segmentation and characterization of masses in the digital mammogram… · performance in the...

Article ID: WMC00726 2046-1690

Segmentation And Characterization Of Masses InThe Digital MammogramsCorresponding Author:Dr. Rash Dubey,Professor, ECE Dept, Hindu College of Engg, Sonepat, 121003 - India

Submitting Author:Dr. Rash B Dubey,Professor, ECE Dept, Hindu College of Engg, Sonepat, 121003 - India

Article ID: WMC00726

Article Type: Research articles

Submitted on:24-Sep-2010, 05:29:08 AM GMT Published on: 24-Sep-2010, 05:29:25 PM GMT

Article URL: http://www.webmedcentral.com/article_view/726

Subject Categories:BREAST

Keywords:Breast cancer, Malignant Breast Masses, Digital Mammograms, MGMRGT and WatershedSegmentation.

How to cite the article:Dubey R , Hanmandlu M , Gupta S . Segmentation And Characterization Of Masses InThe Digital Mammograms . WebmedCentral BREAST 2010;1(9):WMC00726

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Segmentation And Characterization Of Masses InThe Digital MammogramsAuthor(s): Dubey R , Hanmandlu M , Gupta S

Abstract

Breast tumor segmentation is needed for monitoringand quantifying breast cancer. However, automatedtumor segmentation in mammograms poses manychallenges with regard to characteristics of an image.A comparison of two different semi-automatedmethods, viz., modified gradient magnitude regiongrowing technique (MGMRGT) and watershed methodis undertaken here for evaluating their relativeperformance in the segmentation of breast tumor. Aset of 6 mammogram images is used to validate theeffectiveness of the segmentation methods. TheMGMRGT segmentation shows better results thanthose due to watershed approach. The presentapplication is intended to assist the radiologist inperforming an in-depth examination of the breast atconsiderably reduced time.

Introduction

Breast cancer is the most common female cancer andthe second leading cause of cancer death amongwomen in America. A mammogram is an X-rayexamination of the breast. Mammography is the onlyeffective and viable techniques to detect breast cancer.It is proved that early stages of breast cancer are welltreatable. X-ray mammography is the current, clinicalGold Standard for the detection of breast cancer. It isa well understood and standardized procedure, itworks fairly well in postmenopausal women and it isinexpensive [1- 3]. The early stages of breast cancermay only have subtle indications which can be variedin appearance, making physical examinationineffective and making diagnosis difficult even forexperienced radiologist [4, 10].A mammogram mainly contains two regions: theexposed breast region and the unexposed non-breastregion. It is necessary to first identify the breast regionfor the reduction of the subsequent processingcalculation and the removal of the non-exposed breastregion. Bick et al. [5] have explored a segmentationmethod for the breast region based on themorphological gradient calculation and the modifiedglobal histogram analysis. Ball et al. [6] present anautomated mammographic computer aided diagnosis

system to detect and segment spicules. Mendez et al.[7] have described an automatic algorithm thatcomputes the gradient of gray levels. Wirth et al. [8]make use of the snakes and fuzzy approach [9] for thepurpose of segmentation.Elter and Horsch [11] focused their view onapproaches for mass and micro-calcification diagnosis,covering the segmentation of region of interests forextracting shape and contour features and theirposterior classification [12]. In particular neuralnetwork have demonstrated their efficacy in the clinicaldomain with diseases such as cancer where there is aweak relationship between the classes forming abenign or malignant diagnosis [13-14]. Hassanien [15]proposed a hybrid scheme that combines theadvantages of fuzzy sets and rough sets inconjunction with statistical feature extractiontechniques. An application of breast cancer imaginghas chosen and hybridization scheme have beenapplied to see their ability and accuracy to classify thebreast cancer images into two outcomes: cancer ornon-cancer. Du et al. [16] presented a framework forimprovement of mammogram classification, whichincludes a new preprocessing methodology forsegmenting, a unique associative rule discovery basedalgorithm for classification and an evaluation ofefficacy of raw derived features using fuzzy K-nearestneighbor and agglomerative clustering of associativefeatures. A co–occurrence analysis is applied toidentify statistically significant differences in pathologyco-occurrence patterns between premenopausal andpostmenopausal women [17, 18].This paper explores the comparison of the MGMRGTand morphological watershed approach forsegmentation.

