Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network.
Epicardial adipose tissue is a visceral fat that has remained an entity of concern for decades owing to its high correlation with coronary heart disease. It continues to stump medical practitioners on the pretext of its relevance with pericardial fat and its dependence on a numerous other parameters...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 5 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2019
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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=136129197&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136129197 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2019 vid: 43 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136129197 136129197 136129197 10.1007/s10916-019-1227-3 136129197 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network. aug: au: Priya, C. Sudha, S. affil: Department of ECE, Syed Ammal Engineering College, Landhai, India sug: subj: Neural Networks (Computer) Methods Adipose Tissue Heart Algorithms Methods Diagnosis, Computer Assisted Signal Processing, Computer Assisted Insects Machine Learning Magnetic Resonance Imaging Tomography, X-Ray Computed Human ab: Epicardial adipose tissue is a visceral fat that has remained an entity of concern for decades owing to its high correlation with coronary heart disease. It continues to stump medical practitioners on the pretext of its relevance with pericardial fat and its dependence on a numerous other parameters including ethnicity and physique of an individual. This calls for a fool-proof algorithm that promises accurate classification and segmentation, hence an immaculate prediction. CT is immensely popular and widely preferred for diagnosis. Implementation of an improvised algorithm in CT would be a natural necessity. This research work proposes a Fruitfly Algorithm based Modified region growing algorithm is applied to the acquired CT images to segment fat accurately. The proposed methodology promises image registration and classification in order to segment two cardiac fats namely epicardial, pericardial and mediastinal. The main contributions are (1) Fat feature extraction: Construction of GLCM features CT image (2) Development of GWO based optimal neural network for classification; (3) Modeling the fat segmentation using modified region growing algorithm with Fruitfly optimization. The entire experimentation has been implemented in MATLAB simulation environment and final result is expected to flaunt a definite distinction between cardiac mediastinal and epicardial fats. Parallely, the accuracy, sensitivity, specificity, FPR and FNR have been stated and contrasted methodically with the existing methodology. This venture aims at spurring the healthcare industry towards smarter computational techniques that multiplies efficiency manifold. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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