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Cited by 15Medical Image Segmentation TechniquesAdvanced Clustering Algorithms ResearchMedical Imaging and Analysis

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Segmentation and Boundary Detection of Fetal Kidney Images in Second and Third Trimesters Using Kernel-Based Fuzzy Clustering

Meenakshi, S., Suganthi, M., Sureshkumar, P.·2019·Journal of Medical Systems
DOI 10.1007/s10916-019-1324-3
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Abstract

Organ segmentation is an important step in Ultrasound fetal images for early prediction of congenital abnormalities and to estimate delivery date. In many applications of 2D medical imaging, they face problems with speckle noise and object contours. Frequent scanning of fetal leads to clinical disturbances to the fetal growth and the quantitative interpretation of Ultrasonic images also a difficult task compared to other image modalities. In the present work a three-stage hybrid algorithm has been developed to segment the US fetal kidney images for the detection of shape and contour. At the first stage the hybrid Mean Median (Hybrid MM) filter is applied to reduce the speckle noise. Then a kernel based Fuzzy C - means clustering is used to detect the shape and contour. Finally, the texture features are obtained from the segmented images. Based on the obtained texture features, the abnormalities are detected. The Gaussian Radial basis function provides an accuracy of 80% at the second and third trimesters with weighted constant ranging from 4 to 8, compared to other global kernel functions. Similarly the proposed method has an accuracy of 86% with compared to other FCM techniques.

Overview

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Crossref · 10.1007/s10916-019-1324-3

Cites

31 of 31 references verified0 citation errors · 0 not checkable

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Citations

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Research works printed but not linked: 2

  1. Khare, P., and Mittal, N., Analysis of Medical Image Segmentation Techniques. International Bulletin of Mathematical Research 02(1), 2015.
  2. Xie, J. et al., Segmentation of Kidney from Ultrasound Images Based on Texture and Shape Priors. IEEE Trans. Med. Imaging 24(1):2202–2210, 2005.

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Metadata

Authors
Meenakshi, S., Suganthi, M., & Sureshkumar, P.
Corresponding author
Meenakshi, S.
ORCID
Meenakshi, S.
Venue
Journal of Medical Systems
Publisher
Springer Science and Business Media LLC
Published
2019-05-27
Volume
43
Issue
7
Pages
203-203
ISSN
  • 0148-5598 (ISSN-L)
  • 1573-689X
DOI
10.1007/s10916-019-1324-3
OpenAlex
W2946899660
Cited by
15
References
31
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 6 · 2022: 1 · 2021: 2 · 2020: 2
FWCI
0.56
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of Medical Systems (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
prenatal ultrasound, fuzzy c-means clustering, kernel clustering, Gaussian kernel, speckle reduction, boundary detection, texture features, congenital malformations, gestational age estimation, second and third trimester
MeSH terms
Algorithms, Female, Humans, Image Processing, Computer-Assisted, Kidney, Pregnancy, Pregnancy Trimester, Third, Ultrasonography, Prenatal, Fuzzy Logic

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