Vertebra osteoporosis detection based on bone density using Index-Singh statistical blended method

Abstract

Osteoporosis is a progressive decrease in bone density so that the bones become brittle and broken. Bones are composed of minerals such as calcium and phosphate, so the bones become hard and solid. Many people do not realize that osteoporosis is a silent disease. Therefore, early detection of osteoporosis is very important. Detection of osteoporosis can be done by utilizing x-ray images of the vertebra. In this research the detection of bone density using blended statistical methods and Index-Singh. The x-ray sample used in this research was 50 images of osteoporosis patients. The result of the area calculation yields the highest white pixel is 7,983 pixels and the lowest white pixel is 5,410 pixels. Based on the results of these calculations, a statistical grouping is conducted into 6 Index-Singh. The range of statistical values is 5,410–6,266 pixels grouped into Index-Singh 1, range of data 6,323–6,512 pixels grouped into Index-Singh 2, the data range 6,520-6,747 pixels grouped into Index-Singh 3, data range 6,778-6,998 pixels grouped into Index-Singh 4, data range 7,001-7,219 pixels grouped into Index-Singh 5, and data range 7,338-7,983 pixels grouped into Index-Singh 6. Overall, the results of testing the osteoporosis detection system have been successful and can be used as an early detection system for osteoporosis. This assistance system has a detection accuracy of 76% compared to doctor's justification.