Texture and Shape Content Based MRI Image Retrieval System N. Kumaran #, Dr. R. Bhavani #

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1 ISSN (Onlne) : ISSN (Prnt) : An ISO 397: 007 Certfed Organzaton, Volume 3, Specal Issue 1, February 014 Texture and Shape Content Based MRI Image Retreval System N. Kumaran #, Dr. R. Bhavan # # Department of Computer Scence and Engneerng, Annamala Unversty, Annamala Nagar, Chdambaram, Tamlnadu, Inda Abstract More number of technques about medcal mage retreval s used n medcal doman. In our Content Based Medcal Image Retreval System, hgh level semantcs of an mage s very mportant. Regardng wth ths ssue, we proposed a method usng co-occurrences matrx to extract texture features and Canny Edge Detecton s to extract shape features. Then K-means clusterng algorthm and Eucldean dstance measure are used to retreve smlar mages for a query mage n medcal dagnoss. The performance of ths system s effcent when compare to exstng System. Keywords Canny Edge Detecton; CBMIR, Cooccurrence Matrx; Eucldean Dstance Measure; K-means Clusterng A I. INTRODUCTION large number of medcal mages are generated by hosptals and clncs every day (such as CT, X-ray and MRI).Such mages consttute an mportant source of anatomcal and functonal nformaton for dagnoss of dseases, medcal research and educaton. It s well acknowledged that medcal mage database s key component n dagnoss and preventve medcne. Content based medcal mage retreval (CBMIR)[1][][3] s the dgtal mage searchng problem n large database that makes use of contents of mage themselves rather than relyng on the textual nformaton. Medcal mages generated n hosptals contan semantc nformaton. Ths nformaton can be used to retreve the mages. Under such crcumstance, medcal mage retreval has changed from prevous text based method to the content based method or the method whch combnes both. Accordng to the characterstc of medcal mages such as texture and shape features are the mportant low level features [4][5][6]. Quddus et.al [7] proposed a method, whch reveals superor robustness performance wth respect to accuracy, speed and multmodalty n CBMIR. In ther work, support vector machne (SVM) are used for dentfyng 3- D MR volume and performng semantc classfcaton of human bran nto varous semantc regon. A novel scheme has been proposed by Jann- Der Lee et.al [8] for mage retreval task usng the feature extracted drectly from a compressed or uncompressed mage. The texture nformaton s frst extracted by explotng the mult resoluton nature of wavelet decomposton, whch represents the horzontal, vertcal and dagonal frequency dstrbuton of an mage. Then calculate the mean and standard devaton of wavelet coeffcent of each sub-band as texture features. The technque of scale multplcaton s analyzed n the framework of Canny edge detecton proposed by Paul Bao et.al [9]. A scale multplcaton functon s defned as the product of the responses of the detecton flter at two scales. Edge maps are constructed as the local maxma by thresholdng the scale multplcaton results. The detecton and localzaton crtera of the scale multplcaton are derved. Reshma Chauudhar et. al [10] proposed an algorthm whch ncorporates the advantage of varous other algorthms to mprove the accuracy and the performance of retreval. The accuracy of color hstogram based matchng can be ncreased by usng color coherence vector for successve refnement. The speed of shape based retreval can be enhanced by consderng approxmate shape rather than exact shape. In addton to ths a combnaton of color and shape retreval s also ncluded to mprove the accuracy of the result A novel approach by successfully combnng rotaton nvarant contourlet transform and Fourer descrptor was proposed by Arun K.S. et.al [11]. Rotaton nvarant contourlet transform s used for texture feature and Fourer descrptor extract shape feature. Bag of feature approaches have become promnent for mage retreval and mage classfcaton task n the past decade. Jngyan et.al [1] proposed a novel vsual word weghtng method. The dscrmnatve power of each vsual word s analyzed by the sub-smlarty functon n the bn that corresponds to the vsual word. Each subsmlarty functon s then treated as a weak classfer. A strong classfer s learned by boostng method that combnes that weak classfer. Ths paper s organzed as follows. Secton II descrbes the overall archtecture of the proposed retreval technque. Secton III descrbes the co-occurrence matrx Copyrght to IJIRSET 41

