Lesion Area Detection (LAD) using Superpixel Segmentation

Transcription

1 Indan Journal of Scence and Technology, Vol 8(15), DOI: /jst/2015/v815/74558, July 2015 ISSN (Prnt) : ISSN (Onlne) : Leson Area Detecton (LAD) usng Superpxel Segmentaton K. H. Hemalatha *, G. Babu and R. Svakumar Appled Electroncs, R. M. K. Engneerng College, Gummdpoond, Truvallur , Taml Nadu, Inda; hemadr7@gmal.com Abstract Nowadays, n bomedcal feld, magng technques are used to dscover the abnormaltes n the partcular regon. Leson s an area n an organ or tssue, whch has suffered from damage through njury or dsease, such as a wound, ulcer, abscess, or tumor. Leson area of the tssue s detected usng source mages. Wreless capsule endoscopy mage of the gastrontestnal tract s used to fnd the leson. Preprocessng of mage s done usng denosng by multdmensonal flter. Denosed mage s edge detected durng whch the edge ponts n an mage dfferentaton s hghlghted by Sobel edge detecton. The value of each pxel n the edge detected mage s evaluated based on the comparson of correspondng pxel n the nput mage wth ts neghborng pxels by the morphologcal operaton. Eroson and dlaton operaton erodes and adds pxels respectvely to the edge detected mage, so that the value of the output pxel has the maxmum pxel value. Durng segmentaton, the pxels are grouped usng superpxel algorthm to reduce the complexty whle mantanng hgh dagnostc accuracy. As a fnal pont, the Correlaton coeffcent test fnds the spoled area between healthy tssue and abnormal tssue by color encodng. Ths ntends to dentfy the suspected abnormal areas (leson) from the source mages. Keywords: Correlaton Coeffcent, Leson Area, Morphologcal Operaton, Multdmensonal Flter, Sobel Edge Detecton, Superpxel Segmentaton 1. Introducton In the feld of Bomedcal and Health Informatcs, Detecton of lesons s always problematc due to smlar ntensty between lesons and normal tssues. Lesons can occur anywhere n the body that conssts of soft tssue found n the mouth, skn, ntestnal tract and the bran. Lesons can also be caused by metabolc processes, lke an ulcer, cancer, or autommune actvty. Many researchers used dfferent technques to detect the leson area n the targeted tssue. Manual detecton of leson area s slow and dffcult; therefore automatc multple scleross leson detecton s used to fnd the multple leson n the targeted regon. A fast and accurate method for evaluatng the sze of MS lesons n the damaged regon s used to dagnose the dsease and helps the doctors n treatment decson. Due to the food practce and emotonal stress, ulcer n stomach s the emergng problem nowadays. These ulcers are developed from the lesons whch occur n the soft tssues of the stomach. An endoscopy s consdered the best procedure for dagnosng dgestve ulcers, Wreless Capsule endoscopy mages shows the clear vew of the gastrontestnal tract as n Fgure 1. The lesons and ulcers developed n the ntestnal regon should be detected usng the scanned mage before t leads to cancer. Early detecton of leson helps n further treatment. 2. Scope of the Research In medcal feld computatonal tme s the most mportant parameter for analyss of any dseases and ther treatment. Detecton of leson area reduces computatonal tme for *Author for correspondence

2 Leson Area Detecton (LAD) usng Superpxel Segmentaton dffcult to flter. Hence the sgnal subspace dmenson s reduced as well as sgnal to nose rato s enhanced. Fgure 1. Wreless capsule endoscope mage of ntestne. clncal dagnoss. In large datasets, manual leson detecton s requred and t takes more tme to dagnose the abnormalty. Many technques have been developed for the dentfcaton of nfected regon n terms of voxels segmentaton, but stll t needs an mprovement n terms of accuracy and computatonal complexty. Hence LAD usng superpxel segmentaton and Pearson s correlaton coeffcent detect the leson area automatcally and reduces the computatonal complexty. 