*VALLIAPPAN Raman 1, PUTRA Sumari 2 and MANDAVA Rajeswari 3. George town, Penang 11800, Malaysia. George town, Penang 11800, Malaysia

Transcription

1 38 A Theoretcal Methodology and Prototype Implementaton for Detecton Segmentaton Classfcaton of Dgtal Mammogram Tumor by Machne Learnng and Problem Solvng *VALLIAPPA Raman, PUTRA Sumar 2 and MADAVA Rajeswar 3 School of Computer Scence, Unversty Sans Malaysa, George town, Penang 800, Malaysa 2 School of Computer Scence, Unversty Sans Malaysa, George town, Penang 800, Malaysa 3 School of Computer Scence, Unversty Sans Malaysa, George town, Penang 800, Malaysa Abstract Breast cancer contnues to be a sgnfcant publc health problem n the world. Early detecton s the key for mprovng breast cancer prognoss. The CAD systems can provde such help and they are mportant and necessary for breast cancer control. Mcrocalcfcatons and masses are the two most mportant ndcators of malgnancy, and ther automated detecton s very valuable for early breast cancer dagnoss. The man objectve of ths paper s to detect, segment and classfy the tumor from mammogram mages that helps to provde support for the clncal decson to perform bopsy of the breast. In ths paper, a classfcaton system for the analyss of mammographc tumor usng machne learnng technques s presented. CBR uses a smlar phlosophy to that whch humans sometmes use: t tres to solve new cases of a problem by usng old prevously solved cases. The paper focus on segmentaton and classfcaton by machne learnng and problem solvng approach, theoretcal revew have been undergone wth more explanatons. The paper also descrbes the theoretcal methods of weghtng the feature relevance n case base reasonng system. Key words: Dgtal Mammogram, Segmentaton, Feature Extracton and Classfcaton.. Introducton Breast cancer s the most common cancer of western women and s the leadng cause of cancer-related death among women aged 5-54 [4]. Survval from breast cancer s drectly related to the stage at dagnoss. Earler the detecton, hgher chances of successful treatments. In an attempt to mprove early detecton, a study has been undertaken to process the screenng mammograms of breast cancer patents n order to analyze the mass/mcrocalcfcatons features that help to dfferentate bengn from malgnant cases. In ths paper we propose the selected shape-based features n order to classfy clustered masses between bengn and malgnant [5]. The computerzed analyss of mammographc masses performed n ths work can be dvded nto four stages: ) dgtzaton of mammograms and enhancement of mages; 2) detecton of suspcous areas; 3) extracton of features for every segmented tumors n the dgtzed mammogram; and 4) analyss of the features usng Case Based Reasonng technques. A Case- Based Reasonng algorthm s used for classfyng these cases nto bengn or malgnant cases. We have to be aware that Case-based Reasonng means usng prevous experence n form of cases to understand and solve new problems [3]. The man objectve of ths paper s to focus on the segmentaton and theoretcal revew for classfcaton of tumor by case base reasonng approach and how to apply weghts to the features, and mprove the accuracy rate. The paper s organzed as follows: In Secton 2, t clearly explans the exstng works of mammography detecton, then machne learnng technque s explaned n secton 3, ext n secton 4 problem solvng capabltes are explaned, next expermental results are shown n Secton 5. Secton 6 dscusses the shown expermental results. Fnally concluson and future works are specfed n

2 39 secton Exstng Research Works The problem of mage processng has been dvded nto several research areas and medcal research has been qute receptve of mage processng n applcatons lke x ray, computer aded tomography, ultrasound and magnetc resonance [8]. There are several exstng approaches were made to detect the abnormal tssues n breast mages and to detect the cancer earler. Zhang et al. [3] noted that the presence of spculated lesons led to changes n the local mammographc texture. They proposed that such a change could be detected n the Hough doman, whch s computed usng the Hough transform. They parttoned an mage nto overlappng ROIs and computed the Hough transform for each ROI. The Hough doman of each ROI was thresholded to detect local changes n mammographc texture and to determne