Decision Support System for Automatic Identification ROI for Medical Images

Abstract

newline Today bone fractures are very common in our country because of road accidents, sports injuries and falls. Patients with bone fractures who go into shock state have a mortality of 30-50%. When combined with other injuries in the body, for example, an abdominal injury, the chance of mortality rises even higher, approaching 100% in some cases. newlineThe X-Ray images are the most common means of medical imaging accessibility for people during the injuries and accidents. The numerous incidences necessitate the health care professionals to analyze a huge number of x-ray images. The use of computer-assisted automatic detection of fractures in X-Ray images can be a significant contribution for assisting the physicians in making faster and more accurate diagnostic decisions and fasten treatment planning. Among fractures, automatic detection is considered more challenging because they are different and variable in presentation and their outcomes are unpredictable. The research proposes an automatic algorithm (ROIMI) for detecting bone fracture in the X-Ray image. The proposed algorithm processes image step by step to obtain the results. The first step is segmentation. Segmentation facilitates the process partitioning bone for faster and better identification of the region of interest (ROI) from the X-ray image. The next step is to identify the region of interest. The region of interest (bone fracture) is a diagnostically important part of the analysis and suggesting the type of fracture based on its size. newline