{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:24:34Z","timestamp":1774293874698,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"15","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Alignment of medical images is a vital component of a large number of applications throughout the clinical track of events; not only within clinical diagnostic settings, but prominently so in the area of planning, consummation and evaluation of surgical and radiotherapeutical procedures. However, image registration of medical images is challenging because of variations on data appearance, imaging artifacts and complex data deformation problems. Hence, the aim of this study is to develop a robust image alignment method for medical images.<\/jats:p>\n               <jats:p>Results: An improved image registration method is proposed, and the method is evaluated with two types of medical data, including biological microscopic tissue images and dental X-ray images and compared with five state-of-the-art image registration techniques. The experimental results show that the presented method consistently performs well on both types of medical images, achieving 88.44 and 88.93% averaged registration accuracies for biological tissue images and X-ray images, respectively, and outperforms the benchmark methods. Based on the Tukey\u2019s honestly significant difference test and Fisher\u2019s least square difference test tests, the presented method performs significantly better than all existing methods (P \u2264 0.001) for tissue image alignment, and for the X-ray image registration, the proposed method performs significantly better than the two benchmark b-spline approaches (P &amp;lt; 0.001).<\/jats:p>\n               <jats:p>Availability: The software implementation of the presented method and the data used in this study are made publicly available for scientific communities to use (http:\/\/www-o.ntust.edu.tw\/\u223ccweiwang\/ImprovedImageRegistration\/).<\/jats:p>\n               <jats:p>Contact: \u00a0cweiwang@mail.ntust.edu.tw<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt309","type":"journal-article","created":{"date-parts":[[2013,5,30]],"date-time":"2013-05-30T00:18:49Z","timestamp":1369873129000},"page":"1879-1887","source":"Crossref","is-referenced-by-count":23,"title":["Improved image alignment method in application to X-ray images and biological images"],"prefix":"10.1093","volume":"29","author":[{"given":"Ching-Wei","family":"Wang","sequence":"first","affiliation":[{"name":"1 Graduate Institute of Biomedical Engineering, and 2Graduate Institute of Applied Science and Technology, Honors College National Taiwan University of Science and Technology, Taipei City, 10607 Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsiang-Chou","family":"Chen","sequence":"additional","affiliation":[{"name":"1 Graduate Institute of Biomedical Engineering, and 2Graduate Institute of Applied Science and Technology, Honors College National Taiwan University of Science and Technology, Taipei City, 10607 Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,5,29]]},"reference":[{"key":"2023012810452192400_btt309-B1","doi-asserted-by":"crossref","first-page":"e22835","DOI":"10.1371\/journal.pone.0022835","article-title":"Facilitating tumor functional assessment by spatially relating 3D tumor histology and in vivo MRI: image registration approach","volume":"6","author":"Alic","year":"2011","journal-title":"PLoS One"},{"key":"2023012810452192400_btt309-B2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0021-9045(91)90025-6","article-title":"A vector spline approximation","volume":"67","author":"Amodei","year":"1991","journal-title":"J. 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