the presence or absence of a spculated mass. Brzakovc et al. [4] use a two stage mult-resoluton approach for detecton of masses. Frst they dentfed suspcous ROIs usng Gaussan pyramds and a pyramd lnkng technque, based on the ntensty of edge lnks. Edges were lnked across varous levels of resoluton. Ths was followed by a classfcaton stage, where the ROI were classfed as malgnant, bengn or normal based on features lke shape descrptors, edge descrptors and area Petrck et al. [5] developed a two-stage algorthm for the enhancement of suspcous objects. In the frst stage they proposed an adaptve densty weghted contrast enhancement flter (DWCE) to enhance objects and suppress background structures. The central dea of ths flterng technque was that t used the densty value of each pxel to weght ts local contrast. In the frst stage the DWCE flter and a smple edge detector (Laplacan of Gaussan) was used to extract ROIs contanng potental masses. In the second stage the DWCE was re-appled to the ROI. Fnally, to reduce the number of false postves, they used a set of texture features for classfyng detected objects as masses or normal. They further mproved the detecton algorthm by addng an object-based regongrowng algorthm to t. La [6] made an approach based on a multresoluton Markov random feld model detect mass lesons. Its ntal wndow sze for segmentaton nfluences the senstvty of detecton. L [7] proposed a method on rs flter was developed to detect mass lesons of rounded convex regons wth low contrast. The rs flter enhances most round malgnant masses. However, some malgnant masses are shaped rregularly. The above methods show less than fve false postves per mage wth a true postve detecton rate of approxmately 90%.It s dffcult to compare the performance of these methods because ther databases are dfferent. 3. Machne Learnng Compare to all the exstng works; we developed mammographc tumor segmentaton by regon growng approach and classfcaton usng case base reasonng approach [5]. In ths paper there are two stages; frst stage ncludes machne learnng approach such as dgtzng the mages, preprocessng and segmentaton. Tumor segmentaton classfed as two types: Mass and Mcroclacfcaton.It s more dffcult to detect masses than mcrocalcfcatons because ther features can be obscured or smlar to normal breast parenchyma. Masses are qute subtle, and often occurred n the dense areas of the breast tssue, have smoother boundares than mcrocalcfcatons, and have many shapes such as crcumscrbed, speculated lobulated or ll-defned. Second stage s the problem solvng approach usng Case Base Reasonng method; new cases are solved by prevous solved old cases, whch s the man focus of the paper. Fgure llustrates the overall block dagram of tumor Classfcaton method. Dgtzng Mammogram Image Preprocessng Image Segmentaton Feature Extracton and Selecton Classfcaton Fg llustrates the overall block dagram of Mass Classfcaton method 3. Dgtzaton Machne Learnng Problem Solvng Case Base Reasonng Method Frst, the X-ray mammograms are dgtzed wth an mage resoluton of μm2 and 2 bts per pxel by a laser flm dgtzer. To detect mcrocalcfcatons on the mammogram, the X-ray flm s dgtzed wth a hgh resoluton. Because small masses are usually larger than 3mm n dameter, the dgtzed mammograms are decmated wth a resoluton of mm 2 by

3 40 averagng 4 4 pxels nto one pxel n order to save the computaton tme. Fg 2 llustrates orgnal mage [6] by reducng mage dmenson n averagng of 8 8 Matrx 3.2 Preprocessng Preprocessng s an mportant ssue n low-level mage processng. The underlyng prncple of preprocessng s to enlarge the ntensty dfference between objects and background and to produce relable representatons of breast tssue structures. An effectve method for mammogram enhancement must am to enhance the texture and features of tumors. The reasons are: () lowcontrast of mammographc mages; (2) hard to read masses n mammogram because t s hghly connected to surroundng tssues; the enhancement methods are grouped as global hstogram modfcaton approach and local processng approach. Current work s carred out n global hstogram modfcaton approach. Preprocessng Global Hstogram Modfcaton Local 3.3 Segmentaton Descrpton Re-assgn the ntensty values of pxels to make the new dstrbuton of the ntenstes unform to the utmost extent Feature-based or