Lumbar Ultrasound Image Feature Extraction and Classification with Support Vector Machine.
Needle entry site localization remains a challenge for procedures that involve lumbar puncture, for example, epidural anesthesia. To solve the problem, we have developed an image classification algorithm that can automatically identify the bone/interspinous region for ultrasound images obtained from...
| Publicado en: | Ultrasound in Medicine & Biology Vol. 41; no. 10; pp. 2677 - 2690 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Oct2015
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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=109639115&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109639115 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03015629 JJ6 jtl: Ultrasound in Medicine & Biology issn: 03015629 maglogo: N pubinfo: dt: Oct2015 vid: 41 iid: 10 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 109639115 NLM26119460 2013152205 10.1016/j.ultrasmedbio.2015.05.015 NLM26119460 109639115 ppf: 2677 ppct: 13 formats: tig: atl: Lumbar Ultrasound Image Feature Extraction and Classification with Support Vector Machine. aug: au: Yu, Shuang Tan, Kok Kiong Sng, Ban Leong Li, Shengjin Sia, Alex Tiong Heng sug: ab: Needle entry site localization remains a challenge for procedures that involve lumbar puncture, for example, epidural anesthesia. To solve the problem, we have developed an image classification algorithm that can automatically identify the bone/interspinous region for ultrasound images obtained from lumbar spine of pregnant patients in the transverse plane. The proposed algorithm consists of feature extraction, feature selection and machine learning procedures. A set of features, including matching values, positions and the appearance of black pixels within pre-defined windows along the midline, were extracted from the ultrasound images using template matching and midline detection methods. A support vector machine was then used to classify the bone images and interspinous images. The support vector machine model was trained with 1,040 images from 26 pregnant subjects and tested on 800 images from a separate set of 20 pregnant patients. A success rate of 95.0% on training set and 93.2% on test set was achieved with the proposed method. The trained support vector machine model was further tested on 46 off-line collected videos, and successfully identified the proper needle insertion site (interspinous region) in 45 of the cases. Therefore, the proposed method is able to process the ultrasound images of lumbar spine in an automatic manner, so as to facilitate the anesthetists' work of identifying the needle entry site. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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