Detection of Regions of Interest in Mammograms by Using Local Binary Pattern and Dynamic K-Means Algorithm

Abdelali Elmoufidi; Khalid El Fahssi; Said Jai Andaloussi; Abderrahim Sekkaki

Detection of Regions of Interest in Mammograms by Using Local Binary Pattern and Dynamic K-Means Algorithm

Abdelali Elmoufidi, Khalid El Fahssi, Said Jai Andaloussi, Abderrahim Sekkaki

Abstract

This paper presents a method for the detection of regions of interest (ROI) in mammograms by using a dynamic K-means clustering algorithm. This method is used to partition automatically an image into a set of regions (clusters or classes). Our method consists
of three phases: firstly, preprocessing images by using thresholding and filtering methods; secondly, generating range of number of clusters by using Local Binary Pattern (LBP) and Applying k-means with its features to automatically generating the optimal number of clusters ( thereafter k is The number of clusters generating); thirdly, partition the mammograms images into k clusters by
applying the dynamic k-means clustering algorithm, we end by detecting the regions of interest (ROI) in mammograms images. To demonstrate the results of our proposed method we used the Mini-MIAS (Mammogram Image Analysis Society, UK) database,
consisting of 322 mammograms. Our method’s performance is evaluated using Free response ROC (FROC) curves. The archived results are 2.84 false positives per image (FPpI) and sensitivity of 85%.

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References

A. Gumaei, A. El-Zaart, M. Hussien, M., and M. Berbar, “Breast Segmentation using K-means Algorithm with A Mixture of Gamma Distributions”, IEEE 3rd SBNFI, Lebanon, 28-29 May, 2012, pp. 97-102.

J. Nagi, S. Abdul Kareem, F. Nagi, and S. K. Ahmed, “Automated Breast Profile Segmentation for ROI Detection Using Digital Mammograms”, IECBES 2010 , 30 Nov.- 2 Dec., 2010, Kuala Lumpur, Malaysia, pp. 87-92.

T. F. Chan, and L. A. Vese, “Active contours without edges”, IEEE Transactions on Image processing, vol. 10, no. 2, pp. 266-277, 2001.

A. K. Mohanty, S. Sahoo, A. Pradhan, and S. K. Lenka, “Detection of Masses from Mammograms Using Mass shape Pattern”, International Journal of Computer Technology and Applications, vol. 2, no. 4, pp. 1131-1139.

R. Jahanbin et al, “Automated Region of Interest Detection of Spiculated Masses on Digita Mammograms”, IEEE Southwest Symposium on Image Analysis and Interpretation, Santa

Fe, NM, USA, 24-26 Mar., 2008.

M. M. Abdelsamea, “An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation”, IPCBEE vol.21, Singapore, 2011, pp.1-5.

R. Siddheswar and R.H. Turi, “Determination of Number of Clusters in k-means Clustering and application in Color Image Segmentation”, ICAPRDT’99, Calcutta, India, 1999, pp. 137-143

A. T. Bon, “Developing K-Means Clustering on Beltline Moulding Contours”, Journal of Applied Sciences Research, vol. 5, no.5, pp. 2189-2193, 2009.

M. A. Roula, and A. Y. El-Zaar, “An Iterative Mammographic Image Thresholding Algorithm For Breast Cancer Detection”, ACIT’2013, Oman, 10-13 Dec., 2013.

N. Singh, A. G. Mohapatra, and G. Kanungo, “Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering”, International Journal of Computer

Applications, vol. 22, no.2, May 2011.

J. Suckling et al, “The Mammographic Image Analysis Society digital mammogram database”, Exerpta Medica., International Congress Series 1069, pp. 375-378, 1994.

S. D. Tzikopoulos, M. E. Mavroforakis, H. V. Georgiou, N. Dimitropoulos, and S. Theodoridis, “A fully automated scheme for mammographic segmentation and classification

based on breast density and asymmetry”, Computer Methods and Programs in Biomedicine, vol. 102, no. 1, pp. 47-63, 2011.

S. Jai andaloussi, A. Sekkaki, G. Quellec, M. Lamard, G. Cazuguel, and C. Roux, “Mass Segmentation in Mammograms by Using Bidimensional Emperical Mode Decomposition BEMD” , The 35th IEEE International Conference of

the Engineering in Medicine and Biology Society (EMBC13), Osaka, Japon, 3-7 July, 2013.

A. M. Sabu, N. Ponraj, “Poongodi. Textural Features Based Breast Cancer Detection : A Survey ”, Journal of Emerging Trends in Computing and Information Sciences, vol. 3, no. 9, pp. 1329-1334, Sep, 2012.

D. Huang, C. Shan, M. Ardabilian, Y. Wang, and L. Chen, “Local Binary Patterns and Its Application to Facial Image Analysis: A Survey” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews: Applications and Reviews, vol. 41, no. 6, pp. 765-781, Nov. 2011.

X. Llado, A. Oliver, J. Mart, and J. Freixenet. “Dealing with false positive reduction in mammographic mass detection”, In Medical Image Understanding and Analysis, pp. 81-85,

A. M. Khuzi, R. Besar, W. W. Zaki, and N. N. Ahmad, “Identification of masses in digital mammogram using gray level co-occurrence matrices” , Biomedical Imaging and Intervention Journal, vol. 5, no. 3, 2009.

T. Fawcett, ROC Graphs: Notes and Practical Considerations for Researchers. Palo Alto, USA: HP Laboratories, 2004.

X. Llado, A. Oliver, J. Mart, and J. Freixenet. “Dealing with false positive reduction in mammographic mass detection”.

In Medical Image Understanding and Analysis, pp. 81-85, 2007.

V. D. Nguyen, D. T. Nguyen, T. D. Nguyen, and V. T. Pham, “An Automated Method to Segment and Classify Masses in Mammograms”, World Academy of Science, Engineering and Technology vol. 3 no. 4, pp. 776-781, Apr. 2009.

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