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Journal of Neuroscience Methods 200 (2011) 229– 236

Contents lists available at ScienceDirect

Journal of Neuroscience Methods

jou rna l h om epa ge: www.elsev ier .com/ locate / jneumeth

utomated analysis of zebrafish images for phenotypic changes in drug discovery

hiye Chena, Yongxu Zhua, Weimina Xiab, Shunren Xiaa,∗∗, Xiaoyin Xuc,∗

Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, ChinaCenter for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USADepartment of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

r t i c l e i n f o

rticle history:eceived 27 January 2011eceived in revised form 9 May 2011ccepted 17 June 2011

eywords:ebrafishigital image processing

a b s t r a c t

Zebrafish has become one of the most popular and useful models in cell biology, development, anddrug discovery. Because zebrafish embryo is transparent and can be observed under microscope withoutfixation, it is increasingly used in high-throughput screening. The small size of zebrafish embryos allowsusers to image them in a 96- or 384-well plate under various conditions, in turn, generating such alarge amount of images that only automated analysis is feasible for processing and analyzing. We focuson developing an image processing algorithm to automatically quantify gene expression on zebrafishembryos that have been treated by various compounds. The challenge in this type of application includes

mage alignmentutomatic creation of ROIuantification of gene expression

aligning embryos of different orientations and automatically creating regions of interest (ROIs) to enclosespecific areas in the head and torso of embryos. The image processing pipeline consists of alignment,segmentation, creation and quantification of ROIs. We test the algorithm using high-throughput imagesof zebrafish embryos obtained from an experiment screening compounds that may affect �-secretase inAlzheimer’s disease and results show that automated analysis can achieve satisfactory performance in amuch shorter amount of time with a high level of objectivity.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

As a vertebrate animal model, zebrafish (Danio rerio) is widelysed in many fields of biomedical research. Compared with othernimal models zebrafish has many unique advantages, includingransparent embryos, a high similarity with the human genome, ann vitro and a high fertility rate that allows researchers to obtain aarge number of samples in a short period of time (Zon and Peterson,005). Phenotypes of zebrafish at different development stages,rom embryos to adulthood, have been characterized (Kimmel et al.,995). In addition, zebrafish can be relatively easily modified forenetic expression to mimic various conditions ranging from can-er, development, to neurological diseases (Smith et al., 2010;rummond et al., 1998; Amatruda et al., 2008; Panula et al., 2010).or example, genetically engineered zebrafish has been used in theearch for treatment of Alzheimer’s disease (Xia, 2010; Newmant al., 2011) and Parkinson’s diseases (Boehmler et al., 2009; Sun

nd Gitler, 2008). Researchers can observe changes and characteris-ics of living embryonic midbrain, heart, kidney and other complexrgans using in vivo microscopic imaging techniques, while it is

∗ Corresponding author. Tel.: +1 617 525 9596.∗∗ Co-corresponding author. Tel.: +86 571 8795 1703.

E-mail addresses: srxia@zju.edu.cn (S. Xia), xxu@bwh.harvard.edu (X. Xu).

165-0270/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.jneumeth.2011.06.015

often difficult to do so for other animal models. For these rea-sons, zebrafish has been widely applied in vertebrate development,human diseases modeling and drug screening (Patton and Zon,2001; Stern and Zon, 2003; Campbell et al., 2006; Pujic et al., 2006;Barros et al., 2008). Zebrafish has also found applications for study-ing the embryotoxic effect of chemicals (Yang et al., 2009). Forexample, transgenic zebrafish has been developed to create anin vivo assay to quantify the number of angiogenic blood vesselsas a means to study the effect of antiangiogenic compounds (Tranet al., 2007).

