Study on Cloud Image Processing and Analysis Based Flatfoot Classification Approach

Abstract

The Cloud Image Processing and Analysis based Flatfoot Classification approach is proposed in this study to assist doctors in determining flat feet. The proposed technique can eliminate noise and shape images of the foot based on X-ray images by using image processing and analysis set up on different virtual machines on the cloud. After image processing, the individual X-ray image is divided into four blocks using the proposed division approach, which takes into account the percentage of each foot. Each divided image can be given to distinct analysis algorithms for key-point discovery by dividing the original image into four individual sub-partitions. Each image can be processed based on individual virtual machine on cloud. The system can identify four decision points of each block using the proposed techniques implemented on cloud for individual sub-partitions of the original image. The system can automatically detect flat feet based on the integration of processing data from various algorithms. The information and identification results might also be given to the doctor for manual identification. Additionally, the decision point can be manually chosen. To put it another way, the system can produce more accurate and objective findings based on the doctor's selection. Based on the dpi of the X-ray image, the simulation shows that accuracy can be improved. Furthermore, the performance of different methods for determining decision points varies.