Linear Models of Cumulative Distribution Function for Content based Medical Image Retrieval

We propose an Image matching method based n Cumulative Distribution Function (CDF). The CDF f the query and database Images are approximated by piecewise linear models with two parameters, slope and intercept at various grayscale intervals. The equations solving the least squares line fitting algorithm are very simple to form, due to closed form expressions The contiguous set of lines approximating the CDFs enables us to compare query and database images with corresponding estimated slopes and intercepts. As the dynamic range f CDF is from 0 to 1, images of different sizes can be compared. Approximation of CDFs with lines further reduces the dimension f the image features and thus improves the speed f matching. Also, the monotonically increasing CDF is well suited for approximations with lines. Resolving the CDF with lines f different lengths recasts the matching to a hierarchical methodology.