An important factor facilitating the application of zebrafish intoday’s biomedical research is the adoption of high-throughputscreening to vertebrate animal models (Peterson et al., 2000, 2004;Gehrig et al., 2009). Previously, high-throughput screening is onlyapplicable to in vitro cell culture and invertebrate animals. Withthe fast development of imaging techniques and improved automa-tion capability of instrumentation, high-throughput screening cannow be applied to zebrafish, which turns out to be a highly cost-effective candidate due to its small size and fast development aswell as the transparent embryo (Blackburn et al., 2011). Advantagesof zebrafish and its embryo as a vertebrate animal model make

it suitable not only for investigating diseases but also for screen-ing chemical compounds in an efficient manner, as described inKaufman et al. (2009). Molina et al. designed a high-throughputscreening strategy in zebrafish embryo for small molecules that

2 science

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2.2. Framework of algorithm

As shown in Fig. 1, the images are of different orientations.To automatically create ROIs for analysis, the first step of image

30 S. Chen et al. / Journal of Neuro

arget dual-specificity phosphatase 6, which is a component of thebroblast growth factor (FGF) signaling pathway (Molina et al.,009). Using a transgenic model expressing destablized green flu-rescent protein, Molina et al. screened over 5000 compoundsnd found a small molecule that enhanced FGF expression. Thevailability of a large number of zebrafish embryos makes themell suited for characterizing gene expression. Fan et al. (2010)sed quantitative real time polymerase chain reaction (qRT-PCR)o profile ten nervous system genes and found that a subset ofhe ten genes were altered in the presence of ethanol, a neuro-oxicant. The approach suggests that the genes may function asiomarkers for rapid developmental neurotoxicity screening suchs ethanol (Bilotta et al., 2004). Accompanying the application ofigh-throughput screening techniques on zebrafish is the chal-

enge to quickly, objectively, and quantitatively analyze the vastmount of images generated from the screening experiments. Fromn image processing perspective, a successful algorithm must takento consideration the objectives of the high-throughput screen-ng and the content of the images. While some experiments focusn acquiring fine details such as neurons and somites in zebrafishmbryos, other experiments aim to study the behavior of zebrafishn response to environmental cues. Over the last several years,esearchers have dedicated efforts to develop image processinglgorithms to analyze behavioral and morphological features inebrafish (Liu et al., 2006, 2008a,b). At a microscopic level, methodsave also been developed to extract three-dimensional features ofeurons (Kamali et al., 2009) and vasculatures (Feng et al., 2005)

rom zebrafish. Besides analyzing microscopic features of zebrafish,ethods also have been developed to track behaviors of multi-

le fish in a dynamic environment (Kato et al., 2004). Band et al.2002) developed an automated screening assay to detect hearingefects in zebrafish by monitoring their behavior after receiving

loud sound burst. And in some applications, the requirementsor image processing are to detect the whole fish at first and thennalyze more detailed features. Based on shape analysis, we haveeveloped an algorithm to extract the torso of a zebrafish embryos a first step and then create a region of interest (ROI) to quantifyigmentation in a high-throughput manner (Xu et al., 2010). Theork of Xu et al. (2010) focused on the lateral view of zebrafish

mbryo and automatically identifying its torso area by findingts boundaries and centerline to determine a single ROI contain-ng the pigmentation, which is quantified and used a marker to

easure how the zebrafish embryos respond to various environ-ental cues. In summary, many methods have been developed to

rocess zebrafish images, yet as applications of zebrafish expandn research, the corresponding requirements for image processinglso change and new image processing algorithms are constantlyeing requested.

In this work we focus on developing an image processingipeline within the framework of high-throughput screening toxtract and align zebrafish embryos of different orientations in theirorsal views to quantify gene expression in various areas in theead and upper torso. It is known that gene expression of zebrafishmbryos treated with various drugs will be different, which leads tohe phenotypic changes in the regions of forebrain, midbrain, andther regions of the embryos. We can image the embryos using

microscope and observe the gene expression in terms of pixelntensity at different regions. However, to statistically analyze thehange, we need a method that can automatically place ROIs onach image to measure the pixel intensity. This paper presents aramework of image processing and analysis algorithm that con-ists of extracting an object, realigning zebrafish embryo images of

ifferent orientations, and creating and quantifying ROIs. We usedeal data obtained from an experiment studying the effect of �-ecretase inhibitors on zebrafish embryos to test the performancef the algorithm.