3. Related Works 3.1 Pre-processng Preprocessng of mages commonly nvolves removal of low frequency nose, removng reflectons of mages, normalzng the ntensty of the ndvdual voxels of the mage, and maskng portons of mages. Therefore nput mage must be preprocessed. All medcal mages contan some form of nose due to envronment, where the scan s taken. Ths nose should be removed usng flters based on the nature of the nose present n the nput scan mage. The goal of mage denosng s to remove the nose whle retanng the mportant mage features lke edges, detals as much as possble. Denosng of mage ncludes smoothenng of mages; reduce nose, and preservng edges. A Guded multdmensonal flter must be used to perform smoothng, reducng and preservng edges of the mage wthout the loss of orgnal nformaton 13. Medcal mage contans the sgnal subspace and the nose subspace s very close such that all the useful nformaton s dffcult to extract. Ths leads to artfacts and loss of spatal resoluton n the restored HIS 2. Multdmensonal Flterng s used to denose the ntestnal mage where sgnal subspace and nose subspace are 3.2 Edge Detecton The edge detecton process serves to smplfy the analyss of mages by drastcally reducng the amount of data to be processed, whle at the same tme preservng useful structural nformaton about object boundares 5. Edge detecton s used to vew, partcularly n the areas of feature detecton and extracton. The am of the edge detecton s to dentfy ponts n a dgtal mage n whch mage brghtness that changes sharply or, more formally, the ponts where dscontnutes are present. The ponts at whch mage brghtness changes sharply are typcally organzed nto a set of curved lne segments termed edges. The edge detecton operaton s performed by formng a matrx centered on a pxel chosen as the center of the matrx area. If ths matrx area value s hgher than a gven threshold value, then the center pxel s regarded to be as an edge. Gradent based edge detectors are Sobel, Canny and Prewtt operators. The gradent based algorthms have kernel operators that calculate the strength of the slope n drectons whch are orthogonal to each other, generally vertcal and horzontal. Afterward the dverse components of the slopes are combned to gve the total value of the edge strength. In Intestnal area the leson may found n all the edges, n these case canny edge detector cannot be used. Leson detecton conssts of hgh ntensty mages, Sobel operator s used to detect the edges n ths case. The Sobel operator performs a multdmensonal spatal gradent measurement 3 on an mage. It s used to fnd the approxmate absolute gradent magntude at each pont n an nput grayscale mage. The operator conssts of a par of 3 3 convoluton masks s shown n Fgure 2, and Sobel convoluton masks are shown n Fgure 3, The detector uses the masks to compute the frst order dervatves G x and G y, s gven n Equaton 1(a) and 1(b), G x =Z 7 +2Z 8 +Z 9 G y =Z 1 +2Z 2 +Z 3 Fgure 2. Z 1 Z 2 Z 3 Z 4 Z 5 Z 6 Z 7 Z 8 Z 9 Image neghborhood of Sobel operator. 1(a) 1(b) 2 Vol 8 (15) July Indan Journal of Scence and Technology

3 K. H. Hemalatha, G. Babu and R. Svakumar G x Fgure G y Sobel convoluton masks. The advantage of Sobel operator s ntutveness and easness. The Sobel edge detecton s used for edge detecton and fndng drectons of gradent magntude as the approxmaton of gradent magntude s easy. Hence Sobel edge detecton detects the edges of the gray converted mage and the drecton of gradent magntude s found n the ntestnal mage. 3.3 Morphologcal Processng Morphologcal processng s an operaton that process mages based on shapes. Morphologcal operaton s one n whch the value of each pxel n the output mage s based on a comparson of the correspondng pxel n the nput mage wth ts neghborng pxel. The most mportant morphologcal operatons are dlaton and eroson. Dlaton adds pxels to the boundares of objects n an mage, whereas eroson removes pxels on object boundares. Enhancement of mages wth poor contrast and detecton of background s done usng morphologcal transformatons 14. Image enhancement has been carred out by the two methods. Informaton from mage background analyss by blocks s used by the frst method employs, whereas the second transformaton method utlzes the openng and closng operaton, whch s engaged to defne the mult background grayscale mages. The basc effect of the eroson operator on a bnary mage s to erode away the boundares of regons