{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T19:40:24Z","timestamp":1737229224203,"version":"3.33.0"},"reference-count":15,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2007,3,21]],"date-time":"2007-03-21T00:00:00Z","timestamp":1174435200000},"content-version":"vor","delay-in-days":5192,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems &amp;amp; Computers in Japan"],"published-print":{"date-parts":[[1993,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Statistical quantum noise is a major factor causing degradation in emission computed\u2010tomography (ECT) images. The maximum<jats:italic>a posteriori<\/jats:italic>probability (MAP) estimation has been used to mitigate the noise problem with limited success. The two major shortcomings are the oversmoothing of edges by the homogeneous random field used as the image model, and the lack of well\u2010defined procedure for estimating the parameters of the image model.<\/jats:p><jats:p>This paper proposes a new algorithm for reconstructing the ECT images that will solve the problems of the existing methods. The proposed algorithm uses a compound Gauss\u2010Markov random field that can accurately model the sharp transition of edge regions as well as the smoothness of the bland regions. The image model parameters are derived from observed data using the maximum likelihood (ML) estimation, and the image reconstruction is based upon the joint MAP estimation. These two estimation problems are solved simultaneously in a joint MAP\u2010ML estimation scheme. The optimum solution of the joint MAP\u2010ML estimation problem is obtained using the generalized expectation\u2010maximization (GEM) algorithm.<\/jats:p>","DOI":"10.1002\/scj.4690240408","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T00:43:55Z","timestamp":1183855435000},"page":"78-87","source":"Crossref","is-referenced-by-count":1,"title":["Reconstruction of emission tomographic images using the compound gauss\u2010markov random field"],"prefix":"10.1002","volume":"24","author":[{"given":"Hiroyuki","family":"Kudo","sequence":"first","affiliation":[]},{"given":"Tsuneo","family":"Saito","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2007,3,21]]},"reference":[{"volume-title":"Principles of Computerized Tomographic Imaging","year":"1987","author":"Kak A. C.","key":"e_1_2_1_2_2"},{"volume-title":"Image Reconstruction from Projections","year":"1980","author":"Herman G. 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