Methods 200 (2011) 229– 236

2. Materials and methods

2.1. Data acquisition

Zebrafish embryos were placed in 96-well plates with oneembryo per well. In situ hybridization was carried out using a her6probe according to the protocols described in Thisse et al. (1993).Different compounds were dissolved in 1 mL of egg water, and0.1% dimethyl sulfoxide (DMSO) was used as a negative control.Embryo medium was replaced with the compound solution at 6 hpost-fertilization. Embryos were de-chorionated the following dayafter the compound treatment and incubated at 28 ◦C overnightbefore images were taken. Before imaging, tricane was added tothe final concentration of 0.4% in the same plate. The plate wasthen loaded into an IN Cell Analyzer 1000 (General Electric Co., Fair-field, CT), an automatic high-throughput microscope, for imaging.Images of each well were automatically acquired at four differentdepths. For the purpose of following image processing, we selectthe image that shows a large portion of an embryo. The size ofeach image is 2048 × 1536 pixels with 16 bits per pixel. We exam-ined the images for the expression of the Notch downstream targetgene, her6, in terms of the intensity of her6 expression in differentregions of the embryos, a method that has been successfully usedby us and other researchers (Arslanova et al., 2010; Campbell et al.,2006; Bernardos et al., 2005). Fig. 1 shows representative imagesacquired over four wells by IN Cell Analyzer 1000. We note thatthe orientation and amount of details of each embryo are differ-ent. Some embryo images lack the tail while other images do notshow visible eyes clearly. To quantify gene expression in differentregions, manual analysis requires one to draw ROIs for every imageand then measure the total pixel intensity contained in each ROI.Often a human observer needs to rotate the images to the sameorientation before drawing ROIs and quantifying pixel intensity,adding more time effort to the already laborious process. In addi-tion, manual analysis is subject to inter-observer variation and lacksrepeatability. As described below, we develop an image process-ing pipeline to automatically align the images and measure geneexpression in ROIs. Specifically the algorithm is designed to quan-tify her6 expression in 15 regions, including the telencephalon, leftand right (L&R) dorsal diencephalon, L&R olfactory vesicles, ventralmidbrain, L&R retina, L&R branchial arches, ventral hindbrain, L&Rdorsal hindbrain, and L&R pectoral fin, as shown in Fig. 2.

Fig. 1. Four original images of zebrafish embryos treated with different drugs.

S. Chen et al. / Journal of Neuroscience

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Fig. 2. A zebrafish embryo and ROIs.

rocessing is to align all the images along one direction. Theipeline of image processing is shown in Fig. 3, including image

lignment, analysis of morphological characters, modeling of ROIs,nd automatic extraction and quantification of ROIs. Because eachmage may contain a different amount of details, it is difficult toirectly align two images such as overlaying them by maximizing

Fig. 3. Flowchart of the imag

ig. 4. Results of preprocessing a zebrafish embryo image. (a) The original image. (b) The gperations. (e) The result after using median filter by 25 × 25. (f) The skeleton of the zebr

Methods 200 (2011) 229– 236 231

cross-correlation or mutual information. We designed the steps ofalignment to first segment the embryos and compute their center-lines by skeleton extraction and curve fitting, and then align themby their center-lines. First, the original color images (Fig. 4(a)) areconverted to grayscale images. A example of converted grayscaleimage is shown in Fig. 4(b). Second, we use a Sobel operator todetect edges (Fig. 4(c)) and morphological operators to fill the holesin the binary image, Fig. 4(d). To remove spurs and isolated 1’s weapply a median filter of 25 × 25 to smooth the image, Fig. 4(e). Afterthis step, we note that the edge of the zebrafish has been smoothedand the holes in the zebrafish body have been completely filled.Thirdly we apply morphological thinning to extract the skeleton ofthe zebrafish embryo, Fig. 4(f). The skeleton forks as the head ofthe zebrafish is much wider than the tail. The extracted skeleton isusually not straight, so we use a first-order curve fitting to calcu-late a straight-line that minimizes the area between the line andthe skeleton. The first-order curve fitting is given by

a = 1N

(N∑

i=1

yi − b

N∑i=1

xi

)(1)

b =∑N

i=1y2i

− Ny2∑Ni=1x2

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(2)

where y = (1/N)∑N

i=1yi and x = (1/N)∑N

i=1xi. And xi and yi rep-resent values of the column vector in the x and y directions,

respectively. The parameter values of the curve regression equationy = a + bx are calculated according to Eq. (2). Fig. 5 shows the curvefitting result. For the purpose of computation, we align all images inthe upright direction with the heads pointing upwards. The rota-

e processing methods.

rayscale image. (c) The result after edge detection. (d) The result after morphologicalafish embryo.

