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A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images

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Abstract

Purpose

Glomerular basement membrane segmentation is an ultimate step in several image processing applications for kidney diseases and abnormalities in microscopic images. However, extracting the glomerular basement membrane (GBM) regions accurately is considered challenging because of the large variants in the microscopic images. The contribution of this work is to propose a computer-aided detection system to provide accurate GBM segmentation.

Methods

A novel GBM segmentation algorithm is developed based on neutrsophic set and shearlet transform. Firstly, the shearlet features are extracted from the microscopic image samples using shearlet transform. Afterward, the neutrosophic image is defined using shearlet features, and the indeterminacy on the neutrosophic image is reduced using an α-mean operation. Lastly, the k-means clustering algorithm is applied to segment the neutrsophic image and the GBM is identified using its intensity feature.

Results

Three metrics, namely the average distance (AvgDist), the Hausdorff distance (Hdist), and percentage overlap area (POA); were employed to assess the proposed method performance. The results established that the proposed method achieved smaller distance errors and larger POAs. For the tested image, the average of AvgDist, HDist and POA are 1.99204, 4.59535 and 0.67857, respectively. The results established that the cases were segmented accurately using the proposed NS based shearlet transform.

Conclusions

The new method utilizing the shearlet features and neutrosophic set improved the accuracy of GBM segmentation. Further study is underway to improve an automated CAD system using the refined segmentation results.

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Acknowledgement

We have been greatly indebted Dr. Ahmed Ashour, Anatomy Department, Faculty of Medicine, Tanta University, Egypt, for providing the dataset in the current work.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA

    Yanhui Guo

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, China

    Baiqing Sun

Authors
  1. Yanhui Guo
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  2. Amira S. Ashour
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  3. Baiqing Sun
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Corresponding author

Correspondence to Yanhui Guo.

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Guo, Y., Ashour, A.S. & Sun, B. A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images. Health Inf Sci Syst 5, 15 (2017). https://doi.org/10.1007/s13755-017-0036-7

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