{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T04:17:16Z","timestamp":1728361036579},"reference-count":8,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2004,12,12]],"date-time":"2004-12-12T00:00:00Z","timestamp":1102809600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"},{"start":{"date-parts":[[2004,12,12]],"date-time":"2004-12-12T00:00:00Z","timestamp":1102809600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                        <jats:title>Background<\/jats:title>\n                        <jats:p>This paper analyzes the effect of the mean-square error principle on the optimization process using a Special Case of Hopfield Neural Network (SCHNN).<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Methods<\/jats:title>\n                        <jats:p>The segmentation of multidimensional medical and colour images can be formulated as an energy function composed of two terms: the sum of squared errors, and a noise term used to avoid the network to be stacked in early local minimum points of the energy landscape.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>Here, we show that the sum of weighted error, higher than simple squared error, leads the SCHNN classifier to reach faster a local minimum closer to the global minimum with the assurance of acceptable segmentation results.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusions<\/jats:title>\n                        <jats:p>The proposed segmentation method is used to segment 20 pathological liver colour images, and is shown to be efficient and very effective to be implemented for use in clinics.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1472-6947-4-22","type":"journal-article","created":{"date-parts":[[2005,1,13]],"date-time":"2005-01-13T14:51:15Z","timestamp":1105627875000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Modification of the mean-square error principle to double the convergence speed of a special case of Hopfield neural network used to segment pathological liver color images"],"prefix":"10.1186","volume":"4","author":[{"given":"Rachid","family":"Sammouda","sequence":"first","affiliation":[]},{"given":"Mohamed","family":"Sammouda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2004,12,12]]},"reference":[{"key":"50_CR1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/1053-4296(92)90019-H","volume":"2","author":"E Chaney","year":"1992","unstructured":"Chaney E, Pizer S: Defining anatomical structures from medical images. 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