X-ray Absorptiometry Image Enhancement using Sparse Representation

Recently, the evaluating method of the bone mineral density (BMD) in X-ray absorptiometry image has been studied for the early diagnosis of osteoporosis which is known as a metabolic disease. The BMD, in general, is evaluated by calculating pixel intensity in the bone segmented regions. Accurate bone region extraction is extremely crucial for the BMD evaluation. So, a X-Ray image enhancement is needed to get precise bone segmentation. In this paper, we propose an image enhancement method of X-ray image having multiple noise based sparse representation. To evaluate the performance of proposed method, we employ the contrast to noise ratio (CNR) metric and cut-view graphs visualizing image enhancement performance. Experimental results show that the proposed method outperforms the BayesShrink noise reduction methods and the previous noise reduction method in sparse representation with general noise model.

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