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Breast Cancer Diagnosis using Geometrical Descriptors Obtained from Adaptive Convex Hulls of Suspicious Regions

Yıl 2021, Cilt: 4 Sayı: 3, 96 - 105, 30.12.2021

Öz

Breast cancer is the leading cause of cancer-related deaths among women worldwide as well as being the most frequently diagnosed cancer type. Advances in technology introduce Computer-Aided Diagnosis (CAD) systems for breast cancer which gain importance in reduced mortality rate by the increased sensitivity of early diagnosis. Although any imaging technique can be adapted, mammography, with known effectiveness in early diagnosis, are mostly used in CAD systems for breast cancer. Hence, this paper focuses on design of a CAD system analyzing mammography images for breast cancer diagnosis and proposes a new approach for geometrical feature extraction for this system. The proposed scheme is verified on a subset of the publicly available Mammographic Image Analysis Society digital mammogram database. In the detection phase of this system, initially, adaptive median filtering is applied for noise reduction; artifact suppression and background removal is realized via morphological operations, and pectoral muscle removal is executed using a region growing algorithm. Then, Chan-Vese active contour modeling is utilized for the ROI detection. Thereupon, the center of gravity (CoG) of each ROI is determined, and a convex image is created by specifying 92 points, called as edge points, on the boundary curves of the related ROI. In the feature extraction stage of the diagnosis phase, the angles between each pair of edge points and the CoG, the Euclidean distance between edge points and the CoG, and the Euclidean distance between each pair of edge points are computed. These geometrical descriptors are utilized in the classification stage via the Random Forest classifier using the five-fold cross-validation technique. As a result, breast cancer diagnosis is achieved by an accuracy of 70.13%. Analyzing the overall confusion matrix constructed in the classification stage, it is clearly seen that although healthy and benign diagnoses are mixed, malignancy is diagnosed well by the proposed geometrical descriptors.

Kaynakça

  • [1] International Agency for Research on Cancer. “Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018”. World Health Organizatio5n, Lyon, France, 263, 2018.
  • [2] Ergin S, Kılınç O. “A new feature extraction framework based on wavelets for breast cancer diagnosis”. Computers in Biology and Medicine, 51, 171-182, 2014.
  • [3] Işıklı Esener İ, Ergin S, Yüksel T. “A new feature ensemble with a multistage classification scheme for breast cancer diagnosis”. Journal of Healthcare Engineering, 2017, 1-15, 2017.
  • [4] D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA, et al. ACR BIRADS® Atlas, Breast Imaging Reporting and Data System. American College of Radiology [internet]. 2013 [cited 2018 Dec 06].
  • [5] Souza JC, Silva TF, Rocha SV, Paiva AC, Braz G, Almeida JD, Silva AC. “Classification of malignant and benign tissues in mammography using dental shape descriptors and shape distribution”. 2017 7th Latin American Conference on Networked and Electronic Media (LACNEM 2017), Valparaiso, Chile, 6-7 November 2017.
  • [6] Osada R, Funkhouser T, Chazelle B, Dobkin D. “Shape distributions”. ACM Trans Graph, 21(4), 807-832, 2002.
  • [7] Yu M, Atmosukarto I, Leow WK, Huang Z, Xu R. “3D model retrieval with morphing based geometric and topologic topological feature maps”. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, 18-20 June 2003.
  • [8] Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL. “A leaf recognition algorithm for plant classification using probabilistic neural network”. 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza, Egypt, 15-18 December 2007.
  • [9] Mahdikhanlou K, Ebrahimnezhad H. “Plant leaf classification using centroid distance and axis of least inertia method”. 2014 22nd Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, 20-22 May 2014.
  • [10] Türkoğlu M, Hanbay D. “Plant recognition system based on extreme learning machine by using shearlet transform and new geometric features”. Journal of The Faculty of Engineering And Architecture of Gazi University, 34(4), 2097-2112, 2019.
  • [11] Suckling J et al. “The Mammographic Image Analysis Society Digital Mammogram Database”. Exerpta Medica Int Congr Ser, 1069, 375-378, 1994.
  • [12] Işıklı Esener İ, Ergin S, Yüksel T. “A novel multistage system for the detection and removal of pectoral muscles in mammograms”. Turkish Journal of Electrical Engineering & Computer Sciences, 26(1), 35-49, 2018.
  • [13] Işıklı Esener İ, Ergin S, Yüksel T. “A practical Region-of-Interest (ROI) detection approach for suspicious region identification in breast cancer diagnosis”. 2017 International Conference on Engineering Technologies (ICENTE17), Konya, Turkey, 7-9 December 2017.
  • [14] Chan TF, Vese LA. “Active contours without edges”. IEEE Transactions on Image Processing, 10, 266-277, 2001.
Yıl 2021, Cilt: 4 Sayı: 3, 96 - 105, 30.12.2021

