Multispectral analysis of multimodal images.
INTRODUCTION: An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially...
| Publicado en: | Acta Oncologica Vol. 48; no. 2; pp. 277 - 285 |
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| Autores principales: | , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts website Journal Article |
| Publicado: |
Medical Journals Sweden AB
Mar2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105644680&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105644680 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0284186X B84 jtl: Acta Oncologica issn: 0284186X maglogo: N pubinfo: dt: Mar2009 vid: 48 iid: 2 pid: 59195 pub: Medical Journals Sweden AB artinfo: ui: 105644680 105644680 2010185969 10.1080/02841860802290516 NLM18752080 105644680 ppf: 277 ppct: 8 formats: tig: atl: Multispectral analysis of multimodal images. aug: au: Kvinnsland Y Brekke N Taxt TM Grüner R affil: Department of Surgical Sciences, University of Bergen, Bergen, Norway. yngve@nordicimaginglab.com sug: subj: Brain Neoplasms Diagnosis Brain Neoplasms Therapy Diagnosis, Differential Radiotherapy Stroke Diagnosis Algorithms Data Analysis Software Funding Source Magnetic Resonance Imaging Norway World Wide Web Human ab: INTRODUCTION: An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially as unlimited number of images can be combined, and tissue properties across the images can be extracted automatically. MATERIALS AND METHODS: We have developed a software solution for MSA containing two algorithms for unsupervised classification, an EM-algorithm finding multinormal class descriptions and the k-means clustering algorithm, and two for supervised classification, a Bayesian classifier using multinormal class descriptions and a kNN-algorithm. The software has an efficient user interface for the creation and manipulation of class descriptions, and it has proper tools for displaying the results. RESULTS: The software has been tested on different sets of images. One application is to segment cross-sectional images of brain tissue (T1- and T2-weighted MR images) into its main normal tissues and brain tumors. Another interesting set of images are the perfusion maps and diffusion maps, derived images from raw MR images. The software returns segmentations that seem to be sensible. DISCUSSION: The MSA software appears to be a valuable tool for image analysis with multimodal images at hand. It readily gives a segmentation of image volumes that visually seems to be sensible. However, to really learn how to use MSA, it will be necessary to gain more insight into what tissues the different segments contain, and the upcoming work will therefore be focused on examining the tissues through for example histological sections. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts website Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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