpal bones are extracted to classify the skeleton age. Finding out a simple but powerful feature vector for skeleton age assessment is the main contribution in this study. The simulation results using the features are satisfactory, and we can get high accuracy within one year error for the database we have so far.