{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:34:33Z","timestamp":1717461273398},"reference-count":17,"publisher":"IGI Global","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,4,1]]},"abstract":"<p>SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have signi?cant impact on the shapes and orientations of tensors in di?usion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error\u2014Stein\u2019s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform.<\/p>","DOI":"10.4018\/jhisi.2010040108","type":"journal-article","created":{"date-parts":[[2010,4,19]],"date-time":"2010-04-19T23:05:09Z","timestamp":1271718309000},"page":"73-81","source":"Crossref","is-referenced-by-count":1,"title":["The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules"],"prefix":"10.4018","volume":"5","author":[{"given":"D.","family":"Selvathi","sequence":"first","affiliation":[{"name":"Mepco Schlenk Engineering College, India"}]},{"given":"S. Thamarai","family":"Selvi","sequence":"additional","affiliation":[{"name":"Anna University, India"}]},{"given":"C. Loorthu Sahaya","family":"Malar","sequence":"additional","affiliation":[{"name":"Mepco Schlenk Engineering College, India"}]}],"member":"2432","reference":[{"key":"jhisi.2010040108-0","article-title":"Activation Points Extraction and Noise Removal of fMRI Signal using Novel Local Cosine Technique (Tech. Rep.). Newark, NJ: University of Medicine and Dentistry of New Jersey","author":"D.Asefa","journal-title":"Department of Health Informatics."},{"key":"jhisi.2010040108-1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.906002"},{"key":"jhisi.2010040108-2","unstructured":"Cand`es, M., Demanet, L., Donoho, D., & Ying, L. (2006, March). Fast Discrete Curvelet Transforms (Tech. Rep. No. 94305). Stanford, CA: Stanford University."},{"key":"jhisi.2010040108-3","doi-asserted-by":"crossref","unstructured":"Donoho, D. L., & Duncan, M. R. (1999, November). Digital Curvelet Transform: Strategy, Implementation and Experiments (Tech. Rep.). Stanford, CA: Stanford University, Department of Statistics.","DOI":"10.1117\/12.381679"},{"key":"jhisi.2010040108-4","article-title":"The Very Fast Curvelet Transform.","author":"B.Eriksson","journal-title":"VLSI Structures for Digital Signal Processing"},{"key":"jhisi.2010040108-5","first-page":"1117","article-title":"Noise Removal Methods for High Resolution MRI. In","volume":"2","author":"R. L.Gregg","year":"1997","journal-title":"Proceedings of the Nuclear Science Symposium"},{"key":"jhisi.2010040108-6","unstructured":"Gupta, L. K. S., & Chauhan, R. C. (n.d.). Image Denoising using Wavelet Thresholding."},{"key":"jhisi.2010040108-7","unstructured":"Jiang, L., & Yang, W. (2003, December). Adaptive Magnetic Resonance Image Denoising Using Mixture Model and Wavelet Shrinkage. In Proceedings of the 7th Digital Image Computing: Techniques and Applications, Sydney, Australia (pp. 10-12)."},{"key":"jhisi.2010040108-8","doi-asserted-by":"publisher","DOI":"10.1109\/83.791966"},{"key":"jhisi.2010040108-9","unstructured":"Nowak, R. D., Gregg, R. L., Coopery, T. G., & Sieberty, J. E. (n.d.). Removing Rician Noise in MRI via Wavelet \u2013Domain Filtering."},{"key":"jhisi.2010040108-10","doi-asserted-by":"crossref","unstructured":"Parthiban, L., & Subramanian, R. (2006, September). Medical Image Denoising using X-lets. In Proceedings of the Annual India Conference (pp. 1-6).","DOI":"10.1109\/INDCON.2006.302763"},{"key":"jhisi.2010040108-11","doi-asserted-by":"publisher","DOI":"10.1016\/S0730-725X(97)00199-9"},{"issue":"1","key":"jhisi.2010040108-12","first-page":"21","article-title":"Denoising of Computer Tomography Images Using Curvelet Transform.","volume":"2","author":"R.Sivakumar","year":"2007","journal-title":"ARPN Journal of Engineering and Applied Sciences"},{"key":"jhisi.2010040108-13","author":"K. P.Soman","year":"2008","journal-title":"RamaChandran, K. I"},{"key":"jhisi.2010040108-14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2002.1014998"},{"key":"jhisi.2010040108-15","author":"Y.Wang","journal-title":"Total Variation Wavelet Based Medical Image Denoising"},{"key":"jhisi.2010040108-16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2008.924386"}],"container-title":["International Journal of Healthcare Information Systems and Informatics"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=42999","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T14:25:51Z","timestamp":1654093551000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jhisi.2010040108"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2010,4,1]]},"references-count":17,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2010,4]]}},"URL":"https:\/\/doi.org\/10.4018\/jhisi.2010040108","relation":{},"ISSN":["1555-3396","1555-340X"],"issn-type":[{"value":"1555-3396","type":"print"},{"value":"1555-340X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,4,1]]}}}