232 S. Chen et al. / Journal of Neuroscience Methods 200 (2011) 229– 236

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Fig. 5. The result of line fitting based on minimum area method.

ion angle of the image is the angle between the fitting line andhe vertical axis. According to the equation of fitting line, the angleetween the fitting line and the vertical axis (counter-clockwise)

s:

= 90◦ − arctan(b) (3)

he zebrafish image is then rotated counter-clockwise according to.

.3. Analysis of morphological characters

After realignment we extract the smallest rectangle enclosinghe complete zebrafish embryo in the image. In this step, first, wealculate all connected components in an image according to con-ectivity. Since each image includes only one embryo, the binary

mage after preprocessing has only one connected component. Wesed the “BoundingBox” option of Matlab’s function “RegionProps”o obtain the smallest enclosing rectangle (MathWorks, Natick, MA,SA). By definition, the BoundingBox outputs a 1 × 4 vector describ-

ng the upper left corner of the enclosing rectangle and its lengthnd width. We also used the “Centroid” option of “RegionProps”o obtain the coordinates of the rectangle’s centroid. The centroidbscissa is shifted to position the image so the embryo is in the mid-le of the image for the next step of processing. The main body of the

mage, enclosed in the rectangle, is segmented. Next the algorithmetects different regions of the embryo. For example, the telen-ephalon of an embryo is located at the top of the head along theenterline of the embryo and has a slightly convex shape. Next, wepply the Otsu’s method on the grayscale image to automaticallyetermine the gray threshold for the head which usually has pixelalues that are well separated from the background (Otsu, 1979),ig. 6. The boundary of the tail is determined after the head region isxtracted. This final boundary map allows the algorithm to identifyOIs as described next.

.4. Modeling and quantifying ROIs

There are fifteen ROIs to be detected in this work, as describedn Section 2.1. To model the ROIs, a user first initializes ROIs onne template image, It, of zebrafish embryo as small circles. Theocation and size of the user-defined ROIs are saved in an array, ˝.

e note here that the number of ROIs can be easily adjusted by theser. After the user initializes ROIs on the template image, other

mages are processed by the algorithm in the following manner. Aew image, In, n = 1, 2, . . ., is divided into 12 regions based on the

Fig. 6. Boundary of a head region of an embryo.

detected key points and lines, like the position of telencephalon, theleft and right boundary lines of the head, the boundary line fromhead to tail and the horizontal midline. These 12 regions are used toguide the placement of ROIs in the images. Each ROI is determinedto belong to one of the 12 regions, and the ratio of the ROI andboundaries of this region are calculated according to the positioncoordinates of ROIs stored in ˝. The position of each ROI is mappedto In according to the sizes of the 12 regional and the ratio of It overIn, Fig. 7.

The size of each ROI of In is determined by the ratio of areas ofIt over In. Finally, the mask of each ROI is created and the pixelswithin each ROI are calculated when the position and size of eachROI in In are determined.

3. Results

The accuracy of automatic extraction and quantification ofROIs is determined by the accuracy of alignment. We tested thealignment on 32 images and found that the results are satisfactory.Fig. 8 shows an example results of the results of the alignment,from which we observe that although the images are different interms of amount of embryos they contain and have different pixelvalues, the algorithm is able to align them in the same direction.Using the algorithm, the ROIs in It are mapped to In’s by comparing

the 12 regions between them. Tables 1–3 list the quantificationresults of three groups of zebrafish embryos treated by DMSO,DAPT (N-[N-(3,5-difluorophenacetyl-l-alanyl)]-S-phenylglycinet-butyl ester, which is a known �-secretase inhibitor), and �-

S. Chen et al. / Journal of Neuroscience Methods 200 (2011) 229– 236 233

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Table 1Quantification results of zebrafish embryos treated by DMSO at the concentrationof 0.1%. ROIs 1–9 refer to telencephalon, dorsal diencephalon, olfactory vesicles,ventral midbrain, retina, branchial arches, ventral hindbrain, dorsal hindbrain, andpectoral fin, respectively.