of foreground pxels (.e. whte pxels, typcally). Thus areas of foreground pxels get smaller n sze, and holes wthn those areas become bgger. The basc effect of the dlaton operator on a bnary mage s to gradually enlarge the boundares of regons of foreground pxels (.e. whte pxels, typcally). Thus areas of foreground pxels grow n sze whle holes wthn those regons become smaller. In a bnary mage, f any of the pxels s set to the value 1, the output pxel s set to 1 based on the correlaton of eroded and dlated mage. The purpose of the morphologcal operators s to separate the abnormal part of the mage. The separated part of the mage has the hghest ntensty than other regons of the mage. Perform a morphologcal openng on each segment subtract the openng from the orgnal segment to obtan regons to be reassgned to neghborng segments of the ntestnal mage. 3.4 Superpxel Segmentaton Segmentaton s the process of parttonng an mage nto dssmlar segments. These segments correspond to varous tssues, pathologes, or other bologcally sgnfcant structures n the feld of medcal magng. Medcal mage segmentaton s made ntrcate by nose, and other magng ambgutes. Even though preprocessng technque by mage segmentaton were exsts 11, some are beng used dstnctvely for medcal mage computng. Detecton of the correspondng area n mage by Thresholdng s the smplest method. Thresholdng s the smplest non contextual segmentaton technque 1. Wth a sngle threshold, t transforms a grayscale or color mage nto a bnary mage consdered as a bnary regon map. Ths map contans two probably dsjont regons, one regon contanng pxels wth nput data values smaller than a threshold and another relatng to the nput values that are at or above the threshold. Pxel and encompassng features whch are the basc data unt of mage representaton form the key for the qualtatve and quanttatve output of any mage processng step. The result exhbtng smlar features wthn local or global neghborhood s termed as Superpxel. The job of groupng pxels nto super pxels by comparng the values of each and every neghborhood pxel wthn specfc connectvty, defnng the boundary strength of superpxel, and the mdpont of superpxel called the seed pont 10. The complexty of mages from hundreds of thousands of pxels to only a few hundred super pxels reduces the computatonal cost and ncreases the mage processng speed. Superpxel based mage segmentaton technques apples many sophstcated algorthms for fast 2D mage segmentaton usng SLIC 9. Superpxels are unversally defned as constrctng and groupng unform pxels n the mage, whch are wdely used n mage segmentaton and object recognton. In addton to the above performance, clusterng technques comparson gves extraordnary results 8. The superpxel map s natural and perceptually meanngful representaton of the nput mage. For that reason, the superpxel representaton greatly reduces the number of mage prmtves and mproves the representatve effcency when compared to the tradtonal pxel representaton of the mage. The con- Vol 8 (15) July Indan Journal of Scence and Technology 3

4 Leson Area Detecton (LAD) usng Superpxel Segmentaton venence and effectveness of superpxels to compute the regon based vsual features provdes mportant benefts for the vson tasks such as object recognton. Groupng of pxels through superpxel segmentaton s to reduce the computatonal complexty whle mantanng hgh dagnostc accuracy. Red rato n RGB space extracts the feature of each superpxel whch s then fed nto support vector machne for classfcaton. To reduce the computatonal cost and make abnormal tssue detecton faster, group the pxels based on color and locaton frst, and the detect abnormaltes at superpxel level. Gray mage s converted to color mage n superpxel segmentaton to mantan the ntensty. The relatve dfference between any two colors whch s approxmated as ponts n 3-D space 15 wth three components (l, a, b) s obtaned usng the Eucldean dstance between them S color = ( l l ) + ( a a ) + b b Smlarty of any two pxels depends not only on color smlarty but also on spatal dstance as S spatal = Therefore the smlarty measure can be obtaned by S = S color +λ S spatal (4) Where S color shown n Equaton (2) s the color smlarty, S spatal shown n Equaton (3) represent the spatal dstance between two pxels and λ s the balancng factor between spatal dstance and color smlarty, shown n Equaton (4). Intestnal mage s segmented usng superpxel, hence the exact geometry of the leson s segmented usng groupng of pxels. The leson s found based on the nconsstency of spatal locaton from the segmented part 12. The segmented regon of the ntestnal mage shows the pxels where the correlaton test has to be performed. 