2 ISSN (Onlne) : ISSN (Prnt) : An ISO 397: 007 Certfed Organzaton, Volume 3, Specal Issue 1, February 014 and extracton of texture features. Secton IV explans the extracton of shape feature usng canny edge detecton. Secton V depcts the K-means clusterng. Secton VI llustrates about the Eucldean dstance measure. Secton VII express the expermental work and dscuss the results obtaned from experments. Secton VIII tells a summary of the paper. Inverse dfference moment: Average of Gray: F6= N 1 0 j0 N 1 F7= [ 0 Average of Dfference: ( p ( ) 1+ (- j0 ] (4) (5) II. PROPOSED METHOD The block dagram of the proposed work s shown n the fg1. In ths paper we extract texture and shape features usng co-occurrence matrx [13] [14] and canny edge detecton [15]. F8= j [ j0 Gray non unform: N 1 0 ] N 1 F9= [ 0 Dfference non-unform: j0 F10= [ j0 N 1 0 ] ] (6) (7) (8) Fg. 1 Proposed CBMIR System Then K-means clusterng [16] and Eucldean dstance measure [17] are used for best medcal mage retreval. III. EXTRACTION OF TEXTURE FEATURES Snce we are nterested n the statstcal approach, frst we make use of the most sutable texture features. The major advantage of usng the texture attrbutes s obvously ther smplcty The most common features used n practce are the measures derved from spatal gray tone co-occurrence matrx.e., texture features such as contrast, angular second moment, entropy, energy, nerta, nverse dfference moment etc. These features have been wdely used n the analyss, classfcaton and nterpretaton of medcal mages. They are defned by the equatons as follows: Entropy: Energy: Inerta: F3= - j N 1 F4= 0 N 1 F5= 0 j0 p ( log( ) j0 ( ( ) p ( (1) () (3) Gray entropy: N 1 F1= 0 p ( lg j0 Dfference Entropy: F13= N 1 p ( lg j0 0 j0 N 1 0 Correlaton F14= ( x y j xy (9) (10) (11) IV. EXTRACTION OF SHAPE FEATURES The Canny edge detecton algorthm s known to many as the optmal edge detector. Canny's ntentons were to enhance the many edge detectors already out at the tme he started hs work. Based on these crtera, the canny edge detector frst smoothes the mage for elmnate nose. It then fnds the mage gradent to hghlght regons wth hgh spatal dervatves. The algorthm then tracks along these regons and suppresses any pxel that s not at the maxmum. By usng canny edge detecton we can fnd the edges n the followng mages s shown n fg.. Copyrght to IJIRSET 4

3 . ISSN (Onlne) : ISSN (Prnt) : An ISO 397: 007 Certfed Organzaton, Volume 3, Specal Issue 1, February 014 Fg. Sample edge detecton for bran, spne, and knee jont and abdomen MRI mages. By usng canny edge detecton the followng shape features has been extracted: Area: Area of selecton n square pxels or n calbrated square unts. (e.g., mm, μm, etc) Mean gray value: Ave rage gray value wthn the selecton. Ths s the sum of the gray values of all the pxels n the selecton dvded by the number of pxels. Standard devaton: Standard devaton of the gray values used to generate the mean gray value. Center of mass: Ths s the brghtness-weghted average of the x and y coordnates all pxels n the mage or selecton. These coordnates are the frst order spatal moments. Integrated densty: The sum of the values of the pxels n the mage or selecton. Ths s equvalent to the product of Area and Mean Gray Value. Medan: The medan value of the pxels n the mage or selecton. Skewness: The thrd order moment about the mean. Kurtoss: The fourth order moment about the mean. The followng fg.3 shows the sample output screen for extracton of texture and shape features. Fg.3 Sample output screen for shape and texture feature extracton V. K-MEANS CLUSTERING The K-means algorthm ntalzes 10 clusters by arbtrarly selectng one mage to represent each cluster. Each of the remanng mages s assgned to a cluster and the clusterng crteron s used to calculate the cluster mean. These means are used as the new cluster ponts and each mage s reassgned to the cluster that t s most smlar to. Ths contnues untl there s no longer a change when the clusters are recalculated. The followng fg.4 shows the clusterng of database usng cluster number as three. VI. IMAGE RETRIEVAL The bass of many measures of smlarty and dssmlarty s the Eucldean dstance. The dstance between vectors X and Y s defned as follows: n Dj ( Xv Xv v 1 (15) The Eucldean dstance s calculated between the query mage and the clustered mages. The calculated dstances are sorted n ncreasng order and dsplay the frst N mages as the best smlar MRI scan mages for medcal treatment. VII. EXPERIMENTAL RESULTS The doman of medcal magng as a specfc part of medcne s a very convenent envronment for usng a varety of CBIR systems. The man reason s the necessty of Copyrght to IJIRSET 43