usng nonlnear mappng locally Advantage Effectve n enhancng the entre mage wth low contrast Effectve n local texture enhancement Table llustrates the preprocessng approach After preprocessng, next stage s to separate the suspcous regons that may contan masses from the background parenchyma, that s to partton the mammogram nto several non-overlappng regons, then extract regons of nterests (ROIs), and locate the suspcous mass canddates from ROIs. The suspcous area s an area that s brghter than ts surroundngs, has almost unform densty, has a regular shape wth varyng sze, and has fuzzy boundares. The Segmentaton methods do not need to be excrucatng n fndng mass locatons but the result for segmentaton s supposed to nclude the regons contanng all masses even wth some false postves (FP). FPs wll be removed at a later stage. We chose regon growng process for segmentaton of a mammographc tumor. The basc dea of the algorthm s to fnd a set of seed pxels n the mage frst, and then to grow teratvely and aggregate wth the pxels that have smlar propertes. If the regon s not growng any more, then the grown regon and surroundng regon are obtaned. Regon growng may be appled globally or locally. If the grown regon of a seed has an average ntensty greater than that of the surroundng, the regon s classfed as the parenchyma, or fat, tssue.the accuracy reaches 70% for classfyng the tssue patterns. The key ssue of regon growng s to fnd a crteron that checks whether the gray level values of ts neghbors are wthn a specfed devaton from the seed. The performance of the algorthm depends on the enhancement method; therefore the algorthm wll get a better result f a better enhancement method s appled. Global hstogram Modfcaton Enhancement method was appled to enhance the mages before regon growng. Second ssue of regon growng s to fnd the sutable seeds. An automatc seed selecton was appled. There are three parts n mammograms: a fat regon, a fatty and glandular regon, and a dense regon. Accordng to the ntensty values and local contrast between a seed pxel and ts neghbors n the three parttons, three sets of seed pxels are selected from the parttoned regons. The regon growng process starts from seed pxels. The gray level mappng shows local valleys at the boundary of two neghborng regons. The local peak just after the local valley n the gray level mappng gves a sgn of the swtch between the absorpton of pxels n the boundary of the current regon and the absorpton of pxels n the neghborng regon. When the grown regon sze s equal to or greater than a mnmum regon sze wth the stoppng condton such as speckle nose, touchng prevous regon, new adjacent regon, contrast lmtaton. Once the stoppng condton s acheved, regon growng s appled and the masses are segmented. Below algorthm summarzes the regon growng procedures for segmentng the masses. Algorthm. Pull the top tem from the growth lst. 2. Mark ths pxel n the output mage - t s part of the regon. 3. Examne each neghborng pxel. For each pxel, f t has not already been vsted and t fts the growth crtera, mark t as vsted and add t to the growth lst. 4. Go back to step and repeat untl there are no more tems n the growth lst and extract the part of tumor.

4 4 4. Feature Extracton After segmentng the tumors n mammogram, The ROI hunter provdes the regons of nterest wthout gvng further nformaton [2]. To ths purpose sutable features should be selected so that a decson makng system can correctly classfy possble pathologcal regons from healthy ones. Feature extracton plays a fundamental role n many pattern recognton tasks. In ths paper twelve features (global and local features) are extracted from the segmented tumors. Below table llustrates the features. Feature of Selecton Skewness Kurtoss Crcularty Compactness Contrast Descrpton j0 j0 j0 j0 Table 2 llustrates the Local and Global Features A A P 2 A P P P Standard devaton 2 Intensty Area Length Breadth Convex Permeter Roughness g ( / 2 j0 tumor area True Length of Mass True Breadth of Mass 3 Permeter of the convex hull of the mass Permeter/Convex Permeter Case Base Reasonng Case-Based Reasonng (CBR) ntegrates n one system two dfferent characterstcs: machne learnng capabltes and problem solvng capabltes. CBR uses a smlar phlosophy to that whch humans sometmes use: t tres to solve new cases (examples) of