An enhanced random walk algorithm for delineation of head and neck cancers in PET studies.
An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesi...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 6; pp. 897 - 909 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research Journal Article |
| Publicado: |
Springer Nature
Jun2017
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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=123190310&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123190310 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2017 vid: 55 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 123190310 123190310 144127612 NLM27638108 123190310 10.1007/s11517-016-1571-0 NLM27638108 123190310 ppf: 897 ppct: 12 formats: fmt: @attributes: type: P tig: atl: An enhanced random walk algorithm for delineation of head and neck cancers in PET studies. aug: au: Stefano, Alessandro Vitabile, Salvatore Russo, Giorgio Ippolito, Massimo Sabini, Maria Sardina, Daniele Gambino, Orazio Pirrone, Roberto Ardizzone, Edoardo Gilardi, Maria Sabini, Maria Gabriella Gilardi, Maria Carla affil: Department of Biopathology and Medical Biotechnologies (DIBiMED) , University of Palermo , Palermo Italy sug: subj: Tomography, Emission-Computed Methods Head and Neck Neoplasms Diagnosis Image Processing, Computer Assisted Methods Phantoms, Imaging Algorithms Human ab: An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician's requirements in a radiotherapy environment. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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