MICROARRAY IMAGE GRIDDING USING GRID LINE REFINEMENT TECHNIQUE

Transcription

1 DOI: /ijivp MICROARRAY IMAGE GRIDDING USING GRID LINE REFINEMENT TECHNIQUE V.G. Biju 1 an P. Mythili 2 School of Engineering, Cochin University of Science an Technology, Inia 1 bvgpillai@gmail.com, 2 mythili@cusat.ac.in Abstract An important stage in microarray image analysis is griing. Microarray image griing is one to locate sub arrays in a microarray image an fin co-orinates of spots within each sub array. For accurate ientification of spots, most of the propose griing methos require human intervention. In this paper a fully automatic griing metho which enhances spot intensity in the preprocessing step as per a histogram base threshol metho is use. The griing step fins co-orinates of spots from horizontal an vertical profile of the image. To correct errors ue to the gri line placement, a gri line refinement technique is propose. The algorithm is applie on ifferent image atabases an results are compare base on spot etection accuracy an time. An average spot etection accuracy of 95.06% epicts the propose metho s flexibility an accuracy in fining the spot co-orinates for ifferent atabase images. Keywors: cdna Microarray, Griing, Image Processing, Spot Detection, Gri Line Refinement 1. INTRODUCTION Complementary DNA (cdna) microarrays are part of a rapily eveloping technology in molecular biology, allowing one to measure simultaneously the activity of thousans of biomolecules in the cell uner ifferent experimental conitions. Novelty in the fiel of biotechnology allows monitoring of expression levels of thousans of genes simultaneously. Microarray image is a powerful tool in many biotechnological applications such as cancer research, chromosomal abnormalities, artery iseases, rug iscovery an isease iagnosis. Enormous improvement of technology in last ecae makes it possible to simultaneously ientify an quantify thousans of genes by their gene expression [1]-[4]. Analysis of cdna microarray images inclue mainly three steps namely, griing, segmentation an intensity extraction. The griing process is usually ivie into two main steps, sub array griing an spot etection. Most of the existing griing algorithms are semiautomatic requiring human intervention an parameter presetting. Each spot is associate with a gene which contains the pixels that inicate the level of expression of that particular gene. Improper griing will affect image analysis in extracting intensity values from each spot that represents gene expression level. Over the past ecae several software packages such as ScanAlyze [5], GenePix Instruments [6], Quant Array [7], Spot Finer [8], ImaGene [9] an Dapple [10] were evelope. For most of these packages parameters nee to be preset manually an sometimes human intervention was necessary to locate the exact spot centers. Jung an Cho [11] propose a graph base gri approach for griing of microarray images. A rawback of this algorithm was that it requires input parameters such as number of rows an columns of sub arrays an spots within each sub array in an image. Deng an Duan [12] propose an axis projection base approach for griing an it requires user intervention in orer to manually ajust the gri location. A variety of methoologies have been propose to solve rotation an misalignment problems [13], [14]. Branle et al. [15] utilize iscrete Raon transform for this purpose. Zacharia an Maroulis [16] propose a Genetic algorithm (GA) base metho which accurately etermines line segments, constituting borers between ajacent blocks or spots. It oes not require any input parameters or human intervention but was time consuming. L. Ruea an I. Rezaeian [17] propose a fully automatic approach for microarray griing. The metho first fins location of sub arrays of the entire image an then fins coorinates of spots in each sub arrays. The metho etects an corrects rotation in the image by applying affine transform. Gui-Fang S et al. [18] presente a fully automatic griing technique base on improve Otsu threshol metho for locating each spot, co-orinates in the microarray image. A preprocessing algorithm was applie for noise reuction an then griing algorithm was applie. In orer to reuce the error after the initial gri line placement, the gri lines were optimize by a heuristic technique with the help of estimating the istribution of the spots. In this paper a fully automatic griing algorithm which improves spot etection accuracy an computational efficiency irrespective of microarray image atabase is propose. The metho first etects an corrects image tilt an then enhances image spot intensity in the preprocessing step using a histogram base threshol. Griing step fins co-orinates of spots from the horizontal an vertical profile of the image. The propose gri line refinement technique further improves griing. 