A New Machine Learning Algorithm for Breast and Pectoral Muscle Segmentation

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1 Avalable onlne European Journal of Advances n Engneerng and Technology, 2015, 2(1): Research Artcle ISSN: X A New Machne Learnng Algorthm for Breast and Pectoral Muscle Segmentaton Huda Al-Ghab, Y Wang and Reza Adham Department of Electrcal and Computer Engneerng, The Unversty of Alabama n Huntsvlle, Alabama, USA hag0002@uah.edu ABSTRACT Automatc mammogram regstraton provdes nformaton about gradual changes n temporal mammograms. Segmentaton of landmarks such as the breast, breast skn lne, and pectoral muscle are requred for ths process. Ths paper presents a new machne learnng algorthm, known as margn settng algorthm (MSA), to segment the breast and pectoral muscle. MSA creates multple prototypes to enclose patterns belongng to dfferent classes. In ths research, we appled MSA to segment the breast and pectoral muscle. The performance of our algorthm s compared wth four dfferent algorthms; neural network (NN), and three thresholdng algorthms; ant colony optmzaton (ACO), global, and Otsu. These algorthms were tested on 554 mammograms from 125 patents. Subjectve evaluaton, by four researchers n the area of pattern recognton, was used to compare the outcomes. MSA outperformed NN algorthm n 84.21% of the mammograms. Also, MSA outperformed the other three algorthms n 98.12% of the mammograms. Key words: Margn settng algorthm, machne learnng, hyperspheres, thresholdng and mammogram regstraton INTRODUCTION Image segmentaton s the process of extractng meanngful nformaton from an mage for a specfc task or applcaton. It parttons the mage nto dfferent portons or segments usng dfferent technques [1-3]. Segmentaton algorthms can vary from a smple algorthm that uses the range of the mage ntenstes to compute a threshold value to a complex algorthm that utlzes varous mage processng tools and algorthms. Usually, the mage type and the nformaton needed to be extracted determne the segmentaton method. Segmentaton algorthms can be subdvded nto three categores; thresholdng, boundary detecton, and regon mergng. In the thresholdng category, a threshold value s computed to separate an object of nterest from the background. Image hstogram can be used for ths purpose to determne the value that subdvdes the mage nto two classes,.e., object and background classes. These two classes contan all the pxel values that are below and above a threshold value. Ths algorthm s known as global thresholdng wth one threshold value appled to the entre mage. When the threshold value vares n dfferent mage regons t s known as local or varable thresholdng [1]. Thresholdng provdes good results when the object s easly separable from the background. Boundary detecton segmentaton algorthms attempt to detect an object usng ts boundary. Ths can be done by computng the local maxmum gradent or the zero-crossng of the second dervatve. Algorthms such as Laplacan or Canny can be used for ths category. In the thrd category, adjacent smlar ntensty values are combned together to merge smlar regons and detect objects. Watershed and morphologcal algorthms can be used for ths purpose [1]. Segmentaton s wdely used n many real tme applcatons. Ths paper explores the applcatons of segmentaton n mammography. Mammogram segmentaton s used for leson detecton and landmark detecton for the purpose of mammogram regstraton. Also, segmentaton can be used to remove or dscard unnecessary nformaton such as background, labels, or even some breast tssues. Lesons n mammograms nclude masses, calcfcatons, and archtectural dstortons [4-6]. Segmentaton s the only avalable tool for detectng these lesons. Some mammogram nformaton such as breast boundary, tssues, npple, and pectoral muscle can be used to regster 21