3.5 Correlaton Coeffcent j j j ( x x ) + y y 2 2 j j (2) (3) The correlaton coeffcent matrx represented n statstcal that the normalzed measure of the strength of lnear relatonshp between varables 7. The correlaton coeffcent r X, Y between two random varables X and Y wth expected values μ x and μ y and standard devatons σ X and σ Y s ther covarance normalzed by ther standard devatons, s gven n Equaton 5, (( y) ) cov(x, Y) E X µ x Y µ r X,Y = = σ σ σ σ X Y where E s the expected value operator and cov means covarance. Snce μ x = E(X), σ x = E(X 2 ) E 2 (X), and lkewse for Y, r X,Y s gven n Equaton 6, rx,y = E( X,Y) In statstcs, the Pearson product moment correlaton coeffcent s a measure of the lnear correlaton (dependence) between two varables X and Y, gvng a value between +1 and 1, where 1 denote total postve correlaton, 0 s no correlaton, and 1 s total negatve correlaton. Ths concept s wdely used n the scences as a measure of the degree of lnear dependence lnkng two varables 6. Pearson s correlaton coeffcent between two varables s defned as the covarance of the two varables dvded by the product of ther standard devatons. Ths form of the descrpton nvolves a product moment, that s, the mean (the frst moment about the orgn) of the product of the mean adjusted random varables; hence the modfer product moment n the name. Pearson s correlaton coeffcent can be defned as the covarance of two groups of numbers dvded by the product of ther standard devatons 4 s gven n Equaton 7, cov(x, Y) r = = σ X σ Y where r s the Pearson s correlaton coeffcent, x s the th element n X, and x s the mean of group X. In the numerator, the raw scores are centered by subtractng out the mean of each varable, and the sum of cross products of the centered varables s accumulated. The denomnator adjusts the scales of the varables to have equal unts. Thus Equaton descrbes r as the centered and standardzed sum of cross product of two varables. Usng the Cauchy Schwartz nequalty, t can be shown that the absolute value of the numerator s less than or equal to the denomnator therefore, the lmts of ±1 are establshed for r. Correlaton technque of the proposed system uses the Pearson s correlaton coeffcent that detects the smlarty between the healthy tssue and abnormal tssue by color encodng and fnds the leson. X E( X) E( Y) E( X )-E ( X) E( Y )-E ( Y) Y (5) (6) ( x x) y y 2 2 (7) ( x x) ( y y) 4 Vol 8 (15) July Indan Journal of Scence and Technology

5 K. H. Hemalatha, G. Babu and R. Svakumar 4. Proposed Methodology The major functonal blocks of our proposed method shown n Fgure 4, explans the proposed system of leson area detecton. Ths Methodology helps n detecton of leson usng superpxel segmentaton; ntestnal mage s segmented by superpxel segmentaton, ths method ncludes the followng steps of work: Step 1: Wreless Capsule endoscope mage s converted nto M fle. Step 2: Read the nput mage nto the GUI. Step 3: Apply multdmensonal flter to the nput mage. Step 4: Denosed mage s restored. Step 5: Denosed mage s converted nto green ntensty mage to separate the layers of the mage and the damaged regon s examned closely usng ths green ntensty mage. Step 6: After the step 6, green ntensty mage s converted to grey scale mage usng grey converson. Lumnosty method s used for converson of green ntensty mage to Gray mage, for human percepton as shown n Equaton 8, 0.21 R G B (8) Step 7: Edge detecton s used for feature detecton and feature extracton. Sobel operator s used for edge detecton usng Sobel convoluton matrx. Step 8: Morphologcal operaton usng both eroson and dlaton s used to enhance the processed mage. The value of each