2. THE PROPOSED METHOD Microarray image analysis involves mainly three steps namely Griing, Segmentation Intensity extraction an Quantification. The propose metho eals with microarray image griing. Fig.1 shows the block iagram of propose metho. The metho first checks an corrects tilt in the image. This process is important because the tilt in the image makes the whole griing process ineffective. The secon step preprocessing, helps to perform griing process effectively. The thir step griing fins co-orinates of sub arrays in an image an also spot co-orinates within the sub array. Finally, gri line refinement metho, corrects the gri line placement errors. Imperfect spot griing will affect the forthcoming steps 1010

2 ISSN: (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 2015, VOLUME: 05, ISSUE: 04 such as spot segmentation, gene expression calculation an biological conclusion. So, perfect griing is a prime step in microarray image analysis. Microarray Image Fig.1. Block iagram of the propose metho 2.1 IMAGE TILT DETECTION AND CORRECTION A tilt present in microarray image ecreases the griing efficiency. Hence any tilt in the microarray image has to be rectifie before griing process. Image tilt can be calculate in two stages 1) Tilt ientification an 2) Tilt angle calculation. Tilts in the images are observe in two ifferent irections, with respect to the x an y axes. To fin the tilt angle in the image, Raon transform is applie. The Raon transform, transforms microarray image into a new image plane with parameters an t. Each point in new image plane accumulates all information corresponing to a line in original image with angle an raius t. When Raon transform localizes a local maximum near an angle 0 an aroun a slice t 0, it means the original image has a line in position ( 0, t 0 ) [19], [20]. Applying Raon transform to an image A(x,y), for a given set of angles can be thought of as computing projection of the image along given angles. The resulting projection is the sum of intensities of the pixels in each irection, i.e. an integral line. Result is a straightene image R(,). R, Ax, y xcos y sin xy where, is efine as (1) = xcos + ysin (2) where, (0) is Dirac elta function an is the perpenicular istance of a line from the origin an θ is angle between line an y-axis. 2.2 PREPROCESSING Real cdna microarray image contains a number of sub arrays, an each sub array contains large number of spots arrange in rows an columns. In the pre-processing stage, the given image is first converte to grayscale. A histogram base threshol is use to replace image backgroun with zero intensity value an enhance spot intensity by using a scaling factor. The eges of spots are etecte using canny ege etection. Morphological operations such as ilation an erosion are one after filling spots. Meian filtering is one to remove isolate noisy pixels. 2.3 GRIDDING Image tilt etection an correction Preprocessing Griing Propose gri line refinement The aim of griing is to fin the sub array locations in the image an then the spot location co-orinates within the sub array. The griing is use to etect each spot location from the image. To fin the location of spots, coorinates of each spot is necessary. Consier a microarray image A having p rows an q columns of sub arrays an each sub array with m rows an n columns of spots. The first step in griing fins vertical an horizontal sub array location co-orinates V an H, V = [V 1, V 2 V q+1 ] an H = [H 1, H 2 H p+1 ]. Secon step fins spot location co-orinates within each sub arrays v an h, where v = [v 1, v 2 v n+1 ] an h = [h 1, h 2 h m+1 ], m+1 an n+1 represents total number of horizontal an vertical grilines require to separate each spot within a sub array. S = {S ij }, where S is a sub array in image. S ij represents intensity values of each pixel in a sub array an i = 1,, a an j = 1,,b where a b represents the sub array size. The area between ajacent horizontal an vertical vectors h i+1 -h i an v i+1 -v i elimit the area corresponing to a spot. To obtain the co-orinates, horizontal an vertical intensity profile of the preprocesse image is first compute using Eq.