Methods

Modified gradient magnitude region growing technique(MGMRGT) In the first step proper threshold is chosen in order todistinguish the interior area from other organs in theMR image dataset. Then modified gradient magnituderegion growing algorithm is applied, in which gradientmagnitude is computed by Sobel operator andemployed as the definition of homogeneity criterion.This implementation allowed stable boundary



detection when the gradient suffers from intersectionvariations and gaps. By analyzing the gradientmagnitude, the sufficient contrast present on theboundary region that increases the accuracy ofsegmentation [19].To calculate the size of segmented tumor therelabeled method based on remaps the labelsassociated with object in a segmented image such thatthe label numbers are consecutive with no gapsbetween the label numbers used. Any object can beextracted from the relabeled output using a binarythreshold. Here, the algorithm is adjusted to extractand relabeled the tumor and then find its size in pixels.The algorithm works well in two stages. The first stageis to determine the input image labels and the numberof pixels in each label. The second stage is todetermine the output requested region to get totalnumber of pixels accessed. Segmented areas areautomatically calculated and to get desired tumor areaper slice [19-20].Fig. 1: (a) Original image, (b) segmented mage, (c)extracted tumor after MGMRGT and ROI.Watershed Segmentation (WS)A watershed line is defined as the line separating twocatchment’s basins, as shown in Fig. 2. The rain thatfalls on either side of the watershed line will flow intothe same lake of water. The image gradient can beviewed as terrain. The homogeneous regions in theimage usually have low gradient values whichrepresent valleys, while edge represents the peakshaving high gradient values. Vincent et al. [21]propose the immersion simulation algorithm for thecalculation of watershed lines.Fig. 2: Watershed line with catchment basins. The watershed transform detects intensity valleys inthe image and the image is enhanced by highlightingthe intensity valleys. The enhanced image is used toconvert the objects of interest into intensity valleys.We detect all intensity valleys below a particularthreshold with output as a binary image. Thenimposed minimum function will modify the image tocontain only valleys. The imposed minimum functionwill also change a valley's pixel values to zero. Allregions containing an imposed minimum will bedetected by the watershed transform. Thesegmentation of the imposed minima image isaccomplished with the watershed function. Watershedfunction returns a label matrix containing non-negativenumbers that correspond to watershed regions. Pixelsthat do not fall into any watershed region are given avalue of zero. The label matrix is to convert it to a colorimage. In the colored version of the image, eachlabeled region is displayed in a different color and thepixels that separate the region are white. We specify a

polygonal region of interest of the objects in binaryimage. Total area is a scalar whose value correspondsroughly to the total number of pixels in the image.Morphological OperationsMorphology is an operation of image processingbased on shapes. The value of each pixel in the outputimage is based on a comparison of the correspondingpixel in the input image with its neighbors. By choosingthe size and shape of the neighborhood, we canconstruct a morphological operation that is sensitive tospecific shapes in the input image [22-24]. Dilation anderosion are two fundamental morphological operations.Dilation adds pixels to the boundaries of objects in animage, while erosion removes pixels from the objectboundaries. The number of pixels added or removedfrom the objects in an image depends on the size andshape of the structuring element used to process theimage.Contrast EnhancementFirst image is dilated and then eroded using matlabfunctions. Now, to minimize the number of valleysfound by the watershed transform, we maximize thecontrast of the objects of interest. A commontechnique for contrast enhancement is the combineduse of the top hat and bottom-hat transforms. Thetop-hat transform is defined as the difference betweenthe original image and its opening. The opening of animage is the collection of foreground parts of an imagethat fit a particular structuring element.The top-hat image contains the peaks of objects thatfit the structuring element. The bottom-hat transform isdefined as the difference between the closing of theoriginal image and the original image. The closing ofan image is the collection of the background parts ofan image that fit a particular structuring element[22-24]. To maximize the contrast between the objectsand the gaps that separate them from each other weadd the top-hat image to the original image and thensubtract the bottom-hat image from the result. Top-hatimage contains the peaks of objects that fit thestructuring element. In contrast, the bottom-hat imageshows the gaps between the objects of interest. Tomaximize the contrast between the objects and thegaps that separate them from each other, thebottom-hat image is subtracted from the original andtop-hat image. The various processes involved inwatershed segmentation are shown in Fig. 3.Fig. 3 (a-k): Various steps involved during watershedsegmentation.

