Parameter Selection using Evolutionary Strategies in - MPI-CBG
Contents
1. 0 4 4 2 Controller oaoa ee ee 5 4 3 Model 2 2 0 0 000 2 2 ee 6 4 3 1 Parameterized Macro 02004 6 4 3 2 Evolution Strategies 222000 7 5 Future Work 8 Appendix A User Manual 9 References 14 1 Introduction Images of all sorts have become an important data source for various fields of academia as well as for in dustrial applications With the ever increasing amount of images which are being produced the task of process ing them becomes more and more important ImageJ is an open source image processing tool which is written in Java and is therefore very portable ImageJ offers the essential image processing tools as its basic features and allows for extensions through plug ins as well as through macros Macros can even be produced by recording the users interaction with ImageJ which makes it rather easy to automate complex processing steps Typical im age processing tasks such as image segmentation often involve many parameters which have to be chosen by hand In some cases one might have prior knowledge about what might be good values for a specific param eter and in other cases one has to try one value after an other in order to find one which is suitable for the purpose of the task at hand The aim of this thesis is to create a Plug in for Im ageJ which automates this process in such a way that it guides the user through an optimization procedure where th2. added to the list of parameters such that it is possible to directly manipulate the values of the parameters within the macro In order to run the macro the code list containing the Strings and instances of Parameter are simply being con catenated and then this new macro string is passed to the runMacro method of ImageJ In the concatenation process the toString method is called on each instance of Parameter which inserts the current values into the right positions within the macro string and thus runs the macro with the current values for the parameters 4 3 2 Evolution Strategies The plug in uses the interface EvolutionStrategy to in teract with strategy implementations during the opti mization process Following are the methods which a strategy needs to implement with a description of what the purpose of the method is public void setDimension int dim Set the dimension of the optimization space which equals the number of parameters which have been enabled for optimization public void setDimensionBounds Double lower Double upper Set the bounds for the dimensions These are the bounds which have been defined by the user for the parameters public void setDimensionBounds int dim Double lower Double upper Set the bounds for one individual dimension public void setOffspringSize int offsprings Set the number of offsprings of each generation This must be set to the number of images which will
3. be displayed in the ranking UI public int getMinSelectionSize Retrieve the minimal number of individuals which need to have positive fitness This value is then set in the ranking UI as the minimal number of images which need to be ranked before a next step can be made public int getMaxSelectionSize Retrieve the maximal number of individuals whose assigned fitness will be used in the evolution step This value is then set in the ranking UI as the maximal number of images which can be ranked in each step public void setInitialX double x Set the initial value from which the first popula tion is created public void initialize Initialize the strategy Once the strategy has been initialized there will always be a current popula tion available public void setFitness double fitness Set the fitness of the individuals of the population The fitness is determined by the ranks which have been assigned to the images and set to be the in verse of the rank for all non zero ranks public void makeEvolutionStep Makes one evolution step based on the fitness which has been set and creates a new population public void repeatEvolutionStep Repeats an evolution step which usually corre sponds to resampling from the same distribution with a slightly smaller step size This method is used if the user decides that none of the images is worth ranking it public double getCurrentPop
4. by holding the shift Key and clicking on them Figure 2 Ranking UI with one ranked image Slice scroll bar hidden because image is not a stack the user interface are independent of ImageJ and could be used with other libraries through a different con troller class 4 3 Model The model in this application consists of the parameter ized macro and the Evolution Strategies 4 3 1 Parameterized Macro In order to detect parameters in a macro script a simple macro parser has been implemented which recognizes possible parameters in function calls The parameters which can be detected need either be numbers or be con tained in a constant string argument having the form name value Numbers in other places such as loops and if statements are ignored and treated as static code The macro parsing is implemented in the Parame terizedMacro class which needs to be instantiated with a string containing the macro code This code is then split into tokens by 3 Abstract Syntax Tree which is a tree like representation of the code Tokenizer tk new Tokenizer Program pgm tk tokenize macroString where the Tokenizer and Program classes of ImageJ are used This is basically the same step that the macro interpreter of ImageJ makes before actually interpreting the code The instance of Program which is being cre ated here contains a list of of tokens such as an if token or a opening bracket token Unfortunate
