{"id":15743,"date":"2018-03-09T09:03:02","date_gmt":"2018-03-09T09:03:02","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/medical-image-registration-based-on-a-creaseness-measure\/"},"modified":"2018-03-09T09:03:02","modified_gmt":"2018-03-09T09:03:02","slug":"medical-image-registration-based-on-a-creaseness-measure","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/medical-image-registration-based-on-a-creaseness-measure\/","title":{"rendered":"Medical image registration based on a creaseness measure"},"content":{"rendered":"<h2>Tesis doctoral de <strong> David Lloret Vilallonga <\/strong><\/h2>\n<p>Esta tesis trata de la puesta en correspondencia autom\u00e1tica de im\u00e1genes m\u00e9dicas. Proponemos un algoritmo gen\u00e9rico, aplicable a im\u00e1genes con una caracter\u00edstica determinada: que contengan estructuras en forma de valle o de cresta. Estas estructuras se extraen autom\u00e1ticamente mediante un operador dise\u00f1ador para detectar su forma. Ejemplos de este tipo de estructuras son los huesos con tc y rm, capilares en im\u00e1genes oftalmol\u00f3gicas y los sulci en ecograf\u00edas. Una vez se han extra\u00eddo \u00e9stas, una de las im\u00e1genes se transforma iterativamente mediante una aproximaci\u00f3n jer\u00e1rquica hasta que la funci\u00f3n que detecta el alineamiento llega a un m\u00e1ximo.  hemos presentado informes completos del funcionamiento de este algoritmo para varias modalidades y condiciones de adquisici\u00f3n. En primer lugar, lo hemos aplicado a la puesta en correspondiencia de tc con rm. Hemos participado en un proyecto para evaluar nuestro algoritmo para un centenar de im\u00e1genes, compar\u00e1ndolo con otros m\u00e9todos, y los resultados, para algunas modalidades, han sido destacados.  otro campo de aplicaci\u00f3n han sido las im\u00e1genes oftalmol\u00f3gicas. En esta modalidad, nuestro algoritmo funcion\u00f3 mejor y m\u00e1s r\u00e1pido que los algoritmos existentes hasta la fecha, y adem\u00e1s ha podido ser aplicado a secuencias largas de im\u00e1genes slo. Ejecutamos pruebas exhaustivas para conseguir una convergencia r\u00e1pida y segura, lo que hizo posible, en colaboraci\u00f3n con otro grupo, construir un primer prototipo real para un hospital.  finalmente, hemos explorado diferentes problemas de puesta en correspondencia en el \u00e1rea de las ecograf\u00edas intra-operativas. Despu\u00e9s de construir un sistema inform\u00e1tico para capturar y localizar las im\u00e1genes en tiempo real, empezamos nuestros experimentos con un cerebro humano in vitro.  conseguimos construir un volumen con las im\u00e1genes, y alinearlo con una imagen rm del mismo. Adem\u00e1s, conseguimos registrar las im\u00e1genes individuales de las ecograf\u00edas<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Medical image registration based on a creaseness measure<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Medical image registration based on a creaseness measure <\/li>\n<li><strong>Autor:<\/strong>\u00a0 David Lloret Vilallonga <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Aut\u00f3noma de barcelona<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 22\/02\/2002<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Joan Serrat Gual<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Juan  jose Villanueva pipa\u00f3n <\/li>\n<li>l.g. Hill derek (vocal)<\/li>\n<li> Gonz\u00e1lez penedo Jos\u00e9 lu\u00eds (vocal)<\/li>\n<li>joan Molet teixid\u00f3 (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de David Lloret Vilallonga Esta tesis trata de la puesta en correspondencia autom\u00e1tica de im\u00e1genes m\u00e9dicas. Proponemos un [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[1890,199,2528,126,1525,21130],"tags":[50063,50065,50066,40204,15895,50064],"class_list":["post-15743","post","type-post","status-publish","format-standard","hentry","category-ciencia-de-los-ordenadores","category-fisica","category-inteligencia-artificial","category-matematicas","category-optica","category-tratamiento-digital-de-imagenes","tag-david-lloret-vilallonga","tag-gonzalez-penedo-jose-luis","tag-joan-molet-teixido","tag-joan-serrat-gual","tag-juan-jose-villanueva-pipaon","tag-l-g-hill-derek"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/15743","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=15743"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/15743\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=15743"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=15743"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=15743"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}