Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Trends in Online Biomonitoring Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis

Authors : Liliana Losurdo, Annarita Fanizzi, Teresa M. A. Basile, Roberto Bellotti, Ubaldo Bottigli, Rosalba Dentamaro, Vittorio Didonna, Alfonso Fausto, Raffaella Massafra, Alfonso Monaco, Marco Moschetta, Ondina Popescu, Pasquale Tamborra, Sabina Tangaro, Daniele La Forgia

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic model for characterizing and discriminating tissue in normal/abnormal and benign/malign in digital mammograms, as support tool for the radiologists. We trained a Random Forest classifier on some textural features extracted on a multiscale image decomposition based on the Haar wavelet transform combined with the interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg), respectively. We tested the proposed model on 192 ROIs extracted from 176 digital mammograms of a public database. The model proposed was high performing in the prediction of the normal/abnormal and benign/malignant ROIs, with a median AUC value of \(98.46\%\) and \(94.19\%\), respectively. The experimental result was comparable with related work performance.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses
next chapter An Empirical Study of Word Sense Disambiguation for Biomedical Information Retrieval System
Literature
1.
Elter, M., Horsch, A.: CADx of mammographic masses and clustered microcalcifications: a review. Med. Phys. 36(6), 2052–2068 (2009)CrossRef
2.
Howell, A.: The emerging breast cancer epidemic: early diagnosis and treatment. Breast Cancer Res. 12(4), S10 (2010)CrossRef
3.
4.
Fletcher, S.W., Elmore, J.G.: Mammographic screening for breast cancer. N. Engl. J. Med. 348(17), 1672–1680 (2003)CrossRef
5.
Elmore, J.G., Armstrong, K., Lehman, C.D., Fletcher, S.W.: Screening for breast cancer. JAMA 293(10), 1245–1256 (2006)CrossRef
6.
Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn. 36(12), 2967–2991 (2003)CrossRefMATH
7.
Brown, J., Bryan, S., Warren, R.: Mammography screening: an incremental cost effectiveness analysis of double versus single reading of mammograms. BMJ 312(7034), 809–812 (1996)CrossRef
8.
McCormack, V.A., dos Santos Silva, I.: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Prev. Biomark. 15(6), 1159–1169 (2006)CrossRef
9.
Wolfe, J.N.: Breast patterns as an index of risk for developing breast cancer. Am. J. Roentgenol. 126(6), 1130–1137 (1976)CrossRef
10.
Jiang, Y., Nishikawa, R.M., Wolverton, D.E., Metz, C.E., Giger, M.L., Schmidt, R.A., Vyborny, C.J., Doi, K.: Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology 198(3), 671–678 (1996)CrossRef
11.
Chan, H.P., Sahiner, B., Lam, K.L., Petrick, N., Helvie, M.A., Goodsitt, M.M., Adler, D.D.: Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Med. Phys. 25(10), 2007–2019 (1998)CrossRef
12.
Nakayama, R., Uchiyama, Y., Watanabe, R., Katsuragawa, S., Namba, K.: Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Med. Phys. 31(4), 789–799 (2004)CrossRef
13.
Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. Handb. Image Video Process. 2(1), 1195–1217 (2005)CrossRef
14.
Zhang, X., Homma, N., Goto, S., Kawasumi, Y., Ishibashi, T., Abe, M., Sugita, N., Yoshizawa, M.: A hybrid image filtering method for computer-aided detection of microcalcification clusters in mammograms. J. Med. Eng. 2013, 8 p. (2013). Article no. 615254
15.
Vivona, L., Cascio, D., Fauci, F., Raso, G.: Fuzzy technique for microcalcifications clustering in digital mammograms. BMC Med. Imaging 14(1), 23 (2014)CrossRef
16.
Wang, J., Nishikawa, R.M., Yang, Y.: Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model. Med. Phys. 43(1), 159–170 (2016)CrossRef
17.
Oliver, A., Torrent, A., Lladó, X., Tortajada, M., Tortajada, L., Sentís, M., Freixenet, J., Zwiggelaar, R.: Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowl.-Based Syst. 28, 68–75 (2012)CrossRef
18.
Gallardo-Caballero, R., García-Orellana, C.J., García-Manso, A., González-Velasco, H.M., Macías-Macías, M.: Independent component analysis to detect clustered microcalcification breast cancers. Sci. World J. 2012, 6 p. (2012). Article no. 540457
19.
Jian, W., Sun, X., Luo, S.: Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform. Biomed. Eng. Online 11(1), 96 (2012)CrossRef
20.
Phadke, A.C., Rege, P.P.: Detection and classification of microcalcifications using discrete wavelet transform. Int. J. Emerg. Trends Technol. Comput. Sci. 2(4), 130–134 (2013)
21.
Khehra, B.S., Pharwaha, A.P.S.: Classification of clustered microcalcifications using MLFFBP-ANN and SVM. Egypt. Inform. J. 17(1), 11–20 (2016)CrossRef
22.
Boulehmi, H., Mahersia, H., Hamrouni, K.: A new CAD system for breast microcalcifications diagnosis. Int. J. Adv. Comput. Sci. Appl. 7(4), 133–143 (2016)
23.
Chen, Z., Strange, H., Oliver, A., Denton, E.R., Boggis, C., Zwiggelaar, R.: Topological modeling and classification of mammographic microcalcification clusters. IEEE Trans. Biomed. Eng. 62(4), 1203–1214 (2015)CrossRef
24.
Fanizzi, A., Basile, T.M.A., Losurdo, L., Amoroso, N., Bellotti, R., Bottigli, U., Dentamaro, R., Didonna, V., Fausto, A., Massafra, R., Moschetta, M., Tamborra, P., Tangaro, S., La Forgia, D.: Hough transform for clustered microcalcifications detection in full-field digital mammograms. In: Applications of Digital Image Processing XL, vol. 10396, p. 1039616. International Society for Optics and Photonics, San Diego (2017)
25.
Sklansky, J.: On the Hough technique for curve detection. IEEE Trans. Comput. C–27(10), 923–926 (1978)CrossRefMATH
26.
Pedersen, S.J.K.: Circular hough transform. Aalborg Univ. Vis. Graph. Interact. Syst. 123, 123 (2007)
27.
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (2006)
28.
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefMATH
29.
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef
30.
Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1994, pp. 593–600. IEEE (1994)
31.
32.
Ramos-Pollán, R., Guevara-López, M.A., Suárez-Ortega, C., Díaz-Herrero, G., Franco-Valiente, J.M., Rubio-del-Solar, M., González-de-Posada, N., Pires Vaz, M.A., Loureiro, J., Ramos, I.: Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4), 2259–2269 (2012)CrossRef
Metadata
Title
A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis
Authors
Liliana Losurdo
Annarita Fanizzi
Teresa M. A. Basile
Roberto Bellotti
Ubaldo Bottigli
Rosalba Dentamaro
Vittorio Didonna
Alfonso Fausto
Raffaella Massafra
Alfonso Monaco
Marco Moschetta
Ondina Popescu
Pasquale Tamborra
Sabina Tangaro
Daniele La Forgia
Copyright Year
2018
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-319-78722-0

Electronic ISBN: 978-3-319-78723-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-78723-7_26

Premium Partner

    Image Credits