5. or turn of the mouse wheel can have different effects on how the image is being displayed It is also creating the region of interest selections on the ImagePlus instance that is being displayed These core classes are strongly interlinked and it is difficult to extend one without having to extend others due that strong dependency and the visibility of some fields and methods One more class is worth being mentioned here and that is WindowManager This class is as its name sug gests a manager for the windows in ImageJ but it has also a role in ImageJ which is important for the process ing of the images in the plug in developed in this thesis ImageJ uses the WindowManager to determine on which image a macro is going to be run This is usually the image contained in the instance of ImageWindow which was last in focus but it can also temporarily be set to a different ImagePlus instance with a call to setTem pCurrentImage This is important because it makes it possible to run a macro on an image which is not dis played in an instance of ImageWindow as it will be the case in the plug in 2 1 2 Plug ins Plug ins in ImageJ can be anything from an analysis tool to an image generator and it need not even have to do anything with ImageJ All that s required for a plug in to be used in ImageJ is that its main class implements the PlugIn interface or if the plug in is supposed to ma nipulate the currently focused image then it can also impl
6. such that implementations of either the controller or the user in terface can be changed without changes to the other It also allows to hide methods of the controller intended to be used by the selection UI from the ranking UI and vice versa The configuration of the plug in which includes e the selected file containing the macro e the flag for whether or not the history of the val ues and assigned fitnesses is being exported at the end of the optimization process e the selected Evolution Strategy e and the settings for the parameters in the param eterized macro is being stored in an instance of class Configuration which can be written to a file and later again loaded from a file to avoid having to configure the same pa rameters again and again for each image on which the optimization plug in is being run The controller creates both an instance of Param eterRankingUI and of ParameterSelectionUI which are being used for interaction with the user These instances are being created at the point where they are first re quired For the ParameterSelectionUI this is already upon start of the plug in and the ParameterRanking UI is being created when the configuration is complete and the optimization has been started Besides the use of ImagePlus in the Ranking UI the class Parameter Optimization Plugin is the only class which directly interacts with ImageJ so the classes for step Ranki or more images ofthe images below
7. Parameter Selection using Evolutionary Strategies in ImageJ Roland Bartschi Advisor Janick Cardinale Prof Dr Ivo Sbalzarini 04 07 2011 Abstract ImageJ is an open source image processing tool written in Java which is extendible through macros as well as through plug ins There exists a large collection of both kinds of extensions for various tasks such as image segmentation Plug ins often have various arguments and complex macros a lot of parameters These need usually be selected by hand which is a task that often depends on prior knowledge for what values produce the expected results In the absence of this knowledge the parameters are usually found by trying a few values until the results approximately match the expectations The Plug in developed in this thesis aims at automating this process with an intuitive user interface using Evolution Strategies such that the work on the user side mostly consists of ranking a few results in comparison to one another until a satisfying result has been reached Contents 1 Introduction 2 2 ImageJ 2 2 1 ImageJ 1 4x and Fiji 2 aa ee 2 2 1 1 Core classes of ImageJ o oo a a a 2 2 1 2 Plug ins aoaaa aaa 00 22 2000 02 eee ee 3 2 1 3 Macros aaa ee 3 2 1 4 Fiji is just ImageJ oaaao 3 2 2 ImageJ 2 0 naaa e 3 3 Evolution Strategies 3 4 The Parameter Optimization Plug in 4 Al View aeaa aaa 4 4 1 1 The Selection UI 0 0 00 4 4 1 2 The Ranking UI
