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Histological Image Segmentation with Fuzzy Clustering Method

Yıl 2021, Sayı: 22, 393 - 399, 31.01.2021
https://doi.org/10.31590/ejosat.836329

Öz

In this study, a method for nuclei image segmentation in histopathological images is proposed. This method is based on a fuzzy clustering method, which is pre-trained on an supplementary domain with very large labeled images, and coupled with an additional network composed of fully connected layers. In this study, Fuzzy Clustering Mean (FCM) was used for clustering and segmentation and the effective ways for breast cancer nuclei detection were obtained. Wherefore, fuzzy clustering means have been used to detect the centers of breast cancer nuclei, then the extracted centers were compared with the ground truth samples. It is worth mentioning, that this work passes through many experimental stages, of detection and segmentation by applying a combination of more than one effective method. 

Kaynakça

  • M. Ilse, J. M. Tomczak, and M. Welling, “Deep multiple instance learning for digital histopathology,” in Handbook of Medical Image Computing and Computer Assisted Intervention, Elsevier, 2020, pp. 521–546.
  • A. ter Telgte et al., “Histopathology of diffusion-weighted imaging-positive lesions in cerebral amyloid angiopathy,” Acta Neuropathol., pp. 1–14, 2020.
  • R. B. Dettmeyer, Forensic histopathology: fundamentals and perspectives. Springer, 2018.
  • J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The Fuzzy c-Means Clustering Algorithm Computer & Geosciences,” Volume, vol. 10, pp. 2–3, 1984.
  • E. H. Ruspini, J. C. Bezdek, and J. M. Keller, “Fuzzy clustering: A historical perspective,” IEEE Comput. Intell. Mag., vol. 14, no. 1, pp. 45–55, 2019.
  • M.-S. Yang, “A survey of fuzzy clustering,” Math. Comput. Model., vol. 18, no. 11, pp. 1–16, 1993.
  • R. Bhukya and J. Gyani, “Survey on Fuzzy Associative Classifications Techniques and Their Performance Evaluation with Different Fuzzy Clustering Techniques Over Big Data,” in ICDSMLA 2019, Springer, 2020, pp. 420–431.
  • X. Zhu, S. Zhang, Y. Zhu, W. Zheng, and Y. Yang, “Self-weighted multi-view fuzzy clustering,” ACM Trans. Knowl. Discov. from data, vol. 14, no. 4, pp. 1–17, 2020.
  • H. Irshad et al., “Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd,” in Pacific symposium on biocomputing Co-chairs, 2014, pp. 294–305.
  • S. Roy, D. Bhattacharyya, S. K. Bandyopadhyay, and T.-H. Kim, “An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI,” Comput. Methods Programs Biomed., vol. 140, pp. 307–320, 2017.
  • H. E. Atlason, A. Love, S. Sigurdsson, V. Gudnason, and L. M. Ellingsen, “Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder,” in Medical Imaging 2019: Image Processing, 2019, vol. 10949, p. 109491H.
  • M. Sahnoun, F. Kallel, M. Dammak, C. Mhiri, K. Ben Mahfoudh, and A. Ben Hamida, “A comparative study of MRI contrast enhancement techniques based on Traditional Gamma Correction and Adaptive Gamma Correction: Case of multiple sclerosis pathology,” in 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2018, pp. 1–7.
  • A. S. Abdullah, Y. E. Özok, and J. Rahebi, “A novel method for retinal optic disc detection using bat meta-heuristic algorithm,” Med. Biol. Eng. Comput., vol. 56, no. 11, pp. 2015–2024, 2018.
  • A. A. I. Mohamed, M. M. Ali, K. Nusrat, J. Rahebi, A. Sayiner, and F. Kandemirli, “Melanoma skin cancer segmentation with image region growing based on fuzzy clustering mean,” Int. J. Eng. Innov. Res., vol. 6, no. 2, p. 91C95, 2017.
  • I. A. Masoud Abdulhamid, A. Sahiner, and J. Rahebi, “New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation,” Biomed Res. Int., vol. 2020, 2020.

