Osteoarthritis is a degenerative joint disease which is hard to diagnose objectively and may vary based on the surgeon. This disease is usually diagnosed by measuring several characteristic features of Hip X-rays mainly the joint distance between the femoral head and acetabular cup. Hip joint distance reduction is a clear symptom of Osteoarthritis as it suggest cartilage disappearance. Hip joint distance metric involves segmentation of the femur and pelvis in X-rays, which is a challenging task because of contrast variations as well as external factors like anatomical and pose-variation. A multiscale approach based on Machine Learning is presented in this work for the segmentation of multiple bone structures. This technique uses landmark detection via data-driven joint estimation of image displacements and introduces a unique refinement step for improving the accuracy of detection. The detection is based on supervised learning using manually annotated landmarks. Therefore, the landmark placement along the edge of the bone has been covered in detail. The detected landmarks are then used to determine the joint distance in several locations along the hip joint. Aside from the segmentation technique, this work also introduces novel joint distance metrics which can be used to detect joint space narrowing. A detailed quantitative evaluation proved this work to be superior to the current state-of-the-art segmentation that handles multiple bone structures and is the first in evaluating the joint space width metric. We have also considered and discussed in brief the impact of such a system for diagnostic purposes.