Human Skeletal Image Enhancement Processing

In this document, we explore methods for human skeletal image enhancement processing and implement this process using the MATLAB language. Human skeletal image enhancement represents a crucial technique in the field of medical image processing that enables clearer observation and analysis of human bone structures. Through MATLAB implementation, we can apply various image processing algorithms and filters to enhance image quality and detail resolution. These algorithms typically involve contrast enhancement techniques like histogram equalization, noise reduction filters such as median or Gaussian filters, and edge detection operators including Sobel or Canny algorithms. The implementation approach generally follows these key steps: image preprocessing, contrast adjustment using imadjust() or histeq() functions, noise reduction with medfilt2() or imgaussfilt(), and edge enhancement through edge detection functions. Such processing methods significantly improve image contrast, reduce noise interference, and enhance boundary definition. Consequently, by implementing human skeletal image enhancement techniques, we obtain more accurate and reliable imaging results, thereby facilitating better understanding of human anatomical structures and physiological functions.