X-Ray Imagery are largely used by doctors, especially for finding issues like fissure or broken bones, also are used in dentistry field or even in other fields of the medicine in order to find objects or materials inside the body. However, in many cases the simple observation of doctors can lead to mistakes or misdiagnoses. Therefore, there are important reasons to find ways to automate the diagnose process for medical images, and it has been, on the last decades, one of the most studied areas of research. The main problem with X-Ray Images is that they may be blurred, out of focus, with noisy, or even with improperly bright, which makes the examination or analysis more difficult. To treat such problems are being used image processing approaches that are leading to more accurate results. In this research work, we find the application of many image processing and computer vision algorithms to many different contexts and problematic situations which requires an exhaustive X-Ray image analysis in order to give more accurate result. Recent implementations have been commonly done using the C++ and Python programming languages and the OpenCV library and also new approaches employing machine learning techniques.