a problem by usng old prevously solved cases. The process of solvng new cases contrbutes wth new nformaton and new knowledge to the system. Ths new nformaton can be used for solvng other future cases. The basc method can be easly descrbed n terms of ts four phases.the frst phase retreves old solved cases smlar to the new one. In the second phase, the system tres to reuse the solutons of the prevously retreved cases for solvng the new case. The thrd phase revses the proposed soluton. Fnally, the fourth phase retans the useful nformaton obtaned when solvng the new case. In a Case-Based Classfer System, t s possble to smplfy the reuse phase. Classfyng the new case wth the same class as the most smlar retreved case can do reuse [3]. Elaborate a ew Case Problem Doman Knowledge Case Base ew case Revsed case Retan Problem Retreved Case Acqured Case ew case Reuse Acqured Case Solved Case Fg 3 llustrates the Case Base Reasonng The kernel n a Case-Based Reasonng system s the retreval phase (phase ). Phase retreves the most smlar case or cases to the new case. Obvously, the meanng of most smlar wll be a key concept n the whole system. Smlarty between two cases s computed usng dfferent smlarty functons. For our purpose n ths paper, we use the smlarty functons based on the dstance concept. The most used smlarty functon s the earest eghbor algorthm, whch computes the smlarty between two cases usng a global smlarty measure. The future practcal mplementaton (used n our system) of ths functon s based on the Mnkowsk s metrc. Mnkowsk s metrc s defned as: Smlarty Case _ x, Case _ y r F W x y r

5 42 () Where Case_x, Case_y are two cases, whose smlarty s computed; F s the number of features that descrbes the case; x y represent the value of the th feature of case Case_x and Case_y respectvely; and w s the weght of the th feature. In ths study we test the Mnkowsky s metrc for three dfferent values of r: Hammng dstance (r = ), Eucldean dstance (r = 2), and Cubc dstance (r = 3). Ths smlarty functon needs to compute the feature relevance ( w ) for each problem to be solved. Assumng an accurate weght settng, a case-based reasonng system can ncrease ther predcton accuracy rate. We use also the Clark s and the Cosne dstance, both are based on dstance concept and also use weghtng features. Sometmes human experts can not adjust the feature relevance, automatc method can solve ths lmtaton. 5. Feature Selecton Based on Rough Set theory Ths paper presents a revew on weghtng method based on the Rough Sets theory ntroduced by Pawlak [0]. It s a sngle weghtng method (RSWeght) that computes the feature weghts from the ntal set of tran cases n the CBR system. We also ntroduce a weghtng method that computes the Sample Correlaton among the features and the classes that the cases may belong to. The dea of the rough set conssts of the approxmaton of a set by a par of sets, called the lower and the upper approxmaton of ths set. In fact, these approxmatons are nner and closure operatons n a certan topology generated by the avalable data about elements of the set. The man research trends n Rough Sets theory whch try to extends the capabltes of reasonng systems are: () the treatment of ncomplete knowledge; (2) the management of nconsstent peces of nformaton; (3) the manpulaton of varous levels of representaton, movng from refned unverses of dscourse to coarser ones and conversely. We compute from our unverse (fnte set of objects that descrbe our problem, the case memory) the concepts (objects or cases) that form parttons of that Unverse. The unon of all the concepts made the entre Unverse. Usng all the concepts we can descrbe all the equvalence relatons (R) over the unverse. Let an equvalence relaton be a set of features that descrbe a specfc concept. The unverse and the relatons form the knowledge base, defned as KB = (U; R). Every relaton over the unverse s an elementary concept n the knowledge base [0]. All the concepts are formed by a set of equvalence relatons that descrbe them. So we search for the mnmum set of equvalence relatons that defne the same concept as the ntal set. The set of mnmum equvalence relatons s called reduct. A reduct s the essental part, whch suffces to defne the basc concepts occurrng n the knowledge. The core s the set of all ndspensable equvalence relatons over the unverse, n a certan sense the most mportant part of the knowledge. The core s defned as the ntersecton of all the reducts. Reducts contan the dependences from the knowledge. We can use ths nformaton to wegh the relevance of each feature n the system [0]. An attrbute that does not appear n the reduct has a feature weght value of 0.0, whereas an attrbute that appears n the core has a feature weght value of.0. The rest has a feature weght value dependng on the proportonal appearance n the reducts. Ths s the weght feature nformaton that we use n the case-based classfer system. 