Automated and effective content-based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map

• Autorzy: Singh V. P. , Srivastava R.

Identyfikatory

DOI

10.1016/j.bbe.2017.09.003

• Języki publikacji: EN

Abstrakty

EN

In this paper, automated, fast and effective content based-mammogram image retrieval system is proposed. The proposed pre-processing steps include automatic labelling-scratches suppression, automatic pectoral muscle removal and image enhancement. Further, for segmentation selective thresholds based seeded region growing algorithm is introduced. Furthermore, we apply 2-level discrete wavelet transform (DWT) on the segmented region and wavelet based centre symmetric-local binary pattern (WCS-LBP) features are extracted. Then, extracted features are fed to self-organizing map (SOM) which generates clusters of images, having similar visual content. SOM produces different clusters with their centres and query image features are matched with all cluster representatives to find closest cluster. Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure. So, at the searching time the query image is searched only in small subset depending upon cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Descriptive experimental and empirical discussions confirm the effectiveness of this paper.

Słowa kluczowe

EN

self-organizing map; mammography; image segmentation; discrete wavelet transform; content-based image retrieval (CBIR); CS-LBP

PL

mapa samoorganizująca; mammografia; segmentacja obrazu; dyskretna transformata falkowa; CBIR; CS-LBP

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 1
• Strony: 90--105
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Singh V. P.

• Department of Computer Science & Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India

autor

Srivastava R.

• Department of Computer Science & Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India

Bibliografia

• [1] Suckling J. The mammographic image analysis society digital mammogram database exerpta medica. Int Congr Ser 1994;1069:375–8.
• [2] Donepudi MS, Kondapalli K, Amos SJ, Venkanteshan P. Breast cancer statistics and markers. J Cancer Res Ther 2014;10(3):506–11.
• [3] Pisano ED. Image processing algorithms for digital mammography: a pictorial essay. Radio Graph 2000;20 (5):1479–91.
• [4] Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology 2001;220 (3):781–6.
• [5] Srivastava S, Sharma N, Singh SK, Srivastava R. A combined approach for the enhancement and segmentation of mammograms using modified fuzzy C-means method in wavelet domain. J Med Phys 2014;39(3):169.
• [6] Srivastava S, Kumar N, Singh SK, Srivastava R. Quantitative analysis of a general framework of a CAD tool for breast cancer detection from mammograms. J Med Imaging Health Inf 2014;4:1–21.
• [7] Nagi J, Kareem S, Nagi F, Ahmed SK. Automated breast profile segmentation for ROI detection using digital mammograms. IEEE EMBS Conference on Biomedical Engineering & Sciences; 2010. pp. 87–92.
• [8] Wei CH, Li CT, Wilson R. Effective extraction of gabor features for adaptive mammogram retrieval. ICME; 2007. p. 1503–6.
• [9] Wei CH, Li CT, Wilson R. A general framework for content- based medical image retrieval with its application to mammogram retrieval. Proc SPIE Int Symp Med Imaging 2005;5748:134–43.
• [10] Sun J, Zhang Z. An effective method for mammograph image retrieval. Proceedings of International Conference on Computational Intelligence and Security; 2008. p. 190–3.
• [11] Wiesmuller S, Chandy DA. Content-based mammogram retrieval using gray level aura matrix. Int J Comput Commun Inf Syst (IJCCIS) 2010;2(1):217–22.
• [12] Chandy DA, Christinal AH, Theodore AJ, Selvan SE. Neighbourhood search feature selection method for content-based mammogram retrieval. Med Biol Eng Comput 2016;1–13.
• [13] Chandy DA, Johnson JS, Selvan SE. Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval. Multimed Tools Appl 2014;72 (2):2011–24.
• [14] Sterns EE. Relation between clinical and mammographic diagnosis of breast problems and the cancer/biopsy rate. Can J Surg 1996;39:128–32.
• [15] Highnam R, Brady M. Mammographic image analysis. Kluwer Academic Publishers; 1999. p. 191–222, ISBN: 0-7923- 5620-9 [chapter 11].
• [16] Wei CH, Chen SY, Liu X. Mammogram retrieval on similar mass lesions. Comput Methods Prog Biomed 2012;106 (3):234–48.
• [17] Rouhi R, Jafari M, Kasaei S. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 2015;(42):990–1002.
• [18] Cao Y, Hao X, Zhu X, Xia S. An adaptive region growing algorithm for breast masses in mammograms. Front Electr Electron Eng China 2010;5(2):128–36.
• [19] Singh VP, Gupta A, Singh S, Srivastava R. An efficient content based image retrieval for normal and abnormal mammograms. 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON). 2015. pp. 1–6.
• [20] Heikkilä M, Pietikäinen M, Schmid C. Description of interest regions with local binary patterns. Pattern Recogn 2009;42 (3):425–36.
• [21] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002;24 (7):971–87.
• [22] Yildizer E, Metin A, Balci, Hassan M, Alhajj R. Efficient content-based image retrieval using multiple support vector machines ensemble. Expert Syst Appl 2012;39(3):2385–96.
• [23] Kohonen T. Self-organizing map. Berlin: Springer; 1997.
• [24] Eklund GW, Cardenosa G, Parsons W. Assessing adequacy of mammographic image quality. Radiology 1994;190(2):297–307.
• [25] Zhou C, Hadjiiski LM, Paramagul C, Sahiner B, Chan HP, Wei J. Computerized pectoral muscle identification on MLO-view mammograms for CAD applications. Proc SPIE 2005;5747:852–7.
• [26] Gupta R, Udrill PE. The use of texture analysis to delineate suspicious masses in mammography. Phys Med Biol 1995;40:835–55.
• [27] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9(1):62–6.
• [28] Kwok S, Chandrasekhar R, Attikiouzel Y. Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection. IIS Conference. 2001. pp. 67–72.
• [29] Ferrari RJ, Rangayyan RM. Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 2004;23:232–45.
• [30] Chen CW, Luo C, Parker KJ. Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications. IEEE Trans Image Process 1998;7(12):1673–83.
• [31] Juneja M, Mohana R. An improved adaptive median filtering method for impulse noise detection. Int J Recent Trends Eng 2009;1(1):274–8.
• [32] Singh VP, Srivastava S, Srivastava R. Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests. Technol Health Care 2017;25(4):709–27.
• [33] Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973;SMC- 3:610–21.
• [34] Daubechies I. Ten lectures on wavelets, vol. 61. Philadelphia: Society for Industrial and Applied Mathematics; 1992. p. 198–202.
• [35] Mallat S. A wavelet tour of signal processing. Academic Press; 1999.
• [36] Beura S, Majhi B, Dash R. Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 2015;154:1–14.
• [37] Du L, You X, Xu H, Gao Z, Tang Y. Wavelet domain local binary pattern features for writer identification. 2010 20th International Conference on Pattern Recognition (ICPR); 2010. pp. 3691–4.
• [38] Kumar I, Bhadauria HS, Virmani J, Thakur S. A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern Biomed Eng 2017.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).