Image Analysis and Morphometry

Transcription

1 Image Analysis and Morphometry Lukas Schärer Evolutionary Biology Zoological Institute University of Basel / Zoology & Evolution Block Course Summary Quantifying morphology why do we need it? Image acquisition image formats and lighting conditions Particle analysis determining particle size with ImageJ statistical analysis with JMP Geometric morphometrics analysis of complex shape variation placing landmarks with tpsdig relative warp analysis with tpsrelw statistical analysis with JMP 2

2 Quantifying morphology phenotypic differences between individuals in a population are the combined result of genetic variation, environmental influences during development and usage of the structure natural selection acts on differences in the phenotype between individuals so a quantitative understanding of phenotypic variation is required to understand development and evolution many traits can be measured directly from the individuals, e.g. using a caliper but computer assisted image analysis can often help to quantify more complex traits and it can greatly speed up analysis 3 Quantifying morphology precision and accuracy are two different issues one can measure something with very little measurement error, but still have a biased sample 4 from Howard & Read 1998

3 Image acquisition an image is a table of numbers and each cell in represents one pixel cell values range from (i.e. 8-bits) with white 0 and black 255 (or vice versa) and many shades of grey in between for some image analyses it is better to have 16-bits per pixel (65536 grey levels) x-coordinate x-coordinate y-coordinate y-coordinate Image acquisition it is therefore possible to make calculations with images one can, for example, add, subtract or average two images one can select all the values above or below a certain threshold pixel! grey-level 0! 95 1! 96 2! 96 3! 96 4! 95 5! 95 6! 95 7! 95 8! 95 9! 95 10! 94 11! 93 12! 93 13! 92 14! 91 15! 89 16! 83 pixel! grey-level 17! 73 18! 61 19! 52 20! 47 21! 46 22! 47 23! 48 24! 50 25! 52 26! 53 27! 54 28! 55 29! 56 30! 57 31! 58 32! 59 6

4 Image acquisition if this is done on the entire image it is possible to select certain structures of interest 7 Image acquisition optimal thresholding requires a drastic and uniform difference between the structure of interest and the background the bigger the difference the better 8

5 Image acquisition colour images are usually represented by an 8-bit image for each colour channel (RGB, i.e. red green and blue) they therefore require 3-times more storage space they are more difficult to analyse (e.g. threshold) because their colour space is three-dimensional colour blue green red 9 Image acquisition there are many image formats (.tif,.jpg,.gif,.png) not all are equally suitable for image analysis the best format is.tif because it uses the raw image data many other formats use a compression algorithm that can change the structure of the data substantially digital consumer cameras are often not very suitable, because they often use compression (except for the.raw format) many flat structures (e.g. leaves) can be optimally imaged with flatbed scanners some structures can imaged on a light table but be aware that neon lights have a highly variable light intensity 10

6 Particle analysis many morphological characters are particulate so they have a distribution, an average size and a variance (see e.g., the fish eggs, red blood cells or virus particles depicted on the right) to estimate these measures requires measuring many particles per individual 11 Particle analysis: a worked example open file in ImageJ select the line tool and measure a known distance on the ruler (e.g. 10 cm) measuring a long distance reduces the error choose Analyse > Set Scale and enter the distance in the field known distance check the box Global (so future images are opened with same calibration) choose Image > Type > 8-bit (to remove the colour information) select the area with the particles using the rectangle tool and then select Image > Duplicate (this makes a new file with only the particles) choose Image > Adjust > Threshold and set the upper and lower thresholds to select the particles click on the Apply button to convert the image into a bitmap (1-bit per pixel) select a small particle using the wand tool, measure it (Analyze > Measure), check the results and then deselect it using (Edit > Selection > Select None) choose Analyze > Analyze Particles and set the size range to include the smallest particles (this allows to ignore dirt or other things) 12