5.2 Sample Correlaton Sample Correlaton computes the weghts w computng the sample correlaton whch exsts between each feature x and the class z [0]. The Sample Correlaton s defned as: Sample_ Correlaton x, z j xj S x x z jz Sz (2) Where s the number of cases; x j s the value of th feature for the case j; z j s the class whch belong to the case j. s the mean of the th feature; s the mean f the classes; S x s the standard devaton of the feature x ; and S s the standard devaton of class z. z Therefore weghtng feature method needs a huge amount of cases to develop a good weghtng feature selecton durng the retreval phase. If the system accuracy rate ncreases, then there s enough nformaton n the system to develop a good weghtng polcy 6. Expermental Results Currently the project s n the ntal stage (prototype) and frst phase of mplementatons are done n matlab. Therefore there are forty sx X-ray mammograms taken for testng the method. The mammograms were taken from the patent fles n the Free Mammogram Database (MIAS). In addton, 0 mammograms were used for tranng of the classfer. The 46 mammograms nclude 5

6 43 malgnant and 0 bengn masses that are n dense regons wth glandular tssues, varous breast areas nvolvng ducts, breast boundares, blood vessels, and/or glandular tssues. After segmentaton, feature extracton and classfcaton need to performed and tested. The below results show the varous stages of mammogram segmentaton. Feature extracton and Classfcaton need to be refned and mplemented n future works. Fg 4 llustrates the orgnal mage from MIAS database [6].Preprocessng the Image, Huntng for ROI, Hstogram of Orgnal Image and segmentng the tumor. Experment Segmentaton Result Accuracy Experment Bengn 63.3% Experment 2 Malgnant 73.7% Experment 3 Bengn 68.6% Table 2 llustrates the results of Bengn and Malgnant tumors Fg 5 llustrates the orgnal mammogram mage [6], segmentaton tumor for malgnant cases and bengn cases usng regon growng method. Fg 6 llustrates the results of segmented malgnant tumors Fg 7 llustrates the results of segmented bengn tumors The effcency and complexty of ths system was mprovng than other systems presented n lterature. The performance of the algorthm on the tranng set was a TP rate of 62% wth 0.4 FP per mage. The algorthm was tested usng a set of 46 mages, consstng of 5 malgnant cases and 0 bengn cases. Performance of the algorthm on the testng set was a TP of 60% and average of 0.5 false clusters per mage. Table 2 shows that average accuracy of detectng bengn and malgnant ranges n 60% -70%. Dscusson The tests were dvded nto 2 stages. The frst part concerns subjectve tests of the ntal detecton of masses and another one focused on the classfcaton usng the selected feature sets. The subjectve tests were performed by mage processng and experts n radology. Radologst s suggestons were used regardng mass shapes and ther occurrence terms. The ntal detecton of mass was optmzed wth regard to nfluence of mage preprocessng, sze and shape of structurng element n global hstogram modfcaton approach, whereas to re-assgn the ntensty values of pxels to make the new dstrbuton of the ntenstes unform to the atmost extent. After that regon growng segmentaton s appled to detect and segment the tumor. On the segmented tumor part, features are selected for classfcaton. Therefore an m 2 real valued matrx s obtaned for each mammography, whch contans as many rows (m) as the number of masses are analyzed n the mage, whle the number of columns (2) s related to the computed shape features for every mass. In order to feed ths nformaton to the system of case base classfcaton, the matrx s flattened nto a vector. Ths process s acheved computng the mean value of each feature of the mass present n the mage. Therefore, an mage can be reduced to a real-valued vector wth 2 features. The human experts also decded whch tranng and test sets must be used. The tranng set contaned 0 samples, whle the test set had 36 samples. The ntal results were evaluated comparng the result of ths classfcaton wth the dagnoss

