Sage Journals: Discover world-class research

Abstract

Early diagnosis of breast cancer can improve the survival rate by detecting cancer at initial stage. In this paper, an efficient content-based mammogram retrieval system is proposed, which helps in early diagnosis of breast cancer by classifying the current case mammogram and retrieving similar past cases mammograms already annotated by diagnostic descriptions and treatment results. The proposed steps include cropping of mammograms for finding the region of interest (ROI), feature extraction using wavelet based-complete local binary pattern (W-CLBP) and K-means clustering. Strong texture characteristics of the mammogram are captured using CLBP to all detailed coefficients (LH, HL, and HH) from two level decomposed 2 Dimensional-discrete wavelet transform (2D-DWT) of ROI. Further, K-means generates the clusters based on this texture similarity of mammograms, and query mammogram features are matched with all cluster representatives to find the closest cluster. Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure. So, at the searching time the query mammogram is searched only in small sub-set depending upon the cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Experiments on benchmark mammography image analysis Society (MIAS) database confirm that the proposed method has better say with respect to other four variants of texture features.

Keywords

Computer-aided diagnosis mammography content-based image retrieval 2D-DWT local binary pattern K-means clustering

Get full access to this article

View all access options for this article.

References

Youlden

D.R.

et al., The descriptive epidemiology of female breast cancer: an international comparison of screening, incidence, survival and mortality, Cancer Epidemiology 36(3) (2012), 237–248.

Jen

C.C.

and Yu

S.S.

, Automatic detection of abnormal mammograms in mammographic images, Expert Systems with Applications 42(6) (2015), 3048–3055.

Singh

V.P.

Srivastava

and Srivastava

, Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests, Technology and Health Care 25(4) (2017), 709–727.

Singh

V.P.

et al., Mammogram Classification Using Selected GLCM Features and Random Forest Classifier, International Journal of Computer Science and Information Security 14(6) (2016), 82.

Ergen

and Baykara

, Texture based feature extraction methods for content based medical image retrieval systems, Bio-medical Materials and Engineering 24(6) (2014), 3055–3062.

Wei

C.H.

C.T.

and Wilson

, A general framework for content-based medical image retrieval with its application to mammograms, In Proc. SPIE 5748 (2005), 134–143.

Wei

C.H.

and Li

C.T

, Effective extraction of Gabor features for adaptive mammogram retrieval, In Multimedia and Expo, 2007 IEEE International Conference, 2007, pp. 1503–1506.

Chandy

D.A.

Johnson

J.S.

and Selvan

S.E.

, Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval, Multimedia Tools and Applications 72(2) (2014), 2011–2024.

Chandy

D.A.

et al., Neighbourhood search feature selection method for content-based mammogram retrieval, Medical & Biological Engineering & Computing 55(3) (2017), 493–505.

10.

M.N.

and Vetterli

, The contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on Image Processing 14(12) (2005), 2091–2106.

11.

Wiesmuller

and Chandy

D.A.

, Content based mammogram retrieval using gray level aura matrix, Int J Comput Commun Inf Syst (IJCCIS) 2(1) (2010), 217–222.

12.

Singh

V.P.

et al., An efficient content based image retrieval for normal and abnormal mammograms, In Electrical Computer and Electronics (UPCON) 2015, IEEE, 2015, 1–6.

13.

Liu

et al., Median robust extended local binary pattern for texture classification, IEEE Transactions on Image Processing 25(3) (2016), 1368–1381.

14.

Liao

Law

M.W.

and Chung

A.C.

, Dominant local binary patterns for texture classification, IEEE Transactions on Image Processing 18(5) (2009), 1107–1118.

15.

Guo

Zhang

and Zhang

, A completed modeling of local binary pattern operator for texture classification, IEEE Transactions on Image Processing 19(6) (2010), 1657–1663.

16.

Yildizer

et al., Efficient content-based image retrieval using multiple support vector machines ensemble, Expert Systems with Applications 39(3) (2012), 2385–2396.

17.

Ojala

Pietikäinen

and Harwood

, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition 29(1) (1996), 51–59.

18.

Daubechies, Ten Lectures on Wavelets, Philadelphia: SIAM, 1992.

19.

Haralick

R.M.

and Shapiro

L.G.

, Image segmentation techniques, Computer Vision, Graphics, and Image Processing 29(1) (1985), 100–132.

20.

Jain

A.K.

Duin

R.P.

and Mao

, Statistical pattern recognition: A review, IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1) (2000), 4–37.

21.

Mahajan

Nimbhorkar

and Varadarajan

, The planar k-means problem is NP-hard, In International Workshop on Algorithms and Computation, Springer, 2009, pp. 274–285.

22.

Singh

V.P.

Srivastava

and Srivastava

, Automated and effective content-based image retrieval for digital mammography, Journal of X-Ray Science and Technology, vol. Preprint, no. Preprint (2017), 1–20.

23.

Suckling

et al., The mammographic image analysis society digital mammogram database, In Exerpta Medica. International Congress Series 1069 (1994), 375–378.

24.

Juan

and Gwun

, A comparison of sift, pca-sift and surf, International Journal of Image Processing (IJIP) 3(4) (2009), 143–152.

25.

Kulshreshtha

et al., Content-based mammogram retrieval using k-means clustering and local binary pattern, In Image, Vision and Computing (ICIVC), 2017 2nd International Conference on IEEE, 2017, pp. 634–638.

26.

Wang

et al., Computer-aided classification of mammographic masses using visually sensitive image features, Journal of X-ray Science and Technology 25(1) (2017), 171–186.

Content-based mammogram retrieval using wavelet based complete-LBP and K-means clustering for the diagnosis of breast cancer

Abstract

Keywords

Get full access to this article

References