8. act on the quality of the results and the performance of the optimization 11 step 3 Enable the parameters which you would like to optimize in the table below and adjust their bounds if necessary Method Parameter Optimize nitial Value Lower Bo run sigma 5 0 mn rolling B y setthres W oy o setThres BW 250 o n Gaussian Blur sigma 5 0 stacky run Subtract Background roling 20 0 light stack setThreshold 0 0 250 0 runMacrofConvertstackTobinary ot Once you have selected at least one parameter for the optimization you can click on the start optimization button oo to start the optimization process Note that there is also a possibility to save the current configuration to a configuration file by clicking on the save configuration button This configuration can later be opened in the first step instead of a macro file After a click on the start optimization button you should see a window similar to the following Step Rank 1 or more images of the images below by holding the shift key and clicking on them Select the next action 12 On the bottom left you have an area with 3 sliders which can be used to zoom in change the currently displayed slice and change the opacity of the overlay image which is the original image Use these controls to examine the different images Next you have to rank the images by holding the shift key and clic
9. e macro has been implemented with a focus on the MVC pattern so in the following sub sections the model view and controller are being discussed separately 4 1 View One of the main goals of this thesis is to make the pro cess of finding good parameters for a macro an easy task Therefore the user interface needs to be intuitive and should only require a minimal amount of work by the user To guide the user through the process each step has been labeled accordingly You can see a walk through with each step of the procedure in the user man ual in Appendix A 4 1 1 The Selection UI The selection UI is where all necessary configurations for the optimization process are made It contains e a button which opens a file dialog to select the macro e a drop down box to select the strategy e a check box to select whether or not to generate a history of the optimization process which can then be imported into MATLAB e a table for the selection and configuration of the parameters which are found in the macro e a text plane where the macro is displayed and the line containing the currently selected macro is highlighted e a button to save the configuration e and a button to start the optimization process once all necessary settings have been made The UI has been implemented using Swing and uses only standard components and containers 4 1 2 The Ranking UI This UI is used to select and rank the images which have bee
10. eJ 2 0 The release of the next major version of ImageJ is due in October and a first beta version should be released in June ImageJ 2 0 is being redesigned from ground up using modern Software Engineering principles to make it more modular and extendible One of the many changes is about the representation of the data In previous ver sions data was represented as an image or as a stack of Images organized in slices Although this basically allows for arbitrary dimensional data by arranging the slices in a particular order it can become very inefficient for high dimensional data in terms of memory access This issue has been addressed by Preibisch Toman k and Saalfel who developed ImgLib 3 a generic Java Image Processing library which is already in use in Fiji ImgLib allows to implement image pro cessing algorithms independent of the type of data that is being processed This is achieved through the Cur sor which defines an access strategy for the data Type which defines a data type and the operations on it and Container which addresses the storage of the data Due to the generic structure of ImgLib some performance issues have appeared for various plug ins using it but many of them have been addressed and resolved with ImgLib2 which is now the basic data library in ImageJ2 Through the extensive use of interfaces in ImageJ2 many of the before mentioned problems of extending an existing class of ImageJ have basically been elimi na
11. ement the PlugInFilter interface Just one more detail needs to be taken into account and that is that the name needs to contain an underscore in order for ImageJ to recognize it as a plug in But besides that it can contain arbitrary libraries and can have a user inter face implemented using a framework other than AWT or Swing 2 1 3 Macros As mentioned in the introduction ImageJ also offers the possibility to automate processes with macros which can even be recorded from actions performed in ImageJ This makes automation quite easy but macros can also be written by hand Macros have a Java like syntax and can basically run all features which are also available in the user interface l Official website http fiji sc wiki index php Fiji 2See roadmap of the development project on http imagejdev org 2 1 4 Fiji is just ImageJ As the maintainers of Fiji put it Fiji is an image processing package It can be described as a distribution of Im ageJ together with Java Java 3D and a lot of plug ins organized into a coherent menu structure Fiji compares to ImageJ as Ubuntu compares to Linux As stated in this quote Fiji is based on ImageJ and ex tends it with useful features such as an updating mech anism and a large collection of plug ins Fiji also intro duced ImgLib which will be used for image representa tion in ImageJ2 The plug in accordingly works both in a basic build of ImageJ as well as in Fiji 2 2 Imag