7 44 gven by the bopses. Currently the classfcaton of bengn and malgnant was dentfed wth accuracy range of 60-70%. Concluson The paper provdes the methodology wth partal results of segmentaton and explans theoretcally how mammogram tumor classfcaton s performed through case base reasonng method. Frst stage of mammogram mass segmentaton result s shown n ths paper, second stage s under mplementaton, so the conceptual framework of classfcaton method s descrbed on the paper. Info structure presented n ths paper when successfully mplemented would have an mmense mpact n the area of computer-aded dagnoss system. In future the methodology can be appled n a varety of medcal mage applcatons Acknowledgments I would lke to thank School of Computer Scence and Insttute of Post graduate Studes, Unversty Sans Malaysa for supportng to progress my research actvtes. References [] Kohonen T, Self Organzaton and Assocatve Memory, Sprng-Verlag, Hdelbarg, (998) [2] Hall EL, Computer Image Processng and Recognton, Academc Press, ew York, (978). [3] Woods R.E, Dgtal Image Processng, Adsson Welsely, Readng, (992). [4] Kapur T, Model based Three Dmensonal Medcal Image Segmentaton, MIT, (992). [5] Sheshadr HS, Kandaswamy A., Detecton of breast cancer by mammogram mage segmentaton. JCRT journal, Page no (2005). [6] S.M. La X. L and W.F. Bschof, On technques for detectng crcum- scrbed masses n mammograms, IEEE Trans Med Imagng 8, (989). [7] H.D. L M. Kallerg L.P. Clarke, V.K. Jan, and R.A. Clark, Markov random feld for tumor detecton n dgtal mammography, IEEE Trans Med Imagng 4, , (995). [8] H. Kobatake, M. Murakam H. Takeo, and S. awano, Computerzed detecton of malgnant tumors on dgtal mammograms, IEEE Trans Med Imagng 8, , (999). [9]. Otsu, A threshold selecton method from gray-level hstograms, IEEE Trans System Man Cybernet SMC-9, (979). [0] Z.Pawalak, Rough Sets: Thoertcal Aspects of Reasonng Data, Kluwer Academc Publcaton (99). [] Jang, Y., R.M. shkawa and J. Papaoannou, "Requrement of Mcrocalcfcaton Detecton for Computerzed Classfcaton of Malgnant and Bengn Clustered Mcrocalcfcatons" In Proceedngs of the SPIE Conference on Image Processng, vol. 3338,pp San Dego (USA), (998). [2] Dhawan, A.P., and Y. Chtre,"Analyss of Mammographc Mcrocalcfcatons usng Gray-level Image Structure Features" IEEE Transactons of Medcal Imagng, (5) pp ,(996). [3] Aamodt, A., and E. Plaza, Case-based reasonng: Foundatons ssues, methodologcal varatons, and system approaches. AI Communcatons, 7: 39-59, (994). [4] Fajardo, L.J., and M.B. Wllams, The Clncal Potental of Dgtal Mammography, In Proceedngs of the 3rd Internatonal Workshop on Dgtal Mammography, pp Chcago (USA), (996). [5] Raman Vallappan and Putra Sumar Dgtal Mammogram Segmentaton: An Intal Stage n 4th IASTED Internatonal conference on Advanced Computng Scence and Technology, Langaw Malaysa, (2008). [6] Mammogram Image Analyss Database, UK. Vallappan Raman s currently dong hs PhD n mammogram tumor segmentaton and classfcaton at Unversty Sans Malaysa. He has completed hs Masters Degree n 2005 at Unversty Sans Malaysa and completed hs Bachelor of Engneerng n Computer Scence n 2002 at Madura Kamaraj Unversty. He has been workng as lecturer for past four years n well establshed unversty. He have undergone varous research projects under major government grants and publshed papers, artcles and journals. Hs research nterest s n medcal magng, watermarkng and health nformatcs. Putra Sumar s currently an Assocate professor n School of Computer Scence, Unversty Sans Malaysa. He has undergone varous research projects under government and unversty grants. He has supervsed many postgraduate and undergraduate students. Hs research areas are n appled nformatcs, multmeda and mage processng. He has publshed many papers n hghly repuatated journal and conferences. Mandava Rajeswar s currently an Assocate Professor n School of Computer Scence, Unversty Sans Malaysa. Hs research areas are n computer vson and medcal magng.