Öz

Kaynakça

  • [1] International Agency for Research on Cancer. “Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018”. World Health Organizatio5n, Lyon, France, 263, 2018.
  • [2] Ergin S, Kılınç O. “A new feature extraction framework based on wavelets for breast cancer diagnosis”. Computers in Biology and Medicine, 51, 171-182, 2014.
  • [3] Işıklı Esener İ, Ergin S, Yüksel T. “A new feature ensemble with a multistage classification scheme for breast cancer diagnosis”. Journal of Healthcare Engineering, 2017, 1-15, 2017.
  • [4] D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA, et al. ACR BIRADS® Atlas, Breast Imaging Reporting and Data System. American College of Radiology [internet]. 2013 [cited 2018 Dec 06].
  • [5] Souza JC, Silva TF, Rocha SV, Paiva AC, Braz G, Almeida JD, Silva AC. “Classification of malignant and benign tissues in mammography using dental shape descriptors and shape distribution”. 2017 7th Latin American Conference on Networked and Electronic Media (LACNEM 2017), Valparaiso, Chile, 6-7 November 2017.
  • [6] Osada R, Funkhouser T, Chazelle B, Dobkin D. “Shape distributions”. ACM Trans Graph, 21(4), 807-832, 2002.
  • [7] Yu M, Atmosukarto I, Leow WK, Huang Z, Xu R. “3D model retrieval with morphing based geometric and topologic topological feature maps”. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, 18-20 June 2003.
  • [8] Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL. “A leaf recognition algorithm for plant classification using probabilistic neural network”. 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza, Egypt, 15-18 December 2007.
  • [9] Mahdikhanlou K, Ebrahimnezhad H. “Plant leaf classification using centroid distance and axis of least inertia method”. 2014 22nd Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, 20-22 May 2014.
  • [10] Türkoğlu M, Hanbay D. “Plant recognition system based on extreme learning machine by using shearlet transform and new geometric features”. Journal of The Faculty of Engineering And Architecture of Gazi University, 34(4), 2097-2112, 2019.
  • [11] Suckling J et al. “The Mammographic Image Analysis Society Digital Mammogram Database”. Exerpta Medica Int Congr Ser, 1069, 375-378, 1994.
  • [12] Işıklı Esener İ, Ergin S, Yüksel T. “A novel multistage system for the detection and removal of pectoral muscles in mammograms”. Turkish Journal of Electrical Engineering & Computer Sciences, 26(1), 35-49, 2018.
  • [13] Işıklı Esener İ, Ergin S, Yüksel T. “A practical Region-of-Interest (ROI) detection approach for suspicious region identification in breast cancer diagnosis”. 2017 International Conference on Engineering Technologies (ICENTE17), Konya, Turkey, 7-9 December 2017.
  • [14] Chan TF, Vese LA. “Active contours without edges”. IEEE Transactions on Image Processing, 10, 266-277, 2001.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İdil Isıklı Esener

Şükriye Kara 0000-0002-5874-8586

Semih Ergin

Yayımlanma Tarihi 30 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 4 Sayı: 3

Kaynak Göster

APA Isıklı Esener, İ., Kara, Ş., & Ergin, S. (2021). Breast Cancer Diagnosis using Geometrical Descriptors Obtained from Adaptive Convex Hulls of Suspicious Regions. Veri Bilimi, 4(3), 96-105.



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