ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 10, April 2014

Transcription

1 Segmentaton and Analyss of Lung Cancer Images Usng Optmzaton Technque Joel George R, Antha Jeba Kumar D Department of Appled Electroncs, Sr Lakshm Ammaal Insttute of Technology, Chenna, TN, Inda Department of Instrumentaton and Control, St.Joseph s College of Eng, Chenna, TN, Inda Abstract: In ths paper an optmzaton method s proposed to segment the lung cancer mage. The mage s acqured for whch Otsu s thresholdng approach s used and then hstogram equalzaton of the mage s dentfed. It s a computatonal procedure that sort mages nto groups accordng to ther smlartes. Two methods such as Fuzzy K- means clusterng method and Partcle Swarm optmzaton method are mplemented for segmentng an mage. The varous parameters lke PSNR, MSE are calculated and compared. The proposed optmzaton method gves optmal soluton. Keywords Thresholdng based segmentaton, Fuzzy K-means (FKM), PSO. I. INTRODUCTION To detect lung cancer at an early stage s qute dffcult [1] wth the exstng methods of thresholdng [3] and segmentaton. Here the lung CT scan mages are taken for the work. So to get nformaton from the scan mages, t has to be processed usng certan technques lke makng the gray level of the mage to be black and whte whch s done by fxng the thresholdng level. After the mage s done wth thresholdng, the mage s segmented usng a good segmentaton technque that dvdes the mage accordng to the regon of nterest. Medcal mage segmentaton s a vtal component of a large number of applcatons such as to study anatomcal structure, dentfy regons of nterest e., locate tumor leson, measure tssue volume to assess growth or decrease n the sze of tumor, helps n treatment plannng pror to radaton therapy and n radaton dose calculaton. In segmentaton process, contour detecton s stll a challengng problem n medcal magng because contour delneaton error above 10% may lead to an unacceptable rsk to rradate healthy tssues nstead of affected ones. So a method based on computatonal ntellgence s proposed to deploy a more effectve segmentaton procedure. In ths work, the theory of fuzzy K means s mplemented as t s an nterestng and useful tool that provdes a good theoretcal bass to represent mprecson of the nformaton. The Fuzzy K-Means method s desgned to classfy the mages. Another optmzaton algorthm named PSO s used for makng the segmentaton more successful. II. METHODOLOGY Ths system s fully mplemented (n matlab) and tested wth real CT scan mages. [6] A. Image Acquston An attempt has been made to collect few lung cancer mages from a prvate hosptal (APOLLO SPECIALITY HOSPITALS, CHENNAI). The dgtzed nformaton s stored n the DICOM format wth a resoluton of 8 bts per plane. CT mages have low nose, better clarty and less dstorton so t s been taken for studyng the segmentaton methods. The Peak Sgnal to Nose Rato (PSNR) values and Mean Squared Error (MSE) Values are calculated for the varous mages processed usng dfferent segmentaton methods. K- Means algorthm Image Acquston Image Preprocessng Feature Extracton Segmentaton Process PSO Method Fg 1. Methodology of work B. Preprocessng of Image The qualty of the mage s affected by dfferent artfacts lke non- unform ntensty, varatons n moton, shft and nose. So the mage s processed by certan methods lke thresholdng, hstogram equalzaton etc., to remove redundancy present n the scanned mages wthout affectng the features of the mage. In ths work thresholdng method s used for preprocessng. Thresholdng s a non-lnear operaton that converts a gray-scale mage nto a bnary mage where the two levels are assgned to pxels that are below or above the specfed threshold value. Here Otsu s method [9] s used (gray thresh) for computng global mage threshold. Otsu s method s based on threshold selecton by statstcal crtera. Otsu suggested mnmzng the weghted sum of wthn-class varances of the object and background pxels to establsh an optmum threshold. Recall that mnmzaton of wthn-class varances s equvalent to maxmzaton of 191

