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http://yadda.icm.edu.pl:443/baztech/element/bwmeta1.element.baztech-6f73f351-31ba-4c9c-9c02-10846ce0eef6

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Classification of abnormalities in mammograms by new asymmetric fractal features

Autorzy Beheshti, S. M. A.  Noubari, H. A.  Fatemizadeh, E.  Khalili, M. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN In this paper we use fractal method for detection and diagnosis of abnormalities in mammograms. We have used 168 images that were carefully selected by a radiologist and their abnormalities were also confirmed by biopsy. These images included asymmetric lesions, architectural distortion, normal tissue and mass lesion where in case of mass lesion they included circumscribed benign, ill-defined and spiculated malignant masses. At first, by using wavelet transform and piecewise linear coefficient mapping, image enhancement were done. Secondly detection of lesions was done by fractal method as a ROI. Since in investigation of breast cancer, it is important that fibroglandular tissues in both breasts be symmetric and for each asymmetric density, evaluation for malignancy is necessary, we define new fractal features based on extracting asymmetric information from lesions. The fractal features were evaluated on 5 data sets using SVM classifier which enabled to achieve high accuracy in classification of mammograms and diagnostic results. We have also investigated the performance of image enhancement in classification of each data set which shows different effects of enhancement on different lesion types.
Słowa kluczowe
PL wymiar fraktalny   cechy asymetrii   falka   poprawa jakości obrazu  
EN fractal feature   asymmetric feature   abnormal lesion   classification   wavelet   image enhancement  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 56--65
Opis fizyczny Bibliogr. 21 poz., rys., tab., wykr.
Twórcy
autor Beheshti, S. M. A.
autor Noubari, H. A.
  • Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
autor Fatemizadeh, E.
  • Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
autor Khalili, M.
  • Shahid Beheshti University of Medical Sciences, Tehran, Iran
Bibliografia
[1] Kopans DB. Breast imaging. 3rd ed. Lippincott Williams & Wilkins; 2007.
[2] American Cancer Society. Breast cancer: early detection. http://www.cancer.org/cancer/breastcancer/ moreinformation/breastcancerearlydetection/ breast-cancer-early-detection-toc [accessed August 2013].
[3] Harvey JA, Nicholson BT, Cohen MA. Finding early invasive breast cancers: a practical approach. Radiology 2008;248 (1):61–76.
[4] Dippel S, Stahl M, Wiemker R, Blaffert T. Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Trans Med Imaging 2002;21 (4):343–53.
[5] Salmeri M, Lojacono R, Frigerio M. Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans Instrum Meas 2008;57(7):1422–30.
[6] Lang M, Guo H, Odegard JE, Burrus CS. Noise reduction using an undecimated discrete wavelet transform. IEEE Signal Process Lett 1995;3(1):10–2.
[7] Emmanouil AI, Nikos PG, Konstantinos SP, Harris GV, Christos MS, Nikos D, et al. Mammographic image enhancement using wavelet-based processing and histogram equalization. 1st International Conference from Scientific Computing to Computational Engineering; 2004.
[8] Rangayyan RM, Oloumi F, Nguyen TM. Fractal analysis of contours of breast masses in mammograms via the power spectra of their signatures. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). IEEE; 2010. p. 6737–40.
[9] Li H, Giger ML, Olopade OI, Lan L. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol 2007;14(5): 513–21.
[10] Chena D, Chang R, Chen C, Hob M, Kuo S, Chen S, et al. Classification of breast ultrasound images using fractal feature. Clin Imaging 2005;29:235–45.
[11] Tourassi GD, Delong DM, Floyd Jr CE. A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol 2006;51(5): 1299–312.
[12] Guo Q, Shao J, Ruiz VF. Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms. Int J Comput Assist Radiol Surg 2009;4 (1):11–25.
[13] Rangayyan RM, Prajna S, Ayres FJ, Desautels JL. Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis. Int J Comput Assist Radiol Surg 2008;2 (6):347–61.
[14] Tao Y, Lam EC, Tang YY. A combination of fractal and wavelet for feature extraction. Int J Pattern Recogn Artif Intell 2001;15(08):1277–98.
[15] Li H, Liu KR, Lo S-C. Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans Med Imaging 1997;16(6): 785–98.
[16] Beheshti SMA, AhmadiNoubari H, Fatemizadeh E, Rezaee M, Khalili M. Mammograms enhancement using wavelet transform and piecewise linear and nonlinear coefficient mapping. 2014 IEEE Middle East Conference on Biomedical Engineering (MECBME). 2014. pp. 107–10.
[17] Nguyen TM, Rangayyan RM. Shape analysis of breast masses in mammograms via the fractal dimension. 27th Annual International Conference of the 2005 IEEE Engineering in Medicine and Biology. 2005. pp. 3210–3.
[18] Tang YY, Tao Y, Lam E. New method for feature extraction based on fractal behavior. Pattern Recogn 2002;35(5):1071–81.
[19] Beheshti SMA, AhmadiNoubari H, Fatemizadeh E, Khalili M. An efficient fractal method for detection and diagnosis of breast masses in mammograms. J Digit Imaging 2014;27 (5):661–9.
[20] Jasmine JL, Baskaran S, Govardhan A. An automated mass classification system in digital mammograms using contourlet transform and support vector machine. Int J Comput Appl 2011;31(9):54–61.
[21] Mammographic Image Analysis Society Databases. http://peipa.essex.ac.uk/ipa/pix/mias/ [accessed August 2011].
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-6f73f351-31ba-4c9c-9c02-10846ce0eef6
Identyfikatory
DOI 10.1016/j.bbe.2015.07.002