7 Particle analysis: a worked example copy the results table and paste it into JMP check the size distribution of the particles by choosing Analyze > Distribution look at the distribution and the values that are reported what do you observe? is the distribution unimodal? is it a normal distribution? could it be that there are different types of TicTac? classify the TicTac according to type and compare them by choosing Analyze > Fit Y by X choose type as the X variable and particle area as the Y variable select Means/ANOVA/Pooled t and Means and Std Dev from red arrow menu look at the figure, the different measures of central tendency and the statistics that are reported make a conclusion 13 Geometric morphometrics the aim of geometric morphometrics is the analysis of complex shape variation shape variation can be analysed by measuring the linear distances between certain landmarks but in this example the choice of which linear measurement are used is arbitrary in fact there are 120 possible linear measurements that could be used with 16 landmarks we could choose the ones that are most informative, but we only know this after me make the analysis geometric morphometrics uses all available information and the data set is reduced to the landmarks alone 14 from Zelditch et al. 2004

8 Geometric morphometrics shape is independent of location, scale (or size) and orientation during the analysis process these factors are removed from the data 15 from Zelditch et al Geometric morphometrics this results in a centroid size (a measure of size variation) for each individual a cloud of points for each landmark (a measure of shape variation) several relative warps, which describe shape variation at different spatial scales the shape variation is often visualised with the thin plate metaphor i.e. as the deformation of a thin metal plate 16 from Zelditch et al. 2004

9 Geometric morphometrics 17 Geometric morphometrics: a worked example creating a tps file before you can place landmarks you need a tps file (i.e. a list of all your images) place all your images (or copies of them) in the same folder open tpsutil (Start > Programs > tps > tpsutil) click on Select an operation and choose Build tps file from images from the drop-down list to select your input directory click Input, find your directory of images, and double-click on one image in that directory to name your output file click Output, choose a name that ends in.tps, and save this file in the folder together with your images finally, to build the tps file click Setup (the checked images will be used to build your tps file), confirm that you have a file named [something].tps under File to be created, then click Create and choose Close to exit tpsutil you should now have a file that you can open in tpsdig2 18

10 Geometric morphometrics: a worked example placing landmarks open tpsdig2 (Start > Programs > tps > tpsdig2) open your tps file (File > Input Source > File...). you can scroll through your images with the red arrow buttons and zoom with the + and - buttons the file name is shown at the bottom and the number of landmarks will appear as you digitise use the Draw Mode (Modes > Draw curves) to place a help line along the middle of each finger by defining the start (one click) and the end of the line (double click) place landmarks by clicking with the blue cross hair icon in the order indicated in the figure (use Edit Mode to delete or move lines or landmarks) save your landmark data (File > Save data > Save > Overwrite) and repeat this process for each hand 19 Geometric morphometrics: a worked example relative warp analysis open tpsrelw (Start > Programs > tps > tpsrelw) open the tps file with the landmark data (File > Open) open the link file Hand_links.nts which is provided by us (File > Open link file) the link file determines between which landmarks the program draws lines compute Consensus, Partial Warps, and Relative Warps by clicking on the buttons in sequence save Centroid Size and Relative Warp Scores matrix (File > Save) for later statistical analysis choose a name that end in.nts to convert this file into a format you can import into Excel or JMP use the Convert tps/nts option in tpsutil use your nts file as the Input and choose a.csv file as the output and click create import this file into JMP 20

11 Geometric morphometrics: a worked example interpretation of relative warp scores plot the consensus hand shape (Actions > Consensus) to display the links select Options > show links plot the relative warps (Actions > Plot relative warps) select the Camera button to visualise a point in the shape space by default, the shape space of the 1st and the 2nd relative warp scores is shown (see X and Y ) move the cursor (open red circle) in the shape space to get an idea what kind of a change in shape a single warp score describes view the report to see the proportion of shape variation explained by the different relative warp scores (File > View Report) to get this kind of display select Options > points and Options > vectors 21 Software used ImageJ ( public domain java program that runs on most platforms huge user base, many developers and very helpful discussion forums tpsutil, tpsdig2 and tpsrelw ( three of a range of free PC programs developed by James Rohlf JMP 10 ( a commercial statistical software with a very intuitive user interface runs on both PC and Mac the University has a campus licence, which costs 15 CHF per year for students and 20 CGHF per year for other University members 22

12 Follow-up Literature Zelditch, M. L., D. L. Swiderski, H. D. Sheets, and W. L. Fink Geometric Morphometrics for Biologists. Elsevier, Amsterdam, The Netherlands. Howard, C. V., and M. G. Reed Unbiased Stereology. Three-Dimensional Measurement in Microscopy. Bios Scientific Publishers, Oxford, UK. 23