2 between-class varance. Ths method gves satsfactory results for bmodal hstogram mages. Threshold value based on ths method wll be between 0 and 1, after acheve ths value we can segment an mage based on t. an n dmensonal space, n beng the number of all features used to descrbe the objects to cluster. The algorthm then randomly chooses k ponts n that vector space, these ponts serve as the ntal centers of the clusters. Afterwards all objects are each assgned to center they are closest to. Usually the dstance measure s chosen by the user and determned by the learnng task. After that, for each cluster a new center s computed by averagng the feature vectors of all objects assgned to t. The process of assgnng objects and recomputng centers s repeated untl the process converges. The algorthm can be proven to converge after a fnte number of teratons. Several tweaks concernng dstance measure, ntal center choce and computaton of new average centers have been explored, as well as the estmaton of the number of clusters k. Yet the man prncple always remans the same do that for you. Fg 2 Otsu s Gray threshold method on CT scan mage C. Feature Extracton The varous features of the mage s extracted usng dfferent technques lke bnarzaton [6],[10] and Gray Level Co-Occurrence Matrx (GLCM) [4] where both methods are based on facts that strongly related to lung anatomy and nformaton of lung CT magng. The features are extracted to detect and solate varous desred portons or shapes (features) of the mage. D. Gray Level Co-Occurrence Matrx Method GLCM s a tabulaton of how often dfferent combnaton of pxel brghtness value (gray level) occur n an mage. Here the matrx s formed from the mage usng gray co-matrx functon n MATLAB. Then the matrx s normalze d usng the followng formula V, j P, j N 1 V, j, j 0 Where, s the row number and j s the column number. From ths we calculate texture measures from the GLCM. The followng features are extracted usng ths method Contrast Correlaton Energy Homogenety III. K-MEANS ALGORITHM K-Means [5] s a rather smple but well known algorthm for groupng objects, clusterng. The K-Means method s numercal, unsupervsed, non-determnstc and teratve Agan all objects need to be represented as a set of numercal features. In addton the user has to specfy the number of groups (referred to as k) he wshes to dentfy. Each object can be thought of as beng represented by some feature vector n A. K-means algorthm propertes There are always K clusters. There s always at least one tem n each cluster. The clusters are non-herarchcal and they do not overlap. Every member of a cluster s closer to ts cluster than any other cluster because closeness does not always nvolve the 'center' of clusters. B. K-means algorthm process The dataset s parttoned nto K clusters and the data ponts are randomly assgned to the clusters resultng n clusters that have roughly the same number of data ponts. For each data pont: Calculate the dstance from the data pont to each cluster. If the data pont s closest to ts own cluster, leave t where t s. If the data pont s not closest to ts own cluster, move t nto the closest cluster. Repeat the above step untl a complete pass Through all the data ponts results n no data pont movng from one cluster to another. At ths pont the clusters are stable and the clusterng process ends. The choce of ntal partton can greatly affect the fnal clusters that result, n terms of nter-cluster and ntra cluster dstances and coheson. Fg 3 Orgnal Image 192