2 Huda Al-Ghab et al sequence of mammograms. Image regstraton attempts to map nformaton n a seres of mages usng these landmarks [2-3]. The focus of ths paper s to use supervsed machne learnng algorthm for the detecton of these landmarks as nputs for mammogram regstraton. A machne learnng algorthm classfes nput data to ts proper class based on prevous knowledge learned from a tranng set. Ths s done by determnng the boundares or margns that best separate the classes. Decson boundares are defned usng the ponts that are close to the margns. In a support vector machne (SVM) algorthm these ponts are known as support vectors. In ths paper, we present a relatvely new machne learnng algorthm that s known as margn settng algorthm (MSA). MSA uses prototypes to determne the decson boundares [9-12]. MSA has been appled to applcatons such as artfcal colours segmentaton, nose estmaton, and hyperspectral [7], [15], [17-18]. Recently, a Fourer-based MSA was proposed to recognze the shape, sze, pose, and locaton of a target n a scene [13]. MSA appears to provde better results for classfyng nonlnear patterns compared wth other machne learnng algorthms such as SVM and neural network (NN) [17-18]. MARGIN SETTING ALGORITHM Margn settng algorthm (MSA) s consdered as a machne-learnng algorthm. A machne-learnng algorthm conssts of two phases: learnng and classfcaton. The learnng phase uses a set of nput patterns wth a known class (tranng dataset). In ths phase, features such as length, shape, colour, or ntensty values, are extracted from each pattern. Only the features that classfy the patterns to ther classes are selected. Decson boundary s defned between patterns of dfferent classes. The decson boundares are used n the classfcaton phase to classfy the testng patterns to ther correspondng classes. When a machne learnng algorthm s desgned to segment an object n a gray scale mage, the nput features may be the ntensty values and the output classes are the object and background. MSA creates number of prototypes of normal dstrbuton to compute the decson boundares. TRAINING PHASE - PARTITION PROCESS The partton process starts by subdvdng the orgnal tranng set, S, nto number of smaller subsets. Ths s manly done to search for a prototype for each subset. The subsets should not overlap and should cover the entre set as gven n the followng equaton: S = U S (1) where >1 S s a non-empty subset and j S I S = for j. For each subset, a prototype G s generated. G has a decson boundary that s dentfed by a radus and a centre. G s defned as: G = {(, r, ), 1 k M, 1 m P} (2) δ η k k m δ and r k k are the center and radus of G, respectvely, ηm s the class label wth P total number of classes, and M s the number of prototypes that belong to class η m. In the begnnng, N random ponts are selected as ntal prototypes. The random prototypes are selected wthn the same range of the ponts n the tranng set usng normal dstrbuton. The Eucldean dstance between each prototype and the ponts n the tranng set s computed. Fg. 1 a) shows a tranng set of two classes, class and non-class sets, wth 10 ponts for the class set that are shown as red * symbol and 10 ponts for the non-class set that are shown as blue + symbol. Four prototypes are shown as cyan o symbol. TRAINING PHASE - EVOLUTION PROCESS The evoluton process computes the optmal prototypes usng the ntal ones constructed durng the partton process. Prototypes wth a mnmum dstance to a pont n the class set and of maxmzed zero-margn radus are consdered as potental prototypes. Zero-margn s defned as the dstance from the centre of the prototype to the closest non-class pont. In Fg. 1 a), only two prototypes satsfy these condtons and consdered as potental prototypes for mutaton 0 as seen n Fg. 1 b). The dstance between each potental prototype and the closest class pont and the zero-margn radus are shown n Fg. 1 b). Fgure of mert s defned as the number of ponts from the class set around a potental prototype that are wthn the zero-margn radus. Fgure of mert s a measurement of true postve rate for a gven prototype. Each prototype has ts own fgure of mert. The fgure of merts for the two prototypes gven n Fg. 1 b) are enclosed by green and purple hyper-planes and are equal to 3 and 6, respectvely. The maxmum value for the fgure of mert s called the generatonal characterstc value, prototype wth MFm s consdered as the best prototype. MF m. A potental 22

