A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear.
Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need f...
| Publicado en: | Journal of Digital Imaging Vol. 29; no. 6; pp. 677 - 696 |
|---|---|
| Autores principales: | , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2016
|
| 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=119539189&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119539189 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2016 vid: 29 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 119539189 119539189 119539189 10.1007/s10278-016-9884-y 119539189 ppf: 677 ppct: 19 formats: fmt: @attributes: type: P tig: atl: A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear. aug: au: Zarandi, M. Khadangi, A. Karimi, F. Turksen, I. affil: Department of Industrial Engineering , Amirkabir University of Technology , Tehran Iran sug: subj: Radiographic Image Interpretation, Computer-Assisted Meniscal Injuries Diagnosis Knee Joint Logic Algorithms Image Processing, Computer Assisted Magnetic Resonance Imaging Adult Middle Age Female Male Aged Meniscal Injuries Classification Diagnosis, Computer Assisted Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper presents a type-2 fuzzy expert system for meniscal tear diagnosis using PD magnetic resonance images (MRI). The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. λ-nhancement algorithm is used to perform the pre-processing step. For the segmentation step, first, Interval Type-2 Fuzzy C-Means (IT2FCM) is applied to the images, outputs of which are then employed by Interval Type-2 Possibilistic C-Means (IT2PCM) to perform post-processes. Second stage concludes with re-estimation of ' η' value to enhance IT2PCM. Finally, a Perceptron neural network with two hidden layers is used for Classification stage. The results of the proposed type-2 expert system have been compared with a well-known segmentation algorithm, approving the superiority of the proposed system in meniscal tear recognition. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|