3 updated usng equaton (4) [13] and the poston of partcle s updated usng equaton (5) [14] v (t+1) = w v (t) + c 1 r 1 (p (t) x (t)) + c 2 r 2 (gbest x (t)) (4) x (t+1) = x (t) + v (t+1) (5) In the formula, w s the nerta weght [16], c1 and c2 are the acceleraton constants, r1 and r2 are random numbers n the range [0,1] and V must be n the range [-Vmax, Vmax], where Vmax s the maxmum velocty. Fg 4 Segmented mage usng K- means algorthm IV. PARTICLE SWARM OPTIMIZATION METHOD Here, a multlevel thresholdng method segmentng mages based on partcle swarm optmzaton (PSO) s proposed [7]. In the proposed method, the thresholdng problem s treated as an optmzaton problem, and solved by usng the prncple of PSO. The algorthm of PSO s used to fnd the best values of thresholds that can gve us an approprate partton for a target mage accordng to a ftness functon. The proposed method has been tested on dfferent mages, and the expermental results demonstrate ts effectveness. Partcle swarm optmzaton (PSO) [7] s a populaton-based optmzaton algorthm modeled after the smulaton of socal behavor of brds n a flock [11], [12]. The algorthm of PSO s ntalzed wth a group of random partcles and then searches for optma by updatng generatons. Each partcle s flown through the search space havng ts poston adjusted based on ts dstance from ts own personal best poston and the dstance from the best partcle of the swarm. The performance of each partcle,.e. how close the partcle s from the global optmum, s measured usng a ftness functon whch depends on the optmzaton problem. Each partcle,, fles through an n dmensonal search space, Rn, and mantans the followng nformaton: x, the current poston of th partcle ( x - vector ), p, the personal best poston of th partcle ( p - vector ), and v, the current velocty of th partcle (v - vector ). The personal best poston assocated wth a partcle,, s the best poston that the partcle has vsted so far. If f denotes the ftness functon, then the personal best of at a tme step t s updated as: P t 1 P t ff ( x t 1 f ( P( t)) X( t 1)ff ( X( t 1) f ( P ( t 1) If the poston of the global best partcle s denoted by gbest, then: gbest { p1( t ), p2 ( t ),..., pm(t) } = mn{ f (p1(t)), f (p2(t)),..., f (pm(t)) } (3) The velocty updates are calculated as a lnear combnaton of poston and velocty vectors. Thus, the velocty of partcle s (1) (2) Fg 5 Segmented mage usng PSO Algorthm The values chosen for PSO processng n=256; c1=2.1; c2=2.1; wmax=0.8; wmn=0.4; G=30; M=2; Where,n=no.of pxels, M = centrods V. RESULTS AND DISCUSSION The GLCM method s mplemented on the sample mage and the varous values are tabulated below Parameters Mn Max Contrast Correlaton Energy Homogenety Table 1 Feature extracted usng GLCM method The obtaned values are used for further segmentaton of the mages usng K- means algorthm. Addtonal to these values the PSNR value and MSE value s calculated usng the formula [17] PSNR = 10 log 10 [R 2 /MSE] (6) MSE = 1 ( m,n ) I2( m,n ) I 2 (7) M,N The PSNR value and MSE for the orgnal mage s gven below Image PSNR MSE Orgnal Image Table 2 PSNR and MSE for orgnal mage Now the mage s segmented usng the two methods, K- Means Algorthm and PSO algorthm. The PSNR and MSE values are calculated usng Equaton (6) & (7) for the segmented mage and the values are tabulated below 193

4 Name of the PSO K-Means Patent PSNR MSE PSNR MSE Sample Sample Sample Sample Sample Sample Sample Sample In general, PSNR must ncrease and MSE must decrease for a good segmented mage, so here the PSNR and MSE values are compared between orgnal mage and the segmented mage. Here the segmentaton done wth PSO algorthm proves to be optmal soluton when compared to that segmentaton done wth K- Means Algorthm. VI. CONCLUSION In ths work, the CT mages are acqured and the seres of operatons are performed to enhance the mage qualty. Here the mage s frst converted to gray scale mage by the otsu s thresholdng method. Then the segmentaton of the mage s done by two methods such as K means algorthm and PSO algorthm [11]. The PSNR value and MSE value s calculated