(3) an Eq.(4) respectively. 1 h a 1 v b a i 1 b j 1 I i, j I i, j The horizontal an vertical intensity profiles are converte to a binary signal, the signals are inverte an autocorrelation of the signals are compute. The peak location coorinates of the auto correlate signals are estimate an are use as the horizontal an vertical co-orinate of gri lines. The slope of intensity signals, gives the perioicity of spots in the image. The spot layout is calculate from the perioicity of spot in horizontal an vertical irections. The horizontal an vertical start an en lines are compute using a histogram base threshol. An ieal microarray image has uniform spot size, inter spot istance, an alignment throughout the image. In real microarray images, spot size, inter spot istance an alignment of spots vary with image atabases. Noise artifacts an eformation of images, which affect griing also vary with image atabases. The existing griline optimization or gri line refinement techniques are not able to gri all atabase images successfully. Hence a more flexible an accurate gri line refinement metho is suggeste. 2.4 GRID LINE REFINEMENT In practical images, gri line placement errors are ue to reunant lines, missing lines an misplacement of lines. A sub array image with spot layout m n requires m+1 horizontal an n+1vertical gri lines. The occurrence of an increase or ecreases of griline are cases of reunant or missing gri lines respectively. Gri lines place insie the spots are cases of misplace lines, provie the require number of lines in both horizontal an vertical irections are satisfie. A gri line refinement metho is propose in this paper to etect an correct the errors mentione above. Two parameters nee to be compute for etecting the errors. 1) Spacing between horizontal an vertical gri lines h (k) an v (l). (3) (4) 1011

3 2) Mean horizontal an vertical gri line spacing istance h an v. The parameters, h (k) an v (l), h an using Eq. (5), Eq.(6), Eq.(7) an Eq.(8). v are compute h (k) = h l (k+1)-h l (k) kє[1, m+1] (5) v (l) = v l (l+1)-v l (l) lє[1,n+1] (6) 1 m 1 h k1 k h (7) m 1 1 n 1 v l1 l v (8) n 1 where, h l an v l enotes horizontal an vertical gri lines vectors an k an l enote the gri line number. Reunant line or misplace gri line is etecte in (k+1) th an (l+1) th location if Eq.(9) an Eq.(10) are satisfie respectively. k h 0. 8h (9) l v 0. 8v (10) The gri line vectors are upate by eleting the (k+1) th an (l+1) th line as follows: h l = [h l (1:k) h l (k+2:en)] v l = [v l (1:l) v l (l+2:en)] Missing gri lines are etecte in (k+1) th an (l+1) th location if Eq.(11) an Eq.(12) are satisfie respectively. k h 1. 5h (11) l v 1. 5v (12) Error can be correcte by upating horizontal an vertical gri line vectors by inserting gri lines in (k+1) th an (l+1) th location as follows. h k 1 hl k 0. 9h l l 1 vl l 0. 9v l v tempx = h(k+1:en) tempy = v(l+1:en) h l = [h l (1:k) h l (k+1) tempx] v l = [v l (1:l) v l (l+1) tempy] where, tempx an tempy are temporary variables use to place the missing line co-orinate. The above mentione threshol values are obtaine using trial an error metho by applying the algorithm on ifferent atabase images. 3. DATABASE USED The algorithm is teste with six ifferent image atabase to valiate accuracy an flexibility of propose metho in microarray image griing. Spots in an image vary in iameter, spacing an alignment in both horizontal an vertical irection. The scanning resolution of an image is also ifferent. Database1 inclues thirteen images selecte from UNC microarray image atabase, with experiment IDs to uner Cell line as category an rug treatment as sub category. Each image consists of 32 sub arrays an each sub array have 625 spots. Database2 inclues images selecte from Stanfor microarray ata base (SMD), with experiment IDs 20385, 20387, 20391, 20392, an by selecting Hormone treatment as category an Transcription factors as subcategory. Each image consists of 32 sub arrays an each sub array have 270 spots. Database3 inclues fourteen microarray images selecte from Computational Cancer Genomics (CCG) group of the Swiss Institute of Bioinformatics (SIB) ( Experiment IDs of images are 661 to 667. Each image inclues 4 sub arrays an each sub array with ifferent spot