ROI 1 2 3 4 5 6 7 8 9

Embryo 1 1454 1659 874 2317 2257 670 1576 1355 601Embryo 2 2288 2314 1978 2589 2951 139 2162 2209 179Embryo 3 1583 1684 804 2594 1259 257 1674 1374 477Embryo 4 1606 1803 1264 2760 2017 547 1748 1797 338Embryo 5 1753 1976 1163 2490 2632 413 1833 1953 168

Table 2Quantification results of zebrafish embryos treated by DAPT at the dose of 5 �M.ROIs 1–9 refer to telencephalon, dorsal diencephalon, olfactory vesicles, ventral mid-brain, retina, branchial arches, ventral hindbrain, dorsal hindbrain, and pectoral fin,respectively.

ROI 1 2 3 4 5 6 7 8 9

Embryo 1 1246 1187 682 1890 1884 653 1202 1184 533Embryo 2 1154 1264 753 2037 2032 592 1276 1286 360Embryo 3 2086 2108 1073 2520 2055 540 1974 2077 304Embryo 4 1162 1032 608 1906 1732 605 1183 1175 581Embryo 5 1201 1143 721 1679 1656 439 1057 1063 450

Table 3Quantification results of zebrafish embryos treated by GSI at the dose of 10 �M. ROIs1–9 refer to telencephalon, dorsal diencephalon, olfactory vesicles, ventral mid-brain, retina, branchial arches, ventral hindbrain, dorsal hindbrain, and pectoral fin,respectively.

ROI 1 2 3 4 5 6 7 8 9

Embryo 1 1350 1328 581 2175 2160 1083 1420 1213 379Embryo 2 1324 1402 1012 2277 1978 612 1259 1348 348Embryo 3 1247 1349 461 1862 1683 895 1098 1301 231

Fig. 7. An embryo is divided into 12 regions, symmetric along its centerline.

ecretase inhibitors (GSI). A statistical analysis shows that there is significant difference between the DMSO group and either GSI orAPT group (p = 0.019 and 0.031, respectively), while there is no

Fig. 8. The example resu

Embryo 4 821 1280 376 1972 1734 620 1274 1367 302

statistical difference between the GSI and DAPT group (p = 0.482).For visual comparison, Fig. 10 displays quantification results ofthree groups of zebrafish embryos that have been treated byDMSO, DAPT, and GSI. From the figure we can observe both GSIand DAPT have resulted in reduced her6 expression in most ROIs,except branchial arches and pectoral fins.

One advantage of automatic quantification is reduced variationamong measurements. In Fig. 9 we plot quantification results ofTables 1–3. We calculated a pair-wise correlation coefficient among

each group. The lowest cross correlation coefficients of the DMSO,DAPT, and GSI groups are 0.775, 0.844, and 0.914, respectively, ver-

lts of realignment.

234 S. Chen et al. / Journal of Neuroscience Methods 200 (2011) 229– 236

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ig. 9. Quantification of ROIs of zebrafish embryos treated with DMSO, DAPT, andSI, respectively.

fying our observation from the figure that measurements withinach group are highly correlated.

As an automated approach, the algorithm can batch-process aarge number of image files. We used a personal computer with anntel Core2 Duo CPU 2.8 GHz and 3G memory and found that theverage time it takes for the algorithm to process an image is about6 s, while it takes a human observer around 70 s to complete pro-essing an image, starting from loading the raw image, rotating themage to an appropriate direction, creating the ROIs, and exportinghe quantification results. Compared with manual analysis, auto-

ated method also has the advantage of high repeatability andbjectivity. Moreover, image processing algorithms can be easilyoupled with statistical tools for further analysis.