3 Huda Al-Ghab et al Fg. 1 a) Constructng ntal prototypes, and b) potental prototypes wth ther correspondng fgure of merts If the potental prototypes for the zero mutaton do not cover all the ponts wthn the class set, then a new mutaton s started to compute new prototypes for the unclassfed ponts. The magntude of the new mutaton s computed as: τ = εαµ (3) where ε s a random sgn symbol, α [0,1], and µ s the maxmum perturbaton that s computed as: Mx+Mn Mx + δk, f δk > µ = 2 (4) δ, otherwse k Mx and Mn are the maxmum and mnmum values n the tranng set, respectvely. The potental prototypes for the new mutaton are computed usng the equaton: N = δ + +τ (5) n1 k 0 where N n1 s the random ponts for frst mutaton and δ k 0 s the centre of a potental prototype n mutaton 0 that s chosen as: k 1 b= 1 f b < ς ς s a random number [0,1] and f m = F m gm F m m= 1 gm s the total number of potental prototypes for a gven mutaton. The mutaton process s contnued untl all ponts n the class set are traned usng a potental prototype or when the maxmum number of mutatons (MM) s reached. There s MG number of generatons for the tranng phase and for each generaton there s MM mutaton. The same procedure can be carred out to select prototypes for the non-class set. Classfcaton Phase Durng the classfcaton phase the Eucldean dstance between the selected prototypes and all the ponts n the testng set, T = { γ, γ,..., γ } 1 2, s computed. γ, s a pont from the testng set and t s assgned to the class wth the smallest Eucldean dstance. MSA for Image Segmentaton Fg. 2 graphs MSA for mage segmentaton. The steps nvolved n the algorthm can be summarzed as the followng: Step 1, Constructon of the tranng set: Select M random ponts from the test mage to construct the tranng set t = { x, for = 1 to M }. t s a 1-D vector wth ntensty values that are correspondng to both object and background classes. k fb b= 1 f s the normalzed fgure of mert computed as: b (6) (7) 23

4 Huda Al-Ghab et al Step 2, Constructon of ntal prototypes: Create the ntal prototypes wthn the tranng set ntensty range and usng normal dstrbuton. The ntal prototypes are represented mathematcally usng Equaton (2). Step 3, Select potental prototypes from ntal prototypes: Examne each prototype to select the potental prototypes of mnmum Eucldean dstance to the object ponts and of maxmzed zero-margn radus. Step 4, Select fnal prototype for current mutaton: Compute the fgure of mert for each potental prototype. The prototype wth MF s selected as the fnal prototype for a gven mutaton. m Step 5, Mutaton to N ponts: Mutate δ of prototype G usng Equaton (5). k Step 6, Comparson: Repeat Steps 2 through 5 untl all the tranng ponts are traned or when the maxmum number of mutatons, MM, s reached. Based on expermental results, MM=20 provded good outcome for our dataset. Step 7, Start new generaton: If at least one pont n the tranng set s not classfed, then start a new generaton and reset the mutaton to mutaton 0. Step 8, Stop condton: Proceed to the classfcaton step f all the ponts n the tranng set are classfed or when the maxmum generaton, MG, s reached. Based on expermental results, MG=20 provded good outcome for our dataset. Step 9, Object classfcaton: Classfy each pxel n the mage, γ, to ts correspondng class usng the saved prototypes. γ s consdered as object pont f t has mnmum Eucldean dstance wthn the zero-margn radus. Fg. 2 MSA for mage segmentaton RESULTS AND DISCUSSION Dataset The segmentaton algorthm s mplemented on 554 full feld dgtal mammography mages for 125 patents. The mammograms are of CC and MLO vews. The pectoral muscle segmentaton algorthm s mplemented on the mammograms wth MLO vews only, wth total number of 284 mammograms. Four