pxel n the output mage s compared wth the correspondng pxel of nput mage wth ts neghbors. Step 9: The enhanced mage after the step 8 s segmented usng superpxel segmentaton whch groups the dentcal pxels usng Eucldean dstance and helps to detect the exact nfected regon n the mage shown n Equaton 2, Pxels are grouped based on the smlarty of pxel color and spatal dstance. S color = Step 10: Segmented mage s color encoded and usng correlaton coeffcent leson s detected usng Equaton 7, cov(x, Y) r = = σ X σ Y where r s the Pearson s correlaton coeffcent, x s the th element n X, and x s the mean of group X. Correlated value dfferentates the normal tssue and nfected tssue by color encodng. 5. Results and Dscusson Leson Area Detecton (LAD) usng superpxel segmentaton and the Pearson s correlaton coeffcent s mplemented n GUI. Fgure 1 shows the ntestnal nput mage s processed usng Denosng, Layer separaton, Gray converson, Edge detecton, Morphologcal operaton, Superpxel segmentaton and Pearson s correlaton coeffcent. Leson s marked as pxels n the last block of Fgure Concluson ( l l ) + ( a a ) + b b j j j ( x x) y y ( x x) y y The obtaned result shows that the detecton of leson n ntestnal tract. The supportve systems manly am at reducng the tme spent at detectng endoscopc mages. The fact of good pxel packng and unformty n sze of 2 2 Fgure 4. Block dagram of leson detecton. Fgure 5. Output showng Leson Area Detecton n ntestnal mage. Vol 8 (15) July Indan Journal of Scence and Technology 5

6 Leson Area Detecton (LAD) usng Superpxel Segmentaton pxels n determned to have better performance than the other pxel based algorthm. Pearson s correlaton coeffcent can detect very small sze lesons, wth more accuracy whch helps n tme consumpton n clncal dagnoss. Ths work may extended by usng Spearman s correlaton coeffcent. Three statstcal test can be performed by combnng both Pearson s and Spearman s coeffcents to get better results for dagnostcans and researchers. 7. References 1. Mustaqeem A, Javed A, Fatma T. An effcent bran tumour detecton algorthm usng watershed & thresholdng based segmentaton. IJIGSP. 2012; 10: Letexer D, Bourennane S. Nose removal from hyperspectral mages by multdmensonal flterng. IEEE Transactons on Geoscences and Remote Sensng. 2008; 46(7): Aybar E. Sobel edge detecton method for MATLAB. Anadolu Unversty, Porsuk Vocatonal School; p Zhu F, Gonzalez DR, Carpenter T, Atknson M, Wardlaw J. Leson area detecton usng source mage correlaton coeffcent for CT perfuson magng. IEEE Journal of Bomedcal and Health Informatcs. 2013; 17(5): Canny J. A computatonal approach to edge detecton. IEEE Transactons on Pattern Analyss and Machne Intellgence.1986; PAMI-8(6): Rodgers JL, Ncewander WA. Thrteen ways to look at the correlaton coeffcent. The Amercan Statstcan. 1988; 42(1): Myers JL, Well AD. Research desgn and statstcal analyss. vol p Achanta R, Shaj A, Smth K, Lucch A, Fua P, Suesstrunk S. SLIC superpxels compared to state-of-the-art superpxel methods. IEEE Transactons on Pattern Analyss and Machne Intellgence. 2012; 34(11): Achanta R, Shaj A, Smth K, Lucch A, Fua P, Susstrunk S. SLIC superpxels. EPFL Techncal Report. 2010; : Ranjtham S, Padmavath K. Super pxel based colour mage segmentaton technques: a revew. Internatonal Journal of Advanced Research n Computer Scence and Software Engneerng. 2014; 4(9): Datta S, Chakraborty M. Bran tumour detecton from preprocessed MR mages usng segmentaton technques. IJCA Specal Issue on 2nd Natonal Conference Computng, Communcaton and Sensor Network; p Shen S, Szametat AJ, Sterr Annette. Detecton of nfarct lesons from sngle MRI modalty usng nconsstency between voxel ntensty and spatal locaton - A 3D automatc approach. TITB R ; Kumar S. Image denosng based on Gaussan/Blateral flter and ts method nose thresholdng. Sgnal Image and Vdeo Processng. 2012; do: /s Sreedhar K, Panlal B. Enhancement of mages usng morphologcal transformatons. IJCSIT. 2012; 4(2): Fu Y, Zhang W, Mandal M, Max Q, Meng H. Computer aded bleedng detecton n WCE vdeo. IEEE Journal of Bomedcal and Health Informatcs. 2014; 18(2): Vol 8 (15) July Indan Journal of Scence and Technology