In this paper, a new method of bone density measurement from X-ray images using an automatic image processing system is described. It starts with loading the X-ray image followed by cutting off unwanted background information. It converts the image into grayscale and adaptive histogram equalization to provide enhanced contrast such that the structures of the bone are more apparent. A thresholding algorithm is applied to divide the bone regions on the basis of pixel intensity values. Morphological processing of opening and hole filling is applied in a bid to improve the binary mask and eliminate holes and noise in the segmented bone structure. Connected component analysis applies in detecting the maximum connected region as opposed to the bone area and separation for analysis. For precision, the mask is opened up to comprise all possible bone structures, optimized thereafter to keep the bone structures intact. Following the optimization of the segmentation, the improved image is processed for delineation of the bone regions, and pixel intensity values for the bone regions are reaped for analysis. Bone density is calculated by computing the average intensity of the segmented bone pixels. The apparatus provides a cost-effective substitute to the conventional bone density measuring methods, thus experiencing wide application within health centers that do not have convenient access to advanced diagnosis machinery. The findings affirm the feasibility of the method in maintaining accurate bone density measurements with significant implications toward the early detection and treatment of bone diseases. This paper highlights the use of image processing procedures to enhance healthcare access and diagnostics in resource-limited settings.