mammograms wth MLO vew have no pectoral muscle and elmnated from pectoral muscle segmentaton evaluaton step. The dataset s a prvate one provded by two radologsts who perform screenng mammography n USA and Europe. The two radologsts provded the annotaton for ther datasets. All malgnant cases are bopsy proven. Of the cases, 40 were of malgnant nature; four cases have lesons of dfferent shapes, seven wth masses, eght wth calcfcatons, three wth archtectural dstortons, fourteen wth asymmetrc denstes, and four wth asymmetrc denstes and archtectural dstortons. The cancer free cases consst of 39 normal cases, three cases wth masses, 41 cases wth calcfcatons, one case wth archtectural dstorton, and one case wth asymmetrc densty. The dataset ncluded two cases wth mplants. The densty dstrbuton for the cases s determned usng BI-RADS classfcaton. 36 of the cases s are almost fatty, 26 are wth scattered fbroglandular tssue, 45 are heterogeneously dense, and 18 are extremely dense. 24

5 Huda Al-Ghab et al Expermental Results MSA algorthm s used to separate the breast from the background n the full feld dgtal mammography mages. Durng the tranng phase of MSA algorthm, the decson boundares are computed usng ntensty values. Random ponts from the object and the background are selected for that purpose. The object s defned as the breast and the rest of the mage nformaton s the background. 20 ponts have been shown as a suffcent number to segment the object regardless of ts degree of non-unformty. As the background s more unform, fewer ponts are needed. In the classfcaton phase, the breast s classfed usng the computed decson boundares. All the ntensty values n the mammogram are the nput for the classfcaton phase. A post-processng operaton usng a medan flter of sze 5x5 s used to remove mpulse nose. MSA performance s subjectvely compared wth NN and several thresholdng algorthms: ACO, global, and Otsu [1], [8], [16], [19]. The nput mages for each algorthm are DICOM format of raw data and of sze approxmately 4000x3328. NN algorthm uses the same tranng and testng data ponts used for the MSA algorthm. For the thresholdng technques, hgh ntensty values, 15% of the maxmum ntensty values for each mammogram, are removed to reduce the varaton n the gray scale level and ncrease the accuracy of the segmentaton step. A smlar approach s appled for the detecton of pectoral muscle. Agan, the problem nvolves two classes; the pectoral muscle regon and background classes. The background class conssts of the rest of the breast regon,.e., parenchymal fatty tssues and the mammogram background. Frst, MSA s compared wth ACO, global, and Otsu thresholdng. The subjectve evaluaton s done by four experts n the area of pattern recognton usng GUI software. One expert s the developer of the algorthm. The other three experts are a Ph.D. and two Ph.D. canddates. The segmented mages are postoned randomly n the GUI wth no ttle and are shown sde by sde on the same page. The order of the segmented mages s scrambled so they would not appear at the same poston for dfferent cases. The experts compared them wth the orgnal mammogram to determne whch method provded better results. Table 1 and 2 show the results of subjectve evaluaton for breast segmentaton n MLO and CC vews, respectvely. Note that when at least two algorthms provded smlar results the output was consdered to be the same. Fg. 3 shows the results of breast segmentaton usng the dfferent thresholdng technques and MSA for four dfferent mammograms. MSA performance was superor n comparson to the other thresholdng algorthms. As the breast densty decreases, t has been noted that Otsu and global thresholdng faled to detect the fatty tssues wthn the parenchyma. When the mammogram has a gradual change n the gray scale level wth no sharp edges that separate the background from the skn lne, ACO faled to detect the skn lne and the fatty regon behnd t. Table -1 Subjectve Evaluaton of Breast Segmentaton n MLO Vew usng Four Algorthms Researcher\Algorthm ACO Global MSA Otsu Same Developer Ph.D. Professor Ph.D. Canddate Ph.D. Canddate Average Table -2 Subjectve Evaluaton of Breast Segmentaton n CC Vew usng Four Algorthms Researcher\Algorthm ACO Global MSA Otsu Same Developer Ph.D. Professor Average Table -3 Subjectve Evaluaton of Breast Segmentaton n MLO Vew usng MSA and NN Researcher\Algorthm MSA NN Same Developer Ph.D. Professor Ph.D. Canddate Average Table -4 Subjectve Evaluaton of Breast Segmentaton n CC Vew usng MSA and NN Researcher\Algorthm MSA NN Same Developer Ph.D. Professor Average