4 ISSN (Onlne) : ISSN (Prnt) : An ISO 397: 007 Certfed Organzaton, Volume 3, Specal Issue 1, February 014 MRI Images TABEL: 1 Performance measure Texture Shape Texture &Shape Precson Recall Precson Recall Precson Recall Bran Spne Knee jont Abdomen Where Rr s the number of relevant retreved mages, T s the total number of relevant tems n an mage database, and Tr s the number of all retreved tems. Ths method s mplemented on a computer system usng JAVA as the programmng language and MS-Access as the back end. In ths work we use around 450 MRI scan mages as a database such as bran, abdomen, knee jont and spnal cord. The sample output screen s shown n Fg. 5. Fg. 4 K-means Clusterng effectve content based analyss, extractng clncally relevant features out of the mage and successful retreval. Ths CBMIR (content based medcal mage retreval) system conssts of four major parts. The frst one s feature extracton, where a set of features s generated to represent the content of each mage n the database.. The second job s usng K-means clusterng for reducng robustness for retrevng mages. Then the fnal and thrd task s to use Eucldean dstance measure, where a dstance between the query mage and each class n the database s computed usng ther mage feature values so that the N most smlar mages whch are belongng to one class can be retreved. The performance of both the shape and texture features, and combnaton of both features are compared and gven n Table 1. To evaluate the retreval effcency of the proposed system, we use the performance measure, Recall and Precson. Recall=R r /T Precson=R r /Tr Fg. 5 Sample output screen for spne MRI mage retreval VIII. CONCLUSION Content based medcal mage classfcaton and retreval based on texture, shape features and Eucldean dstance measure are a feld of dversty and have an enormous scope of applcaton n medcal felds. Ths method s appled around 450 MRI mages such as bran, spne, abdomen, knee jont and retreval effcency s found wth usual precson and recall analyss. We have planned to extend our work to varous parts of the human body MRI scan mages and test ts performance n future. REFERENCES [1] Swapnaln and Bhalke Begnners to Content Based Image Retreval, Internatonal Journal of Scentfc Research Engneerng and Technology, vol.1, 01 Copyrght to IJIRSET 44

5 ISSN (Onlne) : ISSN (Prnt) : An ISO 397: 007 Certfed Organzaton, Volume 3, Specal Issue 1, February 014 [] K.Wanjale, Tejas Borawake and Shahdeep Content Based Image Retreval for Medcal Image Technques and Storage Methods Revew Paper, Internatonal Journals of Computer Applcaton, vol. 1, no.19, 011 [3] Reshma Chauudhar and A.M Patl Content Based Image Retreval Usng Color and Shape Features, Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng, vol.1, 01. [4] Bkesh Kr.Sngh and G.R. Shnha Content Based Retreval of X- ray Images Usng Fuson of Spectral Texture and Shape Descrptor, Internatonal Conference on Advances n Recent Technologes n Communcaton and Computng, vol , 010 [5] Shunren Xa, Werong Mo, and Zanchao Zhang A Content Based Retreval System for Endoscopc Images, Internatonal Journal of Informaton Technology, vol. 11 no.1, 005. [6] B.S Manjunath, Jens-raner ohm, Vnod V. Vasudevan, and Ako yamanda Color and Texture Descrptor, IEEE Transactons on Crcuts and Systems for Vdeo Technology, vol. 11, no.6, 011. [7] Azhar Quddus and Ootman Basr Semantcs Image Retreval n Magnetc Resonance Bran Volumes, IEEE Transactons on Informaton n Bomedcne, vol.16, no.3, 01. [8] Jann-der lee and L-peng Lou Usng Texture and Shape Features to Retreve Sets of Smlar Medcal Images, IEEE Transacton on Bomedcal Engneerng, Applcatons Bass and Communcaton, vol.15 no.5, 003 [9] Paul Bao, Le Zhang, Xaoln Wu Canny Edge Detecton Enhancement by Scale Multplcaton, IEEE Transacton on Pattern Analyss and Machne Intellgence, vol.7, no.9, 005 [10] Reshma Chauudhar and A.M Patl Content Based Image Retreval Usng Color and Shape Features, Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng, vol.1, 01. [11] Arun k. s, and Hema P Menon Content Based Medcal Image Retreval by Combnng Rotaton Invarant Contourlet Features and Fourer Descrptors, Internatonal Journals of Recent Trend n Engneerng, vol., no., 009. [1] Jngyam Wang, Yongpng L Yng Zhang, Chao Wang Honglan Xe, Guolng and Xn gao Bag-of-Features Based Medcal Image Retreval va Multple Assgnment and Vsual Words Weghtng, IEEE Transacton on Medcal Imagng, vol. 30,no.11, 011 [13] Peqang Zhang and Hongguang Zhu Medcal Image Retreval Based on Co-occurrence Matrx and Edge Hstogram, IEEE, vol , 011 [14] Davd A. Claus and Huang Deng Desgn-Based Texture Feature Fuson Usng Gabor Flters and Co-occurrence Probabltes, IEEE, vol , 005. [15] K.Lakshm prya and D.chrstopher Duraraj Integraton of morphologcal segmentaton and Canny edge detecton for rs regonoton, IJCA vol.80-no. oct 013 [16] Hu Xong, Junje Wu and Jan Chen K-means Clusterng verses Valdaton Measure: A data-dstrbuton Prespectve IEEE, vol. 39, no., Aprl 009 [17] Lwe Wang, Yan Zhang and Jufu Feng On the Eucldean Dstance Of the Images, IEEE, vol. 7, no. 8, Aug 005 Copyrght to IJIRSET 45