Novel approach to determine age and gender from wrist x-ray images

Abstract

Human gender and age prediction in field of forensic department is a very important and crucial stage in means of criminal and judicial law. Human identification is essential when it required for recognising a body in case of mass disaster and natural disaster like earth quake, floods, tsunamis, hurricanes and other geological process that causes huge damage for mankind and loss of life. Human bones during the growth stages undergo few substantial changes of size and shapes. In diagnosing growth of bones, x ray images are frequently used. Hand x ray images in particular has been chosen as a part of x ray imaging, since hand has more unique features and more number of parts. Manual technique of identification is also attainable, but this process can be adopted when medical practitioners, assistants and basic tools are available. Manual method can be carried out based on the availability of bone like skull, long bones, short bones, hand, pelvis bone etc. It requires ample time to process the accurate outcome of the available samples. Hence hand operated technique is not feasible for identification. A machine driven automated system for gender and age identification is essential to overcome the flaws occurred in manual technique. This would facilitate better outcome in lesser time, without intervention of labour and also enables quantitative and accurate assessments. In the proposed system, we have identified most important features from wrist bone which contributes in age and gender identification. Main aim of our study is to identify gender and estimation of age of Middle East population of Karnataka state in India by analysing digital images of wrist bone. Random forest classification algorithm is used to deploy this system by considering 76 male samples and 50 female samples in total 126 wrist radiographs of age group between 06 to 78 years old. Random forest classifier belongs to decision tree family, each decision tree when executed may tends to overfit that training data, but random forest avoids this over fitting problems and it will try to capture maximum amount of pattern. Since multiple decision trees are implemented in RFC, this makes it a power full classification algorithm that will predict results with higher accuracy most of the time. Accuracy of 97% is achieved in the present work for age and gender prediction.