6 Huda Al-Ghab et al Fg. 3 Breast segmentaton, a) orgnal mammogram, b) ACO, c) global, d) MSA, and e) Otsu thresholdng, respectvely Fg. 4 Breast segmentaton n MLO vew for three mammogram, a) orgnal mammogram, b) MSA, and c) NN, respectvely 26

7 Huda Al-Ghab et al To overcome the bg dfference n performance between MSA and the thresholdng technques, MSA output s compared wth NN output. The outputs of the comparson are gven Tables 3 and 4 for MLO and CC vews, respectvely. Fg. 4 dsplays the segmented breast usng MSA and NN algorthms for three mammograms wth MLO vew. NN detected the breast n most cases but dd not provde smooth edges, especally when the breast s not well separable from the background wth no vsble sharp edge. Also, t dd not provde good segmentaton for almost fatty breasts. On the other hand, MSA provded better segmentaton for the breast regardless of the breast type. Detecton of the npple when t s shown n the profle was also not fully accomplshed by NN algorthm. MSA algorthm provded better results n detectng the npple, even when t s wth small ntensty values that are close to the background ntensty value. For pectoral muscle segmentaton, the segmented mages usng ACO, global, MSA, and Otsu are subjectvely evaluated to determne whch algorthm provded a better result n solatng the pectoral muscle from the rest of the mammogram nformaton. A protocol smlar to the one used n evaluatng the breast segmentaton s appled here. The results are gven n Table 5. Fg. 5 llustrates the results of the segmented mages usng ACO, global, MSA, and Otsu, respectvely, for four mammograms. The performance of MSA algorthm n detectng the pectoral muscle hghly depends on the breast type,.e., almost fatty, scattered fbroglandular, heterogeneously dense, and extreme dense. When the breast type s almost fatty or fbroglandular tssue, then the parenchymal tssue s completely separable from the pectoral muscle. In ths case, MSA provded excellent results. Also, MSA provded good results for heterogeneously dense breasts. The challenge s the extreme dense breast n whch the breast mostly conssts of fbroglandular r tssue and t s not separable from the pectoral muscle. Comparng the performance of the other three algorthms,.e., ACO, global, and Otsu, n detectng the pectoral muscle llustrated that Otsu provded better segmentaton followed by global and then ACO. The three algorthms provded acceptable results for mammograms of scattered fbroglandular ar and heterogeneously dense breasts. The correspondng mammogram has parencyhmal tssue that s well separable from the pectoral muscle wth fatty tssue n the regon between the pectoral muscle and parenchymal tssue. However, the three algorthms provded bad detecton of pectoral muscle when the breast type s extreme dense or almost fatty. When the breast type s almost fatty, lots of fatty tssues are falsely detected as part of pectoral muscle. Fg. 5 Separaton of pectoral muscle from the rest of breast tssues and background, a) orgnal mammogram, b) ACO, c) global, d) MSA, and e) Otsu thresholdng, respectvely 27