. Discussion

As a versatile animal model, zebrafish is being used tonvestigate many biological processes and increasingly in a high-hroughput screening manner. Because the objectives and designs

Fig. 10. Distribution of quantifications of ROIs.

of the high-throughput experiments are different, the correspond-ing images have different scales and features. The large amount ofimage data generated from the experiments makes manual analysisa time-inhibiting and error prone process subject to inter-observervariations. Computer-based image processing methods are oftenthe only feasible approach to analyze and quantify the data. Gen-erally speaking, a high-throughput image processing method aimto achieve a high level of objectivity, automation, and speed. In thispaper we present an integrated image processing pipeline to ana-lyze phenotypic changes of zebrafish embryos after drug treatment.The pipeline consists of segmentation, realignment, and creation ofROIs for quantification. Taking into consideration that each imagemay contain different amount of a whole embryo and it is very dif-ficult to align two such images directly, the alignment step usesa linear curve fitting to find the center-line of an embryo andalign this center-line with other embryos. The ROIs are selectedaround the head of the embryos and can be easily changed basedon the experimental requirements. The advantage of automaticcreation of ROIs includes repeatability, fast speed, and easy quan-tification. Besides the saving in time to process high-throughputdata, automatic algorithms can be easily changed to suit end-user’srequirements, for example, to increase or decrease the number ofROIs or the information extracted from each ROI. In this work, wemeasured pixel intensity in each ROI as a measurement of geneexpression in zebrafish embryo. It is possible to measure somemorphological features or the level of fluorescent protein for geneexpression (Bian et al., 2010). Quantification results are exportedas a plain-text file that can be seamlessly imported to statisticalanalysis software. The automatic analysis results concur with ourobservation that �-secretase inhibitors such as DAPT resulted inreduced her6 staining when compared with the negative control(Arslanova et al., 2010).

One key factor affecting the performance of the pipeline of thealgorithms is image registration. Image registration itself is a topicthat has been actively studied by many researchers. Broadly speak-ing, existing techniques can be classified into rigid and non-rigidregistration between two images (Zitova and Flusser, 2003). Usuallyimage registration aims to maximize or minimize a cost functionof two images, e.g., maximizing cross correlation (Brown, 1992) ormutual information (Wells III et al., 1996) between them. In addi-

tion, there are other image registration methods based on contourconforming (Li et al., 1995) or Fourier transform (Eddyy et al., 1996).Most of existing techniques are based on pixel-wise registration

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S. Chen et al. / Journal of Neuros

hich attempts to map pixels from one image to another, under thessumption that the two images have the similar shapes and sizes,nd in many cases, similar pixel intensities. However, our work isnique in the sense that two zebrafish embryo images may con-ain a different portion of the embryos and due to the expressionf a specific gene, they may have very different pixel intensities,aking it inappropriate to try to register them exactly. Instead,

he objective of our work requires alignment of embryos alongheir most prominent components, which are the skeletons. There-ore we developed the algorithm to first segment the embryos andhen extract their skeletons for alignment. After alignment, ROIsre automatically mapped to each embryo for measurement. Fromhe test results, we observe that this approach can achieve satisfac-ory results and has a high computational speed. In this work theebrafish images were obtained at macroscopic level to show thehole embryo. Correspondingly the alignment algorithm is based

n analyzing the anatomic features of the embryos such as the headnd tail. It is possible that zebrafish images at higher resolutionf �m may be used in high-throughput screening. At such reso-ution, one may need to take into consideration other features touccessfully align two images.

We observe that image processing and computational tech-iques are only part of a successful high-throughput screeningpproach. A good setup in image acquisition is often the first step tonsure a satisfactory performance of the image processing meth-ds. While in this work we test the algorithm using images acquiredn an Alzheimer’s disease-related experiment, the algorithm can bepplied to quantify similar images generated in other experiments.n vivo screening may be combined with other in situ techniquesike DNA micrarray (Ton et al., 2002). We note that different tech-iques may generate results of various ranges in measuring genexpression in zebrafish. In the work of Ton et al. (2002) it was foundhat microarray data might show 4- to 5-fold of changes for parval-umin � and skeletal myosin light chain 3 while real-time RT-PCRight show a 50-fold change for parvalbumin � and 80-fold change

or the skeletal myosin light chain 3 in zebrafish. It is feasible to inte-rate functions of the presented algorithms with existing methodsnd toolboxes so more comprehensive zebrafish image processingoftware can be developed. The current version of the software isocated at http://mypage.zju.edu.cn/en/srxia/635715.html.

cknowledgments

The work of S. Xia was partly supported by National Natu-al Science Foundation of China 60772092. The work of X. Xuas supported by the Department of Radiology, the Brigham andomen’s Hospital and National Science Foundation award number

958345.

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