Název: | Automatická lokalizace a klasifikace jaterních lézí |
Další názvy: | Automatic localization and classification of liver lesions |
Autoři: | Ryba, Tomáš |
Vedoucí práce/školitel: | Železný, Miloš |
Datum vydání: | 2017 |
Nakladatel: | Západočeská univerzita v Plzni |
Typ dokumentu: | disertační práce |
URI: | http://hdl.handle.net/11025/28548 |
Klíčová slova: | diagnostic;liver;image processing;lesion;localization;classification;registration;saliency map;markov random fields;active contours;decision tree |
Klíčová slova v dalším jazyce: | diagnostic;liver;image processing;lesion;localization;classification;registration;saliency map;markov random fields;active contours;decision tree |
Abstrakt: | Computer-aided diagnostic (CAD) systems are widely used in technical and me\-di\-cal fields. Using the CAD systems in medicine allows the application of image processing methods as well as the methods of artificial intelligence. The purpose of the systems is to assists doctors. A radiologist needs to diagnose a great amount of image data, which is very focus-demanding work. Using a CAD system can support doctor's effectiveness in the sense of processing speed and/or accuracy. The goal of the thesis is to develop a CAD system for the automatic localization and subsequent classification of liver lesions. Liver cancer is mostly diagnosed from a differential diagnosis that consists of analysis of two serial CT screening. Because the screenings are taken at the time interval of several seconds, data registration needs to be performed. In both series the liver region is found using a fully autonomous method based on the Grow Cut algorithm and the results obtained are further refined by a localized active contour method. The liver region is then analyzed and searched for lesions. The localization of the lesion is performed by Markov Random Fields initialized with a combination of saliency maps. The lesions found are then paired-up and classified by a decision tree. |
Abstrakt v dalším jazyce: | Computer-aided diagnostic (CAD) systems are widely used in technical and me\-di\-cal fields. Using the CAD systems in medicine allows the application of image processing methods as well as the methods of artificial intelligence. The purpose of the systems is to assists doctors. A radiologist needs to diagnose a great amount of image data, which is very focus-demanding work. Using a CAD system can support doctor's effectiveness in the sense of processing speed and/or accuracy. The goal of the thesis is to develop a CAD system for the automatic localization and subsequent classification of liver lesions. Liver cancer is mostly diagnosed from a differential diagnosis that consists of analysis of two serial CT screening. Because the screenings are taken at the time interval of several seconds, data registration needs to be performed. In both series the liver region is found using a fully autonomous method based on the Grow Cut algorithm and the results obtained are further refined by a localized active contour method. The liver region is then analyzed and searched for lesions. The localization of the lesion is performed by Markov Random Fields initialized with a combination of saliency maps. The lesions found are then paired-up and classified by a decision tree. |
Práva: | Plný text práce je přístupný bez omezení. |
Vyskytuje se v kolekcích: | Disertační práce / Dissertations (KKY) |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
doctoral_thesis_Ryba.pdf | Plný text práce | 35,72 MB | Adobe PDF | Zobrazit/otevřít |
posudky-ODP-ryba.pdf | Posudek oponenta práce | 2,38 MB | Adobe PDF | Zobrazit/otevřít |
protokol-odp-ryba.pdf | Průběh obhajoby práce | 870,3 kB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/28548
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