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dc.contributor.authorChitsaz, Mahsa
dc.contributor.authorWoo, Chaw Seng
dc.date.accessioned2017-05-03T13:51:22Z
dc.date.available2017-05-03T13:51:22Z
dc.date.issued2011
dc.date.modified2012-05-09T22:45:53Z
dc.identifier.issn10009000
dc.identifier.doi10.1007/s11390-011-9431-8
dc.identifier.urihttp://hdl.handle.net/10072/44706
dc.description.abstractMany image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom247
dc.relation.ispartofpageto255
dc.relation.ispartofissue2
dc.relation.ispartofjournalJournal of Computer Science and Technology
dc.relation.ispartofvolume26
dc.rights.retentionY
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode089999
dc.subject.fieldofresearchcode08
dc.titleSoftware Agent with Reinforcement Learning Approach for Medical Image Segmentation
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2011
gro.hasfulltextNo Full Text
gro.griffith.authorChitsaz, Mahsa


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