Bulanık Kümeleme Yöntemi ile Histolojik Görüntü Segmentasyonu

Yıl 2021, Sayı: 22, 393 - 399, 31.01.2021
https://doi.org/10.31590/ejosat.836329

Öz

Bu çalışmada, histopatolojik görüntülerde çekirdek meme kanseri tespiti ve segmentasyonu için bir yaklaşım önerilmektedir. Bu yaklaşım, çok büyük etiketli görüntülere sahip bir yardımcı alan üzerinde önceden eğitilmiş ve tamamen bağlı katmanlardan oluşan ek bir ağ ile birleştirilen bulanık bir kümeleme yöntemine dayanmaktadır. Bu çalışmada, Fuzzy Clustering Mean (FCM) kümeleme ve segmentasyon için kullanılmış ve meme kanseri çekirdek tespiti için etkili yollar elde edilmiştir. Bu nedenle, göğüs kanseri çekirdeklerinin merkezlerini tespit etmek için bulanık bir kümeleme ortalaması kullanılmış, daha sonra çıkarılan merkezler kesin gerçek örnekleriyle karşılaştırılmıştır. Bu çalışmanın, birden fazla etkili yöntemin bir kombinasyonunu uygulayarak birçok deneysel, algılama ve bölümleme aşamasından geçtiğini belirtmekte fayda var.

Kaynakça

  • M. Ilse, J. M. Tomczak, and M. Welling, “Deep multiple instance learning for digital histopathology,” in Handbook of Medical Image Computing and Computer Assisted Intervention, Elsevier, 2020, pp. 521–546.
  • A. ter Telgte et al., “Histopathology of diffusion-weighted imaging-positive lesions in cerebral amyloid angiopathy,” Acta Neuropathol., pp. 1–14, 2020.
  • R. B. Dettmeyer, Forensic histopathology: fundamentals and perspectives. Springer, 2018.
  • J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The Fuzzy c-Means Clustering Algorithm Computer & Geosciences,” Volume, vol. 10, pp. 2–3, 1984.
  • E. H. Ruspini, J. C. Bezdek, and J. M. Keller, “Fuzzy clustering: A historical perspective,” IEEE Comput. Intell. Mag., vol. 14, no. 1, pp. 45–55, 2019.
  • M.-S. Yang, “A survey of fuzzy clustering,” Math. Comput. Model., vol. 18, no. 11, pp. 1–16, 1993.
  • R. Bhukya and J. Gyani, “Survey on Fuzzy Associative Classifications Techniques and Their Performance Evaluation with Different Fuzzy Clustering Techniques Over Big Data,” in ICDSMLA 2019, Springer, 2020, pp. 420–431.
  • X. Zhu, S. Zhang, Y. Zhu, W. Zheng, and Y. Yang, “Self-weighted multi-view fuzzy clustering,” ACM Trans. Knowl. Discov. from data, vol. 14, no. 4, pp. 1–17, 2020.
  • H. Irshad et al., “Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd,” in Pacific symposium on biocomputing Co-chairs, 2014, pp. 294–305.
  • S. Roy, D. Bhattacharyya, S. K. Bandyopadhyay, and T.-H. Kim, “An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI,” Comput. Methods Programs Biomed., vol. 140, pp. 307–320, 2017.
  • H. E. Atlason, A. Love, S. Sigurdsson, V. Gudnason, and L. M. Ellingsen, “Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder,” in Medical Imaging 2019: Image Processing, 2019, vol. 10949, p. 109491H.
  • M. Sahnoun, F. Kallel, M. Dammak, C. Mhiri, K. Ben Mahfoudh, and A. Ben Hamida, “A comparative study of MRI contrast enhancement techniques based on Traditional Gamma Correction and Adaptive Gamma Correction: Case of multiple sclerosis pathology,” in 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2018, pp. 1–7.
  • A. S. Abdullah, Y. E. Özok, and J. Rahebi, “A novel method for retinal optic disc detection using bat meta-heuristic algorithm,” Med. Biol. Eng. Comput., vol. 56, no. 11, pp. 2015–2024, 2018.
  • A. A. I. Mohamed, M. M. Ali, K. Nusrat, J. Rahebi, A. Sayiner, and F. Kandemirli, “Melanoma skin cancer segmentation with image region growing based on fuzzy clustering mean,” Int. J. Eng. Innov. Res., vol. 6, no. 2, p. 91C95, 2017.
  • I. A. Masoud Abdulhamid, A. Sahiner, and J. Rahebi, “New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation,” Biomed Res. Int., vol. 2020, 2020.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Amani Abraheem Salim Alshoul Bu kişi benim

Fatma Kandemirli 0000-0001-6097-2184

Javad Rahebi 0000-0001-9875-4860

Yayımlanma Tarihi 31 Ocak 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 22

Kaynak Göster

APA Alshoul, A. A. S., Kandemirli, F., & Rahebi, J. (2021). Bulanık Kümeleme Yöntemi ile Histolojik Görüntü Segmentasyonu. Avrupa Bilim Ve Teknoloji Dergisi(22), 393-399. https://doi.org/10.31590/ejosat.836329