A Framework for Forgery Detection from the Digital X-Ray Images

Abstract

Due to high significance of medical images, the medical image analysis has become well recognized research area of computer science and recent advances in computational technology have boost up the process to alter the image, sound and video data so called (ISV)[1-4]. During the process of ISV capturing toeprinting there are faire chances of ISV forgery which could become leading case of misdiagnosis in medical images such as Digital X-rays (DX), CTs, MRIs and etc. Such unnecessary manipulation with medical image may become may vary the cost of treatment, however forgery detection (FD) at early stage would provide additional assistance to doctors to investigate the health condition patients in more effective way. Since the malicious intensions (MI) of human or malfunctioning of DXs would include extra noise into ISVs which could become of the leading cause of misdiagnosis but the pixel level image analytics could be used to detect such anomalies. This research offers a framework to detect the forgery in DXs and ISVs. The methodology comprises over three stages where fist stage prepares the data, second stage used for construction of decision model and thethird stage is used to visualize the results by using the confusion matrix and other measures. The classification accuracy was recorded as 96.90%.