Applied grey relational grade in spinal lesions imaging study

Most along with medical science progress, more complex medical imaging of physical illness can be operated and processed immediately into image. However, physical illness or whether there´s any growth of bone lesions and the disease can only be found when the patients feel pain and go to the hospital for examination and scanning. Therefore, the purpose of this study was to combine AR model and grey relational grade to analyze image of the thoracic cavity and spinal bone. It compares the spinal bone´s spur lesions development and offers a more precise reference for doctors and patients´ family members. First of all, this paper removes the noise to highlight the clarity of spinal bones image. Further, it makes grey relational grade of AR-Model toward the spinal bones image classification model. Then, it compares and determines the spinal bone spur lesion with the model and acts as an inference and prevention toward spinal bone spur disease. So, this paper proposes to do AR-model spectrum analysis toward medical images and makes each row´s image into 256 gray level predictions by means of grey relational grade. According to this, spinal bone prediction model can make a comparison and identify the spinal bone image more effectively. After being simulated and verified, the design of this paper can actually provide a clearer spinal bone form and offer an effective image comparison warning.