Results



The mammograms that are positive for the malignantmass are col lected for this study from themammography image analysis (MIAS) database. Thetotal number of cases is 6. Mammograms come upwith labels and contain noise and irregularities thatneed to be eliminated prior to the segmentation. Thiscan be achieved by using several denoisingtechniques, viz. morphological open-closereconstruction filter and morphological top and bottomhat filtering.The algorithm is implemented on personal computer(1.8GHz CPU, 2GB RAM). The proposed algorithmshave been tested on 6 mammograms containingmalignant masses. Expert-segmented data in all theimages are provided in Table 1. All images aresemi-automatically segmented and the results arecompared with the corresponding expert-segmentedones.We introduce two segmentation approaches formammogram images and investigate its application tothe detection of region of interest (ROI), whichincludes both masses and the pectoral muscles. In themammograms, masses are assumed to be distinctiveregions that are relatively brighter than thesurrounding background, while the pectoral musclesappear to be more uniformly bright making theirpresence at a predictable location. Different tumorarea obtained after MGMRGT and watershedsegmentation are tabulated in Table 3 and the resultsare validated with manually segmented expertradiologist. Table 1: Comparison of tumor area with an expertradiologist.

Conclusion(s)

Two semi-automated approaches are presented forthe segmentation of a tumor. These overcome theaccuracy and sensitivity limitations of the currentsolutions. Our goal here is to compare two populartechniques: MGMRGT and watershed with an expert’smanual segmentation. Recently attention is being paidto the semi-automatic segmentation methods on tumormeasurements in order to avoid the observervariability and therefore to increase the accuracy. Inthe study of the reliability of the breast tumor areameasurements, we quantitatively compare the expertmanual trace method with semi-automaticsegmentation methods. The semi-automaticsegmentation techniques require very less time togenerate tumor area measurements than the manual

method. Manual method is highly labor intensive andrequires more concentration than the semi-automaticmethod. Both methods have been tested extensivelyand results are validated numerically. The resultshows that MGMRGT segmentation better than thewatershed approach.

Authors Contribution(s)

Tested two methodology

References

1. P. Boyle and J. Ferly, Cancer incidence andmortality in Europe, Annuals of Oncology, vol.16(3):481-488, 2005.2. M. Hanmandlu, D. Vineel, and G. Singh, Computeraided fuzzy based technique for detection ofsuspicious masses and micro classifications frommammographic images, in Proc 2008 Intl. Conf.IICAI-07, 2008, pp. 344-358.3. H. S. Sheshadri, Breast Cancer Detection, Journalof Radiology, 4(1):232-234, 2005.4. I. Christoyianni, A. Kotras, E. Dermatas and G.Kokkinakis, Computer aided diagnosis of breastcancer in digitized mammograms, Comput. Med. Imag.and Graph, 26:309-319, 2002.5. U. Bick, M. L. Giger, R. A. Schmidt, R. M. Nishikawa,D. E. Wolverton and K. Doi, Automatic segmentationof digitized mammograms, Academic Radiology, 2: 1-9,1995.6. J. E. Ball, and L. M. Bruce, Digital mammographiccomputer aided diagnosis using adaptive level setsegmentation, in Proc. 2007 IEEE EMBS, 2007, pp.4973-4978.7. A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Sauto,J. L. Correa and J. J. Vidal, Automatic detection ofbreast border and nipple in digital mammograms,Computer methods and programs in biomedicine, 49:253-262, 1996.8. M. A. Wirth, and A. Stapinski, Segmentation ofbreast region in mammograms using snakes, Intl. Conf.on Computer and robot vision, 2004, pp. 385-392. 9. M. A. Wirth, J. Lyon, D Nikitenko,. and A. Stapinski,A fuzzy approach to segmenting the breast region inmammograms, in Proc. 2004 IEEE NAFIPS on Fuzzyinformation, 2004, 474-479.10. R. B. Dubey, M. Hanmandlu, S.K. Gupta, Acomparison of two methods for the segmentation ofmasses in the digital mammograms, Comput. Med.Imag. and Graph, 34(3):185–191, 2010.11. M. Etler and A. Horsch, “CADx of mammographic



