Mammographic images segmentation based on chaotic map clustering algorithm

BMC Medical Imaging

Giuseppe Raso²  Francesco Fauci²  Donato Cascio²  Marius Iacomi¹ 

[1] Institutul de Ştiinţe Spaţiale, Bucharest, Măgurele, Romania;Dipartimento di Fisica e Chimica, Università Degli Studi di Palermo, Palermo, Italy

关键词: Breast cancer;    Microcalcifications;    Mass lesions;    Features;    Mammography;    Segmentation;    Cooperative behavior;    Clustering algorithms;    Chaotic maps;   

Others  :  797805
DOI  :  10.1186/1471-2342-14-12

 received in 2013-11-08, accepted in 2014-03-14,  发布年份 2014

【 摘 要 】

Background

This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography.

Methods

The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image.

Results

The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions.

Conclusions

We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI.

【 授权许可】

   
2014 Iacomi et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Meyer-Bäse A: Pattern Recognition for Medical Imaging. San Diego, CA, USA: Elsevier Academic Press; 2003:346-359.
• [2]American Cancer Society: Cancer Facts & Figures 2012. 2012. http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-031941.pdf webcite
• [3]Shi J, Sahiner B, Chan HP, Ge J, Hadjiiski L, Helvie MA, Nees A, Wu YT, Wei J, Zhou C, Zhang Y, Cui J: Characterization of mammographic masses based on level set segmentation with New image features and patient information. Med Phys 2008, 35:280-290.
• [4]Azavedo E, Zackrisson S, Mejàre I, Heibert Arnlind M: Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review. BMC Med Imaging 2012., 12art. no. 22
• [5]Ciatto S, Cascio D, Fauci F, Magro R, Raso G, Ienzi R, Martinelli F, Simone MV: Computer assisted diagnosis (CAD) in mammography. Comparison of diagnostic accuracy of a new algorithm (Cyclopus®, Medicad) with two commercial systems. Radiol Med 2009, 114:626-635.
• [6]Cascio D, Fauci F, Iacomi M, Raso G, Magro R, Castrogiovanni D, Filosto G, Ienzi R, Vasile MS: Computer-aided diagnosis in digital mammography: comparison of two commercial systems. Imaging in Medicine 2014, 6(1):13-30.
• [7]Nazem-Zadeh M-R, Saksena S, Babajani-Fermi A, Jiang Q, Soltanian-Zadeh H, Rosenblum M, Mikkelsen T, Jain R: Segmentation of corpus callosum using diffusion tensor imaging: validation in patients with glioblastoma. BMC Med Imaging 2012., 12art. no. 10
• [8]Montelius M, Ljungberg M, Horn M, Forssell-Aronsson E: Tumour size measurement in a mouse model using high resolution MRI. BMC Med Imaging 2012., 12art. no. 12
• [9]Wong KP: Medical Image Segmentation: Methods and Applications in Functional Imaging. In Handbook of Biomedical Image Analysis: Segmentation models Part B. Edited by Suri JS, Wilson DL, Laxminarayan S. New York, NY, USA: Kluwer Academic/Planum Publishers; 2005:111-115.
• [10]Cascio D, Fauci F, Magro R, Raso G, Bellotti R, De Carlo F, Tangaro S, De Nunzio G, Quarta M, Forni G, Lauria A, Fantacci ME, Retico A, Masala GL, Oliva P, Bagnasco S, Cheran SC, Torres EL: Mammogram segmentation by contour searching and massive lesions classification with Neural Network. IEEE Trans Nucl Sci 2006, 53:2827-2833.
• [11]Bellotti R, De Carlo F, Tangaro S, Gargano G, Maggipinto G, Castellano M, Massafra R, Cascio D, Fauci F, Magro R, Raso G, Lauria A, Forni G, Bagnasco S, Cerello P, Zanon E, Cheran SC, Lopez Torres E, Bottigli U, Masala GL, Oliva P, Retico A, Fantacci ME, Cataldo R, De Mitri I, De Nunzio G: A completely automated CAD system for mass detection in a large mammographic database. Med Phys 2006, 33:3066-3075.
• [12]Claudia C, Farida C, Guy G, Marie-Claude M, Carl-Eric A: Quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images. BMC Med Imaging 2012., 12art. no. 26
• [13]Farag AA, Ahmed MN, El-Baz A, Hassan H: Advanced segmentation techniques. In Handbook of Biomedical Image Analysis: Segmentation models Part A. Edited by Suri JS, Wilson DL, Laxminarayan S. New York, NY, USA: Kluwer Academic/Planum Publishers; 2005:479.
• [14]Pratt WK: Digital Image Processing: PIKS Scientific Inside. 4th edition. Hoboken, NJ, USA: John Wiley & Sons, Inc; 2007:579.
• [15]Yang S, Sunanda M: Statistical and adaptive approaches for optimal segmentation in medical images. In Handbook of Biomedical Image Analysis: Segmentation models Part B. Edited by Suri JS, Wilson DL, Laxminarayan S. New York, NY, USA: Kluwer Academic/Planum Publishers; 2005:267-271.
• [16]Tangaro S, Bellotti R, De Carlo F, Gargano G, Lattanzio E, Monno P, Massacra R, Delogu P, Fantacci ME, Retico A, Mazzocchi M, Bagnasco S, Cerello P, Cheran SC, Lopez Torres E, Zanon E, Lauria A, Sodano A, Cascio D, Fauci F, Magro R, Raso G, Ienzi R, Bottigli U, Masala GL, Oliva P, Meloni G, Caricato AP, Cataldo R: MAGIC-5: an Italian mammographic database of digitized images for research. Radiol Med 2008, 113:477-485.