12. ey are only required to select the parameters which need to be optimized and to rank results based on their mutual quality The idea is to use Evolution Strategies such that the resulting images become better in each iteration based on the user s ranking until they are satisfied with one of the results and choose to apply the selected parameter values on the original image 2 ImageJ 2 1 ImageJ 1 4x and Fiji ImageJ is an open source image processing toolkit which is implemented in Java It supports many file formats such as PNG JPEG BMP GIF etc and images in the following data formats 8 bit grayscale 8 bit indexed color 16 bit unsigned integer 32 bit floating point and 32 bit RGB color It is extendibe through macros and plug ins Macros can be used for automation of process ing steps or complex processing pipelines It is possible to record the actions performed by the user on ImageJ in the form of a macro Macros can be edited saved and run at any time Plug ins are an even more power full extension because they can contain arbitrary Java code which can interact with ImageJ through a specified interface There are plug ins available for download on various websites and ImageJ even contains a text edi tor and java compiler for writing plug ins ImageJ can be used as an application or as a toolkit ImageJ is one of the fastest Java image processing tools available Basic editing and processing features are already imple mented a
13. ges in and a right mouse click zooms all images out It is also possible to pan the images if the hand tool is not selected but if the space bar is held down The last manipula tion of the display is possible by holding the shift key and scrolling the mouse wheel This changes the opacity of the overlay image which is the orig inal image before the macro has been applied to it e On the bottom left there are controls for manip ulating the image display with scroll bars instead of the scroll wheel This is also where the current slice or zoom factor is displayed along with the opacity of the overlay image e On the bottom right there is an area containing 4 buttons along with a short description of what their purpose is There is a clear selection button which clears the se lection that has been made a Next botton which is enabled as soon as the required number of images has been se lected ranked and which calls the controller to make a next optimization step Configuration step 3 Enable the parameters which you would like to optimize in the table below and adjust their bounds if necessary Method Parameter Optimize nitial Value Lower Bo Upper Bo run sigma 1 1 5 run _ rolling setThres B o o 1 setThres B 250 100 255 Strategy mu lambda sigma adapted Dd run Gaussian Blur sigma 5 0 stacky export history MATLAB run Subtract Background roling 20 0 ligh
14. h as width and height It also contains the ImageProcessor which is used to manipulate the image and for images which are more than 2 dimensional it contains an ImageStack which basically consists of an array of images ImageStack An ImageStack is an expandable array of slices which are arrays of pixels The pixels of the current slice can then be used to create a new ImageProcessor Slices can be added or removed and some meta data can be retrieved and set using methods of this class ImageProcessor ImageProcessor is used to operate on images and is the basic container holding the pixels of an image Image Processor is an abstract class which is extended by the classes ByteProcessor ColorProcessor FloatProcessor and ShortProcessor which implement the basic data ma nipulation operations based on the type of the pixels Image Window This class extends the AWT Frame class and is the ba sic window which is used to display images in ImageJ It contains an instance of ImageCanvas which is where the image is actually painted on the UI ImageCanvas This is an extension of the AWT Canvas class which is used to draw an image in ImageJ It is associated with an instance of ImagePlus from which it retrieves the AWT Image which it draws This class implements mouse and keyboard listener interfaces which are being used for interacting with the image Based on the tool which is selected in the tool bar of the main window of ImageJ a click
15. king on them starting with the one you rate best A number should appear in the top right corner of each ranked image depicting the rank which has been assigned to the image k m i ko e f _ moa F Ma i E L E 7 H S a id P w ti w a s d s 1 1 Li 1 1 is T T 1 k at Ta Fg A a 1 E a 1 woe n o Bes t Pues aa t ofa ofa so oid a a4 4 Fg mba BL Y C ml d 41 L a mi k Lal 1 F ia k m Wi of a i d e 1 As you rank images you will notice that the controls on the bottom right either become enabled or disabled based on the number of images you have ranked If you made a mistake while ranking you can clean the current selection and try again If the images are so different from what you expect them to be that you are not able to rank them then you can try to repeat the evolution step and hope that better results are being produces in that trial Once you have ranked the number of images which is being displayed in the info field on top then you can click on the Next button to generate the next set of images based on your ranking Step 5 Select the next action When you made a few optimization steps and arrived at a point where one or several images fulfill you expectations then you can finish the optimization process by clicking on the Finish button This applies the macro with the value of the selected image on the original image If you checked the box for the export