8 Huda Al-Ghab et al Table -5 Subjectve Evaluaton of Pectoral Muscle Detecton n MLO usng Four Dfferent Algorthms Researcher\Algorthm Developer Ph.D. Professor Ph.D. Canddate 1 Ph.D. Canddate 2 Average ACO Global MSA Otsu Same MSA for pectoral muscle segmentaton s also compared wth NN algorthm. The results of subjectve evaluaton are gven n Table 6. NN algorthm provdes good segmentaton results for almost fatty breast types. However, for scattered fbroglandular breasts, the fbroglandular tssue located next to the pectoral muscle are falsely detected as pectoral muscle as seen n Fg. 6. Also, NN dd not provde good detecton for extreme dense breast types. Table -6 Subjectve Evaluaton Pectoral Muscle Detecton n MLO usng MSA and NN Researcher\Algorthm Developer Ph.D. Professor Average MSA NN Same Fg. 6 Pectoral muscle segmentaton usng MSA and NN CONCLUSION A machne learnng algorthm to detect the breast and pectoral muscle s presented. Ths algorthm s known as the margn settng algorthm (MSA). MSA s a relatvely new supervsed algorthm that utlzes prototypes to determne decson boundares that best separate nonlnear nput patterns. We tested the algorthm on 554 mammograms for MLO and CC vews from 125 patents. The algorthm performance has been compared wth four algorthms; neural network, ant colony optmzaton, global, and Otsu thresholdng. For the detecton of breast, the new algorthm provded better results for 84.21% of the mammograms compared wth 15.79% for NN algorthm. For the detecton and separaton of the pectoral muscle from the rest of the breast tssues, MSA provded better results for 52.52% compared wth 47.48% for NN algorthm. Also, MSA accuracy was 64.97% compared wth 3.55%, 5.85%, and 25.89% for ACO, global, and Otsu thresholdng, respectvely. 28

9 Huda Al-Ghab et al Acknowledgements The authors are grateful to Dr. Melane Scott for her gudance to have better understandng for mammogram analyss and the current challenges n computer-aded dagnoss systems. REFERENCES [1] R C Gonzalez and R E Woods, Dgtal Image Processng, 3 rd edton, Prentce Hall, [2] A Goshtasby, 2-D and 3-D Image Regstraton for Medcal, Remote Sensng, and Industral Applcatons, John Wley & Sons, Hoboken, New Jersey, [3] J Hajnal and D Hll, Medcal mage regstraton, CRC press, [4] L C Hartmann and C L Loprnz, The Mayo Clnc Breast Cancer Book, 1 st edton, Good Books, Intercourse, Phladelpha, [5] O J Peart, Lange Q & A, McGraw-Hll, New York, [6] L Tabar, T Tot and P Dean, Understandng the Breast n Health and Dsease, 1 st edton, C&C Offset. [7] J H Caulfeld, K Hedary, Explorng Margn Settng for Good Generalzaton n Multple Class Dscrmnaton, Pattern Recognton, 2005, vol 38, ssue 8, p [8] J Tan, W Yu and L Ma, Antshrnk, Ant Colony Optmzaton for Image Shrnkage, Pattern Recognt Lett, 2010, vol. 13, p [9] J Fu, J H Caulfeld and A Bandyopadhyay, Parng Mathematcal Morphology wth Artfcal Color to Extract Targets from Clutter, Journal of Imagng Sc and Tech, 2007, vol. 51, no 2, p [10] J Fu and J H Caufeld, Desgnng Spectral Senstvty Curves for use wth Artfcal Color, Pattern Recognton, 2007, vol. 40, ssue 8, p [11] J Fu, J H Caulfeld, D Wu and W Tadesse, Hyperspectral Image Analyss usng Artfcal Color, Journal of Appled Remote Sensng, 2010, vol. 4. [12] J Fu, J H Caulfeld, D Wu and T Montgomery, Effects of Hyperellpsodal Decson Surfaces on Image Segmentaton n Artfcal Color, Journal of Electronc Image, 2010, vol. 19. [13] J Fu, J H Caulfeld and C Gelnn, Prmtve Attempt to Turn Images to Precepts, Internatonal Journal of Machne Learnng and Cybernetcs, 2014, Vol. 5. [14] H Caulfeld, F Jan and Y Seong Moo, Artfcal Color Image Logc, Informaton Scences, 2004,167,p.1-7. [15] H Al-Ghab, R Adham and H Dnh, An Improved Image Compresson Technque Based on Dagonal Edge Estmaton, Proc Of the 5 th Internatonal Mult-Conference on Engneerng and Technologcal Innovaton (IMETI), Orlando, Florda,2012. [16] J Fu, H Caulfeld and T Mzell, Applyng Medan Flterng wth Artfcal Color, Journal of Imagng Scence and Technology, 2005, 49(5), [17] Y Wang, R Adham, and J Fu, A Novel Machne Learnng Algorthm for Salt and Pepper Nose Removal, IEEE Conference, SSP, [18] H Al-Ghab and R Adham, On the Dgtal Image Addtve Whte Gaussan Nose Estmaton, Internatonal Conference on Industral Automaton, Informaton and Communcatons Technology (IAICT),Bal, Indonesa, 2014, p