With the large volume of victims encountered in mass disasters, it has become important to automate forensic identification systems. Because of their survivability and diversity, the best candidates for postmortem biometric identification are the dental features. We introduced an identification system for identifying individuals based on their dental X-ray radiograph records. However, there are still some challenges to overcome. The dental X-ray radiographs sometimes suffer from the poor quality which badly affect the segmentation results, and consequently affect the overall identification system accuracy. The enhancement of the image quality is worth considering. In this research paper we present a technique for dental X-Ray radiographs enhancement by combining morphological operator and Retinex theory. The experimental results show that the technique is robust. We studied the performance of the segmentation module of our previously introduced system when applying our enhancement module prior to the segmentation step.