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Spatial neighborhood intensity constraint (SNIC) and knowledge-based clustering framework for tumor region segmentation in breast histopathology images

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Abstract

Based on the Nottingham Histological Grading (NHG) system, precise and accurate segmentation of breast tumor regions is crucial, typically in the scoring decision in terms of tubule formation. The main purposes of this study are to propose a robust segmentation framework that could produce repeatable, reproducible, and cost-effective measurable outputs. A novel spatial neighborhood intensity constraint (SNIC) and knowledge-based clustering framework for breast tumor region segmentation are proposed in this study. The proposed clustering framework constitutes of five different stages. These stages are color normalization, segmentation and removal of nucleus cells, SNIC, FCM with knowledge-based initial centroids selection, and post-processing. The main contribution of the proposed clustering framework lies within its simplicity yet powerful clustering ability producing precise segmentation in breast tumor regions using histopathology images. The SNIC is meant to remove the original RGB intensity of the nucleus cells and replace it with new intensity using the spatial information from the neighborhood pixels. Additionally, a knowledge-based initial centroids selection method is implemented to facilitate the fuzzy clustering algorithm. The proposed SNIC and knowledge-based initial centroids selection methods are expected to ease the clustering stage producing complementary results. For hypothesis validation purposes, justifications and validations are performed to validate the applicability of the proposed SNIC and knowledge-based initial centroids selection. Both of the proposed methods are found robust and plausible. The proposed framework achieved overall Accuracy (Acc), F1 score (F1), Area Overlap Measure (AOM), and Combined Equal Importance (CEI) of 91.2%, 92.1%, 85.7%, and 90.1%, respectively. For benchmarking purposes, the output results are compared to four recent works. The proposed framework demonstrates its robustness by outperformed these works achieving better results in Acc, F1, AOM, and CEI by 5.8%, 5.9%, 8.5%, and 7.0% higher than that of the best performance work amongst the four recent works.

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Acknowledgements

The authors gratefully acknowledge the financial support from the Fundamental Research Grant Scheme (FRGS) under a grant number of FRGS/1/2016/SKK06/UNIMAP/02/3 from the Ministry of Higher Education Malaysia.

Funding

This study was funded by the Ministry of Higher Education Malaysia under Fundamental Research Grant Scheme (FRGS) (FRGS/1/2016/SKK06/UNIMAP/02/3).

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Authors and Affiliations

  1. Centre for Multimodal Signal Processing, Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University College (TARUC), Jalan Genting Kelang, Setapak, 53300, Kuala Lumpur, Malaysia

    Xiao Jian Tan

  2. Biomedical Electronic Engineering Programme, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia

    Nazahah Mustafa & Mohd Yusoff Mashor

  3. Department of Pathology, Hospital Tuanku Fauziah, Jalan Tun Abdul Razak, 01000, Kangar, Perlis, Malaysia

    Khairul Shakir Ab Rahman

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  1. Xiao Jian Tan
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  2. Nazahah Mustafa
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  4. Khairul Shakir Ab Rahman
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Correspondence to Xiao Jian Tan or Nazahah Mustafa.

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The protocol of this study had been approved by Medical Research and Committee of National Medical Research Register (NMRR) Malaysia referring to the protocol number: NMRR-17-281-34236.

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Tan, X.J., Mustafa, N., Mashor, M.Y. et al. Spatial neighborhood intensity constraint (SNIC) and knowledge-based clustering framework for tumor region segmentation in breast histopathology images. Multimed Tools Appl 81, 18203–18222 (2022). https://doi.org/10.1007/s11042-022-12129-2

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