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Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

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Abstract

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).

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Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Imaging and Computational Intelligence Research Group (ICI), School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia

    Ali Qusay Al-Faris, Umi Kalthum Ngah & Nor Ashidi Mat Isa

  2. Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, Penang, Malaysia

    Ibrahim Lutfi Shuaib

Authors
  1. Ali Qusay Al-Faris
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  2. Umi Kalthum Ngah
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  3. Nor Ashidi Mat Isa
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  4. Ibrahim Lutfi Shuaib
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Corresponding author

Correspondence to Umi Kalthum Ngah.

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Al-Faris, A.Q., Ngah, U.K., Isa, N.A.M. et al. Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG). J Digit Imaging 27, 133–144 (2014). https://doi.org/10.1007/s10278-013-9640-5

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  • DOI: https://doi.org/10.1007/s10278-013-9640-5

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