An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation.
Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near t...
| Published in: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2287 - 2311 |
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| Main Authors: | , , , |
| Format: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515379&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515379 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515379 181515379 181515379 10.1007/s10278-023-00899-6 181515379 ppf: 2287 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation. aug: au: Singh, Chandan Ranade, Sukhjeet Kaur Kaur, Dalvinder Bala, Anu affil: https://ror.org/00xdn8y92 Department of Computer Science, Punjabi University, 147002, Patiala, India sug: subj: Brain Radiography Magnetic Resonance Imaging Methods Clustering Algorithms Radiographic Image Interpretation, Computer-Assisted Logic Human Male Female Child Adolescence Adult Middle Age Aged Radiographic Image Enhancement Diagnostic Imaging Classification Data Security Validity Descriptive Statistics Cluster Analysis Image Processing, Computer Assisted Methods Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near the cluster boundaries. The discrete cosine transform (DCT) domain-based filtering is an effective way to deal with structural and photometric anomalies, while the intuitionistic fuzzy C-means (IFCM) clustering can handle the uncertainty using the intuitionistic fuzzy set (IFS) theory. In this background, we propose two novel approaches, namely, the DCT-based intuitionistic fuzzy C-means (DCT-IFCM) and the DCT-based local information IFCM (DCT-LIFCM), which effectively deal with the Rician and Gaussian noises and also handle the data uncertainty problem to provide high segmentation accuracy. The DCT-IFCM approach performs the histogram-based segmentation, while the DCT-LIFCM uses the pixel-wise computation to include the spatial information. Although the DCT-LIFCM delivers slightly better performance than the DCT-IFCM, the latter is very fast in providing equally high segmentation accuracy. An exhaustive performance analysis is provided to demonstrate the superior performance of the proposed algorithms compared with the state-of-the-art algorithms, including those based on the DCT-based filtering approach and the IFS theory. 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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