• [17]Liu J, Chen J, Liu X, Chun L, Tang J, Deng Y: Mass segmentation using a combined method for cancer detection. BMC Syst Biol 2011., 5(SUPPL. 3) art. no. S6
• [18]Biltawi M, Al-Najdawi N, Tedmori S: Mammogram enhancement and segmentation methods: classification, analysis, and evaluation. The 13th international Arab conference on information technology. 2012.
• [19]Fauci F, Cascio D, La Manna A, Magro R, Raso G, Vasile M, Iacomi M: A Fourier Based Algorithm for Microcalcifications Enhancement in Mammographic Images. 2012, 4388-4391. [IEEE Nuclear Science Symposium and Medical Imaging Conference] art. No. 4774254
• [20]Vivona L, Cascio D, Magro R, Fauci F, Raso G: A Fuzzy Logic Cmeans Clustering Algorithm to Enhance Microcalcifications Clusters in Digital Mammograms. 2012, 3048-3050. [IEEE Nuclear Science Symposium and Medical Imaging Conference] art. No. 6152551
• [21]Coleman GB, Andrews HC: Image segmentation by clustering. Proc IEEE Inst Electr Electron Eng 1979, 67:773-785.
• [22]Blatt M, Wiseman S, Domany E: Super-paramagnetic clustering of data. Phys Rev Lett 1996, 76:3251-3254.
• [23]Manrubia SC, Mikhalkov AS: Mutual synchronization and clustering in randomly coupled chaotic dynamical networks. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 1999, 60:1579-1589.
• [24]Angelini L, De Carlo F, Marangi C, Pellicoro M, Stramaglia S: Clustering data by inhomogeneous chaotic map lattices. Phys Rev Lett 2000, 85:554-557.
• [25]Marangi C, Angelini L, De Carlo F, Nardulli G, Pellicoro M, Stramaglia S: Clustering by inhomogeneous chaotic maps in landmine detection. Proc SPIE 2001, 4170:122-132.
• [26]Bellotti R, De Carlo F, Stramaglia S: Chaotic map clustering algorithm for EEG analysis. Physica A 2004, 334:222-232.
• [27]Basalto N, Bellotti R, De Carlo F, Facchi P, Pascazio S: Clustering stock market companies via chaotic map synchronization. Physica A 2005, 345:196-206.
• [28]Basalto N, De Carlo F: Clustering financial time series. In Practical Fruits of Econophysics: Proceedings of The Third Nikkei Econophysics Symposium. Edited by Takayasu H. Tokio, Japan: Springer Verlag; 2006:252-256.
• [29]Giangreco I, Nicolotti O, Carotti A, De Carlo F, Gargano G, Bellotti R: Analysis of x-ray structures of matrix metalloproteinases via chaotic map clustering. BMC Bioinforma 2010., 11art. no. 500
• [30]Fu C, Meng WH, Zhan YF, Zhu ZL, Lau FCM, Tse CK, Ma HF: An efficient and secure medical image protection scheme based on chaotic maps. Comput Biol Med 2013, 43(8):1000-1010.
• [31]Solé RV, Manrubia SC, Bascompte J, Delgado J, Luque B: Phase transitions and complex systems. Complexity 1996, 4:13-26.
• [32]Haralick R, Shanmungam K, Dinstein I: Textural features for image classification. IEEE Trans Syst Man Cybern C Appl Rev 1973, 6:610-621.
• [33]Haralick R: Statistical and structural approaches to texture. Proc IEEE Inst Electr Electron Eng 1979, 67:786-803.
• [34]Cascio D, Magro R, Fauci F, Iacomi M, Raso G: Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput Biol Med 2012, 42(11):1098-1109.
• [35]Masala GL, Golosio B, Oliva P, Cascio D, Fauci F, Tangaro S, Quarta M, Cheran SC, Lopez Torres E: Classifiers trained on dissimilarity representation of medical pattern: a comparative study. Nuovo Cimento C 2005, 28:905-912.
• [36]Masala GL, Tangaro S, Golosio B, Oliva P, Stumbo S, Bellotti R, De Carlo F, Gargano G, Cascio D, Fauci F, Magro R, Raso G, Bottigli U, Cgincarini A, De Mitri I, De Nunzio G, Gori I, Retico A, Cerello P, Cheran SC, Fulcheri C, Lopez Torres E: Comparative study of feature classification methods for mass lesion recognition in digitized mammograms. Nuovo Cimento C 2007, 30:305-316.
• [37]Fauci F, Raso G, Magro R, Forni G, Lauria A, Bagnasco S, Cerello P, Cheran SC, Lopez Torres E, Bellotti R, De Carlo F, Gargano G, Tangaro S, De Mitri I, De Nunzio G, Cataldo R: A massive lesion detection algorithm in mammography. Phys Med 2005, 21(1):23-30.
• [38]Cascio D, Cheran SC, Chincarini A, De Nunzio G, Delogu P, Fantacci ME, Gargano G, Gori I, Masala GL, Preite Martinez A, Retico A, Santoro M, Spinelli C, Tarantino T: Automated detection of lung nodules in low-dose computed tomography. Int J Comput Assist Radiol Surg 2007, 2:357-359.
• [39]Angelini L, De Carlo F, Mannarelli M, Marangi C, Nardulli G, Pellicoro M, Satalino G, Stramaglia S: Chaotic neural networks clustering: an application to landmine detection by dynamic infrared imaging. Opt Eng 2001, 40:2878-2884. http://www.ba.infn.it/~angelini/minefields.pdf webcite
• [40]Melloul M, Joskowicz L: Segmentation of microcalcification in X-ray mammograms using entropy thresholding. CARS 2002 Computer Assisted Radiology and Surgery 2002, 671-676.