Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further\nassist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is\nnecessary to have a reliable edgemeasure to assist in selecting the optimal edgemap. Existing reference based edgemeasures require\na ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground\ntruth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing\napplications. In this paper, a non reference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis\nis that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The\nNREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge\nmeasure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results\nshow that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.
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