FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2139170612> ?p ?o ?g. }

• W2139170612 endingPage "98" @default.
• W2139170612 startingPage "87" @default.
• W2139170612 abstract "MR brain image segmentation into several tissue classes is of significant interest to visualize and quantify individual anatomical structures. Traditionally, the segmentation is performed manually in a clinical environment that is operator dependent and may be difficult to reproduce. Though several algorithms have been investigated in the literature for computerized automatic segmentation of MR brain images, they are usually targeted to classify image into a limited number of classes such as white matter, gray matter, cerebrospinal fluid and specific lesions. We present a novel model-based method for the automatic segmentation and classification of multi-parameter MR brain images into a larger number of tissue classes of interest. Our model employs 15 brain tissue classes instead of the commonly used set of four classes, which were of clinical interest to neuroradiologists for following-up with patients suffering from cerebrovascular deficiency (CVD) and/or stroke. The model approximates the spatial distribution of tissue classes by a Gauss Markov random field and uses the maximum likelihood method to estimate the class probabilities and transitional probabilities for each pixel of the image. Multi-parameter MR brain images with T1, T2, proton density, Gd+T1, and perfusion imaging were used in segmentation and classification. In the development of the segmentation model, true class-membership of measured parameters was determined from manual segmentation of a set of normal and pathologic brain images by a team of neuroradiologists. The manual segmentation was performed using a human–computer interface specifically designed for pixel-by-pixel segmentation of brain images. The registration of corresponding images from different brains was accomplished using an elastic transformation. The presented segmentation method uses the multi-parameter model in adaptive segmentation of brain images on a pixel-by-pixel basis. The method was evaluated on a set of multi-parameter MR brain images of a twelve-year old patient 48 h after suffering a stroke. The results of classification as compared to the manual segmentation of the same data show the efficacy and accuracy of the presented methods as well as its capability to create and learn new tissue classes." @default.
• W2139170612 created "2016-06-24" @default.
• W2139170612 creator A5016249965 @default.
• W2139170612 creator A5021363129 @default.
• W2139170612 creator A5022808200 @default.
• W2139170612 creator A5051335239 @default.
• W2139170612 creator A5074916784 @default.
• W2139170612 date "2000-04-01" @default.
• W2139170612 modified "2023-10-16" @default.
• W2139170612 title "Multi-level adaptive segmentation of multi-parameter MR brain images" @default.
• W2139170612 cites W1978754619 @default.
• W2139170612 cites W1984979148 @default.
• W2139170612 cites W1985294812 @default.
• W2139170612 cites W1999266396 @default.
• W2139170612 cites W2015513598 @default.
• W2139170612 cites W2023557257 @default.
• W2139170612 cites W2026272904 @default.
• W2139170612 cites W2026384753 @default.
• W2139170612 cites W2031754111 @default.
• W2139170612 cites W2032398883 @default.
• W2139170612 cites W2036084201 @default.
• W2139170612 cites W2039521343 @default.
• W2139170612 cites W2043127893 @default.
• W2139170612 cites W2045986827 @default.
• W2139170612 cites W2047984214 @default.
• W2139170612 cites W2050231893 @default.
• W2139170612 cites W2059509402 @default.
• W2139170612 cites W2063125200 @default.
• W2139170612 cites W2063163870 @default.
• W2139170612 cites W2073087131 @default.
• W2139170612 cites W2073362905 @default.
• W2139170612 cites W2108817119 @default.
• W2139170612 cites W2108843657 @default.
• W2139170612 cites W2115984740 @default.
• W2139170612 cites W2130520030 @default.
• W2139170612 cites W2145845118 @default.
• W2139170612 cites W2148846788 @default.
• W2139170612 cites W2149485626 @default.
• W2139170612 cites W2151507565 @default.
• W2139170612 cites W2163598482 @default.
• W2139170612 cites W2169132902 @default.
• W2139170612 cites W2171765626 @default.
• W2139170612 cites W2180554971 @default.
• W2139170612 cites W4240063595 @default.
• W2139170612 doi "https://doi.org/10.1016/s0895-6111(99)00042-7" @default.
• W2139170612 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/10767588" @default.
• W2139170612 hasPublicationYear "2000" @default.
• W2139170612 type Work @default.
• W2139170612 sameAs 2139170612 @default.
• W2139170612 citedByCount "45" @default.
• W2139170612 countsByYear W21391706122013 @default.
• W2139170612 countsByYear W21391706122014 @default.
• W2139170612 countsByYear W21391706122015 @default.
• W2139170612 countsByYear W21391706122016 @default.
• W2139170612 countsByYear W21391706122019 @default.
• W2139170612 countsByYear W21391706122021 @default.
• W2139170612 crossrefType "journal-article" @default.
• W2139170612 hasAuthorship W2139170612A5016249965 @default.
• W2139170612 hasAuthorship W2139170612A5021363129 @default.
• W2139170612 hasAuthorship W2139170612A5022808200 @default.
• W2139170612 hasAuthorship W2139170612A5051335239 @default.
• W2139170612 hasAuthorship W2139170612A5074916784 @default.
• W2139170612 hasConcept C124504099 @default.
• W2139170612 hasConcept C153180895 @default.
• W2139170612 hasConcept C154945302 @default.
• W2139170612 hasConcept C160633673 @default.
• W2139170612 hasConcept C19609008 @default.
• W2139170612 hasConcept C31972630 @default.
• W2139170612 hasConcept C41008148 @default.
• W2139170612 hasConcept C54170458 @default.
• W2139170612 hasConcept C65885262 @default.
• W2139170612 hasConcept C89600930 @default.
• W2139170612 hasConceptScore W2139170612C124504099 @default.
• W2139170612 hasConceptScore W2139170612C153180895 @default.
• W2139170612 hasConceptScore W2139170612C154945302 @default.
• W2139170612 hasConceptScore W2139170612C160633673 @default.
• W2139170612 hasConceptScore W2139170612C19609008 @default.
• W2139170612 hasConceptScore W2139170612C31972630 @default.
• W2139170612 hasConceptScore W2139170612C41008148 @default.
• W2139170612 hasConceptScore W2139170612C54170458 @default.
• W2139170612 hasConceptScore W2139170612C65885262 @default.
• W2139170612 hasConceptScore W2139170612C89600930 @default.
• W2139170612 hasIssue "2" @default.
• W2139170612 hasLocation W21391706121 @default.
• W2139170612 hasLocation W21391706122 @default.
• W2139170612 hasOpenAccess W2139170612 @default.
• W2139170612 hasPrimaryLocation W21391706121 @default.
• W2139170612 hasRelatedWork W1999008862 @default.
• W2139170612 hasRelatedWork W2022929107 @default.
• W2139170612 hasRelatedWork W2064771172 @default.
• W2139170612 hasRelatedWork W2103507220 @default.
• W2139170612 hasRelatedWork W2185902295 @default.
• W2139170612 hasRelatedWork W2945274617 @default.
• W2139170612 hasRelatedWork W3144569342 @default.
• W2139170612 hasRelatedWork W4205800335 @default.
• W2139170612 hasRelatedWork W4313052709 @default.
• W2139170612 hasRelatedWork W80586315 @default.
• W2139170612 hasVolume "24" @default.

• next