mass and clustered micro-calcification: a review,” Med.Phy., vol. 36, Issue 6, pp. 2052-2068, 2009.12. A. Oliver, J. Freixent, J. Marti, E. Perez, J. Pontand E. R. E. Denton, A review of automatic massdetection and segmentation in mammographic images,Med. Imag. Anal., 14:87-110, 2010.13. P. Lisboa and A. Taktak, The use of artificialneural networks in decision support in cancer:systematic review, Neural Netw., vol. 9: 408-415, 2006.14. P. Lisboa, A review on evidence of health benefitfrom artificial neural networks in medical intervention,Neural Netw., 15:11-39, 2002.15. B. Verma, P. McLeod and A. Klevansky, A novelsoft cluster neural network for the classification ofsuspicious area in digital mammograms, PatternRecogn., 42:1845-1852, 2009.16. A Hassanien, Fuzzy rough set hybrid scheme forbreast cancer detection, Image and Vision Computing,25:172-183, 2007.17. S. Du, H. Singh and H. W. Thompson, Associativeclassification of mammograms using weighted rules,Expert Syst. with Appl., 36:9250-9259, 2009.18. S. M. Maskery, Y. Zang, R. M. Jordan, H. Hu, J. A.Hooke, C. D. Sr iver and M. N. L iebman,Co-occurrence analysis for discovery of novel breastcancer pathology patterns, IEEE Trans Inf. Technol inBiomed, 10(3):497-503, 2006.19. M. Sato, S. Lakare, M. Van and A. Kaufaman, Agradient magnitude based region growing algorithm foraccurate segmentation, in Proc. 2000 Int. Conf. onImage Processing, 2004, 3:448-451.20. S. Saraswathy, F. Crawford and S. J. Nelson,Semi-automated segmentation of brain tumor lesionsin MR images, in Proc. 2009 Intl. soc. Mag. Reson.Med., 2009, vol. 14:1609.21. L. Vicent, and P. Soille, Watersheds in digitalspaces: an efficient algorithm based on immersionsimulations, IEEE Transaction on Pattern Analysis andMachine Intelligence, 13(6):583-598, 199122. R. Van, D. Boomgard and R. Van Balen, Methodsfor Fast Morphological Image Transforms UsingBitmapped Images, Computer Vision, Graphics, andImage Processing: Graphical Models and ImageProcessing, 54(3): 252–254, 1992.23. R. Adams, Radial Decomposition of Discs andSpheres, Computer Vision, Graphics, and ImageProcessing: Graphical Models and Image Processing,55(5):325–332, 1993.24. R. Jones, and P. Soille, Periodic lines: Definition,cascades, and application to granulometrie, PatternRecognition Letters, 17:1057–1063, 1996.



1

Sam-pleNo.

ExpertradiologistArea(mm2)

MGMRGTMethod Area(mm2)

WSMethod Area(mm2)

RelativeError (%)(MGMRGT)

RelativeError(%)(WS)

123456

1000.0085.701000.6020000.302800.89900.00

1090.7878.36989.7620758.662758.13855.08

900.3270.10900.5418684.432271.19805.00

8.327.751.083.791.534.99

9.078.5610.066.5818.914.91

Illustrations

Illustration 1

Table 1: Comparison of tumor area with an expert radiologist.



1

1 (a) 1(b) 1(c)

Illustration 2

Fig. 1: (a) Original image, (b) segmented mage, (c) extracted tumor after MGMRGT and ROI.



Illustration 3

Fig. 2: Watershed line with catchment basins.



Fig. 3 (a): Original image.

Fig. 3(b): Filtered image.

Illustration 4

Fig. 3 (a-k): Various steps involved during watershed segmentation.



Fig. 3 (c): Eroded image Fig. 3 (d): Dilated image Fig. 3 (e): Top-hat image

Fig. 3(f): Original+tophat-bottomhat image.

Fig.3(g): Complementedimage.

Fig. 3 (h): Imposedminima.

Illustration 5

continue..



Fig. 3(i): Distancetransformed image.

Fig.3(j): Watershedsegmented image. Fig. 3(k): detected tumor

location.

Illustration 6

continue..



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segmentation and characterization of masses in the digital mammogram… · performance in the...

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