Contrast Enhancement of Medical X-ray Images Based on Multiscale Limited Adaptive Histogram Equalization and Mathematical Morphology

The medical X-ray image is one of the images most widely applied in clinical applications. Because the lowdose X-ray image needed for imaging is of a low contrast, the X-ray image contrast enhancement is processed before the clinical application. A new algorithm for contrast enhancement of mammographic images was proposed in this paper. The approach was based on the multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator was applied to transform the image into different scale sub-band images. In addition, the high-frequency sub- images were equalized by contrast limited adaptive histogram equalization and low-pass subimages were processed by the mathematical morphology. Finally, the image of enhanced contrast was reconstructed from the Laplacian Gaussian pyramid coefficients of high or low frequencies modified by contrast limited adaptive histogram equalization and mathematical morphology respectively. The enhanced image was processed by a global non-linear operator. The experimental results show that the proposed algorithm is effective for the contrast enhancement of the medical X-ray image. The performances of the proposed algorithm were measured by contrast evaluation criterion for image and contrast improvement index.