16. ly the current version of ImageJ does not actually parse these tokens into an AST but rather sequentially process the tokens and evaluate the statements which are being recognized and run the plug ins which are being called Given an AST it would have been much easier to find possible pa rameters Given the lack of said AST the tokens are be ing processed sequentially and when a token is reached which stands for a function name then an attempt is made to parse a parameterized method This attempt succeeds if either a number is found as an argument or a constant string which contains at least one substring matching the before mentioned name value pattern where name consists of letters and numbers and value can be parsed as an instance of type double If no po tential parameter is found in the function call or if an unexpected token appears in the argument list of the function call then the whole sequence of tokens making up the function call is treated as static code and written into a string But if at least one potential parameter is found then an instance of Parameter is being created and instantiated with the value of the parameter in the macro This way at the end of the parsing step we get a list of Strings and Doubles which are being referred to by instances of Parameter with corresponding Parameter Settings which make up the whole macro Whenever a new instance of Parameter is being created a reference to this instance is
17. n produced by applying the macro with the parame ters set to the values of the individuals of the evolution strategy But it is not restricted to displaying images from the evolution process as it is possible to set arbi trary ImagePlus resources for the images The constructor of the UI takes three arguments The first one is the number of images which will be dis played in this window The second is the minimal num ber of images which need to be selected to make a step of the evolution process and the third is the maximal number of images which are to be selected By selecting an image it is being assigned the next rank starting from 1 This rank is then passed back to the controller which uses it to compute the fitness of each individual The UI consists of 4 main areas e On the top there is a small information area which displays the instruction of how many images need to be selected and it also contains a small info icon which expands a hidden label if the user hovers the mouse over it In this hidden label are further in structions on how to use the ui and which tools of ImageJ work on the displayed images e In the center is the main panel which contains the images Using the scroll wheel of the mouse one can scroll through the slices of the image if it is a stack If the hand tool is selected in the ImageJ toolbar then one can pan the images if they are zoomed in If the zoom tool is enabled then a left mouse click zooms all ima
18. of the evolution data then a file dialog will pop up asking you for the file name to which the values will be written You should now see your image on which the macro has been applied with the optimized values 13 References 1 2 3 4 5 l6 7 el 8 J Cardinale Bachelor thesis description Parameter Selection using Evolutionary Strategies in ImageJ unpublished ImageJ Features http rsbweb nih gov ij features html S Preibisch P Toman k and S Saalfel Into ImgLib Generic Image Processing in Java http fly mpi cbg de preibisch pubs imagejpaper2010 pdf M D Abramoff P J Magelhaes and S J Ram Image processing with ImageJ Biophotonics Int 11 7 36 42 2004 Dirk V Arnold and Hans Gregor Beyer Noisy Local Optimization with Evolution Strategies Kluwer Academic Publishers Norwell MA USA 2002 Hans Georg Beyer Theory of Evolution Strategies Springer 2001 Stephen B Chrisholm Dirk V Arnold and Stephen Brooks Tone mapping by interactive evolution In Proceedings of the 11th Annual conference on Genetic and evolutionary computation GECCO 09 pages 515 522 New York NY USA 2009 ACM Hans Georg Beyer 2007 Evolution strategies Scholarpedia 2 8 1965 14
19. on at some point if he realizes that he has taken some sort of a dead end Appendix A User Manual Parameter Optimization Plug in What s the purpose of this plug in If you re working with complex macros that are calling plug ins and have various parameters for which you need to find good values then you often had to just try over and over again with different values until you got a result which was approximately what you wanted it to be This is where this plug in enters the stage Instead of setting different values in the macro by hand and applying the different macros on the same image stack you can now simply select the image and start the Parameter Optimization Plug in How does the plug in work i Fi Fie Edit Image Process Analyze Plugins Window Help First you need to make sure that the macro you want to run is written to a file and that you know its location Then you just open the image or stack you want to run the macro on like the cell colony image in the samples if Cell_Colony jpg Then navigate to the Plug ins menu and choose the Parameter Optimization plug in TP Pag r LA A E LOCI i m s Landmarks i s Analy Window Help Mosaic i JAJA Parameter Optimizaiton Shortcuts ME SEI Process utilities Registration New y eg 2 SPIM Registration Compile and Run Gr rintin b Now you should see the following window This is the configuration window where you set every