for both the segmented mages and compared. Here the PSO method s proved to best n obtanng the PSNR and MSE value. Ths process can be mproved by mplementng any other evaluaton algorthm to obtan the best segmentaton of the mage. VII. ACKNOWLEDGEMENT The author would lke to thank Apollo Specalty Hosptals, Chenna for provdng the lung CT scan mages. REFERENCES [1] Yongjun WU, Na Wang, Hongsheng ZHANG, Ljuan Qn, Zhen YAN, Ymng WU, Applcaton of Artfcal Neural Networks n the Dagnoss of Lung Cancer by Computed Tomography, 2010 Sxth Internatonal Conference on Natural Computaton (ICNC 2010). [2] M. Gomath and P. Thangaraj, A Computer Aded Dagnoss System for Lung Cancer Detecton \Usng Support Vector Machne, Amercan Journal of Appled Scences 7 (12): , 2010 ISSN [3] Mokhled S. AL-TARAWNEH, Lung Cancer Detecton Usng Image Processng Technques, Leonardo Electronc Journal of Practces and Technologes ISSN 20, January-June 2012 p [4] P. Mohanaah, P. Sathyanarayana, L. Guru Kumar, Image Texture Feature Extracton usng GLCM Approach, Internatonal Journal of Scentfc and Research Publcatons, Volume 3, Issue 5, May ISSN [5] Almas Pathan, Baru.K.saptalkar, Detecton and Classfcaton of Lung Cancer Usng Artfcal Neural Network, Internatonal Journal on Advanced Computer Engneerng and Communcaton Technology Vol-1 Issue: 1: ISSN [6] Ada, Rajneet Kaur, Feature Extracton and Prncpal Component Analyss for Lung Cancer Detecton n CT Scan Images, Internatonal Journal of Advanced Research n Computer Scence and Software Engneerng Volume 3, Issue 3, March 2013 ISSN: X. [7] Fahd M. A. Mohsen, Mohy M. Hadhoud, Khald Amn, A new Optmzaton-Based Image Segmentaton method By Partcle Swarm Optmzaton, (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, Specal Issue on Image Processng and Analyss. [8] Dsha Sharma, Gagandeep Jndal, Identfyng Lung Cancer Usng Image Processng Technques, Internatonal Conference on Computatonal Technques and Artfcal Intellgence (ICCTAI'2011). [9] Anta Chaudhary, Sont Sukhraj Sngh, Lung cancer Detecton usng Dgtal Image Processng, Internatonal Journal of Research n Engneerng & Appled Scences(IJREAS), Volume 2, Issue 2, (Feb 2012),ISSN: [10] Jose, Enrque, Francsco, Antono, Paula, Gsela, Marfa, Femat, Moses, Development of An optmzed multbomarker panel for the detecton of lung cancer based on prncpal component analyss and artfcal neural network modelng, ELSEVIER Expert Systems wth Applcatons 39 (2012) [11] J. Marcello, F. Marques and F. Eugeno, "Evaluaton of thresholdng technques appled to oceanographc remote sensng magery," SPIE, 5573, pp , [12] J. Kennedy, and R. Eberhart, Swarm Intellgence, San Francsco: Morgan Kaufmann Publshers, 2001 [13] R. O. Duda and P. E. Hart, Pattern Classfcaton and Scene Analyss, John Wley & Sons, New-York, [14] A. A. Younes, I. Truck, and H. Akdaj, "Color Image Proflng Usng Fuzzy Sets," Turk J Elec. Engn., Vol.13, No.3, [15] Fatma Taher1,*, Naoufel Wergh1, Hussan Al-Ahmad1, Rachd Sammouda Lung Cancer Detecton by Usng Artfcal Neural Network and Fuzzy Clusterng Methods Amercan Journal of Bomedcal Engneerng 2012, 2(3): DOI: /j.ajbe [16] A-Qn Mu, De-Xn Cao, Xao-Hua Wang, A Modfed Partcle Swarm Optmzaton Algorthm Vol.1, No.2, (2009) [17] Nvedtta Thakur, Swapna Dev A New Method for Color Image Qualty Assessment. Internatonal Journal of Computer Applcatons ( ) Volume 15 No.2, February

5 AUHTOR S PROFILE Joel George R s a PG student n the Department of Apled Electroncs n Sr Lakshm Ammaal Insttute of Technology, Anna Unversty afflated. He has completed hs UG n Electroncs and Communcaton n Karunya Insttute of Technology, Combatore. Antha Jeba Kumar D has completed her PG n Medcal Electroncs, Anna Unversty, CEG campus and UG n Department of Instrumentaton and Control engneerng n Sr Saram Engneerng College. She s currently workng as Assstant Professor n St. Joseph s College of Engneerng, Chenna. 195