Semi-automatic Segmentation of Brain Tumors Using Population and Individual Information.
Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) imag...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 4; pp. 786 - 797 |
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| Autores principales: | , , , , , |
| Formato: | algorithm diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2013
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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=104190871&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104190871 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2013 vid: 26 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104190871 88934666 10.1007/s10278-012-9568-1 NLM23319111 PMC3705006 104190871 ppf: 786 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Semi-automatic Segmentation of Brain Tumors Using Population and Individual Information. aug: au: Wu, Yao Yang, Wei Jiang, Jun Li, Shuanqian Feng, Qianjin Chen, Wufan affil: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515 China sug: subj: Brain Neoplasms Radiography Radiographic Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Image Processing, Computer Assisted Methods Automation Algorithms Evaluation Evaluation Research Human Funding Source ab: Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation. pubtype: Academic Journal doctype: algorithm diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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