20. s tools There are for example various selection tools for rectangular elliptical etc shapes which can be combined and make it possible to apply other tools and operations just to the selected area 2 1 1 Core classes of ImageJ As a first step towards this thesis the current implemen tation of ImageJ has been analyzed in order to make use of the existing code as much as possible Unfortu nately it turned out to be much more difficult to do so than expected because of the architecture and the spe cific implementation of the core classes of ImageJ Many methods and fields in the core classes have default visi bility which makes them visible to other classes within the same package which unfortunately is of no use when developing a plug in which is being loaded by a differ ent class loader which makes it impossible to access any of those fields or methods no matter what package the accessing Class is in Lets never the less take a look at some of the core classes of ImageJ which matter for the implementation of this plug in IJ This class is the core or ImageJ and all its fields and methods are static It contains a lot of utility meth ods which are used by other classes and is the center of interaction with ImageJ ImagePlus The ImagePlus class is basically a wrapper for the im age It can either contain a single image or a whole stack of images Besides the image stack data it also contains some meta data on the image suc
21. se one could restrict the range of parameters for example for the makeRectangle method automatically because the we know the dimension of the image in which the selection is to be made Right now the plug in always uses doubles for the values when they are replaced in the macro But some methods only work for integers which can be circum vented by wrapping the values into a floor or ceil func tion In the current version of the plug in it is necessary to do this by hand before the macro is loaded A simple type selection in the selection table could avoid this and as mentioned before one could even automatically wrap the arguments of ImageJ methods for which the type is known Visualization of evolution path The values of the individuals of each generation are be ing recorded along with their assigned fitness and this data can be exported as a simple MATLAB file The standard user probably hasn t got any interests in this data but it could be quite useful for the analysis of the evolution strategy if an advanced user could see a visu alization of the evolution path during the optimization process History The values and fitnesses of individuals are kept in a his tory data structure which can then be used for the data export as mentioned in the previous paragraph One idea to improve the usability of the plug in could be to make this history available such that the user can take a few steps back and take an other directi
22. t stack setThreshold 0 0 250 0 TunMacroCConvertstackToBinary ot Save configuration Start optimization Figure 1 The Selection UI with a simple segmentation macro a Repeat button which can be used to re sample the current population without select ing ranking any images and it is only enabled as long as no images are currently selected and a Finish button which completes the optimization and calls the controller to ap ply the macro with the value of the selected image on the input image If more than one image is selected then the controller will first compute the mean of the selected values and then apply the macro with the mean to the input image The controls in the bottom left are synchronized with the displayed images such that a click zoom also changes the value of the zoom scroll bar The slice selection scroll bar is only displayed if the image is a stack such that the user only sees controls which he can actually use In order to display the images a customized Canvas subclass has been implemented which contains a few lines of code from the ImageCanvas class which ImageJ uses in the standard image displaying windows 4 2 Controller The main class of the plug in is the Parame ter Optimization Plugin class which implements the PluginFilter interface and the two internally used con troller interfaces Those internal interfaces are used to separate the controller from the user interface
