Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG).
In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and t...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 1; pp. 133 - 145 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2014
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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=94061936&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 94061936 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2014 vid: 27 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 94061936 104013596 104013596 10.1007/s10278-013-9640-5 94061936 ppf: 133 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG). aug: au: Al-Faris, Ali Ngah, Umi Isa, Nor Shuaib, Ibrahim affil: Imaging and Computational Intelligence Research Group (ICI), School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Penang Malaysia sug: subj: Diagnosis, Computer Assisted Breast Radiography Magnetic Resonance Imaging Radiographic Image Enhancement Methods Radiographic Image Interpretation, Computer-Assisted Methods Breast Neoplasms Diagnosis Algorithms Artificial Intelligence Evaluation Research Analysis of Variance P-Value Sensitivity and Specificity ROC Curve Female Human Female ab: In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap ( p = 0.0002), misclassification rate ( p = 0.045), true negative fraction ( p = 0.0001) and sum of true volume fraction ( p = 0.0001). pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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