title: Nonreference medical image edge map measure.
abstract: Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.
authors.affiliation: Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA.
authors.forename: Karen
authors.initials: K
authors.lastname: Panetta
authors.affiliation: Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA.
authors.forename: Chen
authors.identifier: 0000-0001-8700-1409
authors.initials: C
authors.lastname: Gao
authors.affiliation: Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA.
authors.forename: Sos
authors.initials: S
authors.lastname: Agaian
authors.affiliation: MIT Lincoln Laboratory, Lexington, MA 02421, USA.
authors.forename: Shahan
authors.identifier: 0000-0003-4879-7365
authors.initials: S
authors.lastname: Nercessian