23. ted There is for example now an interface ImageCan vas which can be implemented to draw images and that implementation could then be used to replace an exist ing implementation such as AW TImageCanvas which is the basic AWT implementation of said interface without any problems 3 Evolution Strategies Evolution Strategies are one subclass of Evolutionary Algorithms which are often used for optimization prob lems Evolutionary Algorithms mimic Darwin s natural selection principle in that they consist of a mutation re combination and selection operators which are applied to individuals of a generation which leads to a next gen eration One kind of Evolution Strategies are so called u A ES which consider a parent generation of u individuals from which an offspring generation of A individuals is created The selection operation is based on the fit ness which is associated with each individual and selects those individuals which have the largest fitness An indi vidual of the population consists of an object parameter value y a set of strategy parameters s and its fitness F y 4 The Parameter Plug in Optimization Before the plug in can be used the user needs to open the image on which the optimization is supposed to be performed The macro which is to be applied on the image needs to be written to a file in order to load it into the plug in Once these conditions are met the user can use the plug in Th
24. thing up for the optimization The first step is now to select the file which contains the macro code which is to be run on the image fyi Configuration Step 1 Selectthe file containing the macro code Strategy _ export history MATLAB Save Configuration Start optimization When you click on the select file button a standard file dialog should pop up By default it opens the macros folder of ImageJ for the macro selection 10 ek aloe i blur iim ConvertStackToBinary txt dummy 1 ijm listManagement txt File Name segmentijm Files of Type ImageJ Macro ijm txt In the next step you need to select the strategy which you want to use for the optimization In this example you have the choice between a simple A strategy with step size adaption and a CMA strategy Step 2 Select the strategy and optionaly further parameters Strategy Selectastratesy Select a strategy mu lambda sigma adapted CMA In the above step you can also select the checkbox for the export of a history file if you are interested in analyzing the optimization process Once you have selected a strategy you can configure the parameters which have been detected in the plug in You can enable or disable a parameter set its initial value and the bounds which it has to satisfy It is highly recommended that you insert the correct bounds for the parameter if they are known because this can have an imp
25. ulation Retrieves the current population from the strat egy The values of the population are then used to run the macro with the different values to produce the resulting images The plug in currently contains two strategy imple mentations One is a simple yw A Strategy and the other is a CMA implementation by Hansen and Alumni The simple u A Strategy just takes the u individ uals with highest fitness and computes the new mean of their values and their strategy parameters o The A new individuals are then sampled from a normal distri bution with the computed mean and step size o For each individual a strategy parameter g is sampled from a different distribution around o 5 Future Work ImageJ 2 0 As a major release of ImageJ namely ImageJ 2 0 is due in October 2011 it is suggested that the plug in will be adapted to follow the new design principles of the completely refactored version Note that due to the backwards compatibility of the new version which will basically include the old version as a library it should be possible to run the plug in with only minor changes or maybe even without any changes at all But the new version of ImageJ offers quite a few opportunities for a better integration especially on the side of the user interface More Strategies The current implementation of the plug in includes just 2 strategies It would be nice to have a larger collection of strategies such that if one strategy
26. works slow on a particular optimization problem then an other might be chosen which could work better Adding a new strategy currently requires a small change in the code of the main plug in class where the class of the new strategy needs to be added to the list of available strategies One possible improvement would be to implement a discovery procedure which checks the plug in directory and or some subdirectories for imple mentations of the EvolutionStrategy interface and then dynamically loads them into the plug in Macro Parser The current macro parser has quite a few limitations It is only able to detect parameters in function calls on the deepest level If a function call is nested in an other function call then only parameters in the nested func tion call can be detected This is due to the scanning procedure which was chosen for this simple parser as the focus was on finding parameters in function calls One improvement could be to just slightly modify the existing parser to make it consider the nesting func tions as well but a more suitable attempt to resolve the issue would be to completely rewrite the parser such that its output would be a parameterized AST instead of a sequence of static code in the form of a String in terleaved with Parameter objects Method awareness The comfort of the configuration for the user could be taken much further by explicitly analyzing in which method the parameter appears In this ca
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