numbers, which varies from 42 to 49. Database4 inclues images ownloae from following website aress ( projects/magic/magic.html,derisi). Database4 inclues images from channels 1 an 2 with experiment IDs 1302, 1303, 1309 to Each image contains four microarray sub arrays an each sub array contains 1,600 spots. Database5 contains two real images with 36 sub arrays an each sub array with 210 spots. The images can be ownloae from the University of California, San Francisco (UCSF, ata). Database6 inclues images from Gene Expression Omnibus. Images are ownloae from website Thirteen images are ownloae corresponing to channels 1 an 2 with experiment IDsGSM15898, GSM16101, GSM16389, GSM16391, GSM17137, GSM17186, GSM17190 an GSM Each sub array has 182 spots. 4. RESULTS AND DISCUSSION 4.1 IMAGE TILT DETECTION AND CORRECTION To make the whole griing process effective, microarray image tilt etection an correction are important. To etect microarray image tilt an angle calculation Raon transform is use. To fin the accuracy of Raon transform in estimating tilt angle of real microarray images, an artificially tilte microarray sub array image with several angles in both clock wise an counter clockwise irections are use. The tilte angles are estimate by applying Raon transform. The result shows that the angle estimate by the transform is same as that of the artificially tilte angle. So this can be applie successfully for the real microarray tilte images for estimating the unknown angle. Fig.2(a) shows a microarray image tilte by an angle 5 egree an Fig.2(b) shows the correcte image after estimating the angle using Raon transform. 1012

4 ISSN: (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 2015, VOLUME: 05, ISSUE: 04 Table.1. Percentage spot etection accuracy obtaine for genetic algorithm base metho; maximum between class variance base metho an propose metho Data Base Genetic algorithm base metho Maximum between class variance base metho Propose metho Incorrect Marginal Perfect Incorrect Marginal Perfect Incorrect Marginal Perfect UNC SMD SIB Dersi UCSF GEO marginal gri line placement. Arrow pointe vertical gri lines in Fig.3(a) shows marginal griline placement in the metho. From Fig.3(a) it is also observe that in this metho the bottom ege gri line is missing. Error in maximum between class variance base metho is mainly ue to incorrect griline placement. Arrow pointe vertical an horizontal gri lines in Fig.3(b) support this contention. The propose metho shows an improvement of 1.41% spot etection accuracy for atabase2 images compare to other two methos. The griing results of three methos for an image from atabase2 are shown in Fig.4(a-c). The arrow pointe vertical gri lines in Fig.4(a) an Fig.4(b) show examples of marginal gri line placement obtaine using first two methos for an image from atabase2. From Fig.(4b) it is also observe that in maximum between class variance metho the bottom horizontal ege gri line is missing. Fig.2(a) Fig.2(b) Fig 2(a). Tilte microarray image by a 5 egree angle, 2(b). Correcte image using raon transform 4.2 PERFORMANCE OF THE PROPOSED METHOD The performance of the propose metho is teste with six ifferent image atabase. The images from each atabase vary in image resolution, sub array layout, spot layout an spot resolution. Propose metho is compare with two existing metho such as genetic algorithm base metho [16] an maximum between class variance metho [18]. Percentage spot etection accuracy obtaine for gri lines which separate spots incorrectly, marginally an perfectly for all the six atabases are shown in Table.1. The Table.1 shows that the propose metho has an improvement of 2.54% spot etection accuracy compare to other methos for atabase1 images. Griing results obtaine for an image from atabase1 using genetic algorithm base metho; maximum between class variance base metho an propose metho are shown in Fig.3(a-c) respectively. In genetic algorithm base metho the error in griing is mainly ue to Fig.3(a) Fig.3(b) 1013

5 Fig.3(c) Fig.3(a-c). Griing result obtaine using genetic algorithm base metho, maximum between class variance metho an the propose metho for a sub array image from atabase1 For atabase3 images, the perfectly grie spots, spot etection accuracy is less than 90% for all the three methos. In this atabase the image spot iameter is less than inter spot istance which result in incorrect an marginal gri line placement errors. As the number of spots in a sub array is less than 50, a single instance of incorrect placement of griline will consierably affect spot etection accuracy measure. Griing results from a atabase3 image using the above three methos are shown in Fig.5(a-c). From Table.1 spot etection accuracy obtaine for atabase3 images shows an improvement of 4 % for the propose metho compare to other methos. The arrow pointe vertical gri lines in Fig.5(a) show examples of incorrect an marginal griline placement error for genetic algorithm base metho. Fig.5(a) Fig.4(a) Fig.5(b) Fig.4(b) Fig.4(c) Fig.4(a-c). Griing results obtaine using genetic algorithm base metho, maximum between class variance metho an the propose metho for a sub array image from atabase2 Fig.5(c) Fig.5(a-c). Griing result obtaine using genetic algorithm base metho, maximum between class variance an the propose metho for a sub array image from atabase3 1014

6 ISSN: (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 2015, VOLUME: 05, ISSUE: 04 Results in Table.1 for atabase4 images shows an improvement of 1.38 % spot etection accuracy for the propose metho compare to other methos. In this image atabase image spot layout an spot resolution is (1600 spots in a sub array) an 8 8 respectively which result in spots of smaller size in the sub array. So some high noisy images in the atabase give incorrect griing an marginal griing error an hence the average spot etection accuracy is less than 95% for all the methos. For atabase5 images the propose metho shows an improvement of 0.96% spot etection accuracy compare to other methos. Incorrect gri line placement error occurs for images in this atabase ue to improperly aligne spots in some rows of the image. Fig.6(a) The Table.1 shows an improvement of 1.87% spot etection accuracy for the propose metho compare to other methos. Fig.6(a-c) show griing result of a atabase6 image using genetic algorithm base metho, maximum between class variance base metho an the propose metho respectively. Arrow pointe horizontal gri lines in Fig.6(a) shows reunant gri lines in non spot area for genetic algorithm base metho. The average spot etection accuracy obtaine for all the six image atabases using genetic algorithm base metho, maximum between class variance base metho an the propose metho are %, 92.55% an % respectively. The propose metho shows an overall improvement of 2.51%. Performance of all three methos are evaluate on Intel core i3 processor with 2 GB RAM. Sub array images use are from six ataset of ifferent imensions. Table.2 shows the average time taken for etecting a spot from a set of images in a atabase. The propose metho is compare with genetic algorithm base metho an maximum between class variance base metho on a time scale. Time elapse for spot etection is compute by fining ratio of total time taken for griing a sub array image to total number of spots in sub array. In a microarray image, the number of spots in a sub array are large. So a small improvement in time taken in secons for the propose metho compare to maximum between class variance base metho is feasible. Spot etection accuracy an spee of the propose metho compare to other two existing methos shows improve flexibility an accuracy of the propose metho in microarray griing. Table.2. Average processing time for spot etection (secons) Fig.6(b) Database Genetic algorithm base metho Maximum between class variance base metho Propose metho UNC SMD SIB Dersi UCSF GEO CONCLUSION Fig.6(c) Fig. 6(a-c). Griing result obtaine using genetic algorithm base metho, maximum between class variance an the propose metho for a sub array image from atabase6 In this paper a gri line error correction metho base on a refinement technique is propose. The metho is teste with microarray images rawn from six ifferent image atasets of varying resolution. Result shows that the propose metho improves the spot etection accuracy irrespective of microarray image resolution, sub array lay out, spot lay out an inter spot istance without ajusting any other parameters. The spot etection time of the metho is also compare with other methos. The overall result obtaine on real microarray images shows that the propose metho is a goo choice compare to genetic algorithm an maximum between class variance base metho in microarray image griing. 1015

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