%ADASS_PROCEEDINGS_FORM%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % SAMPLE2.TEX -- ADASS XII (2002) ASP Conference Proceedings sample % paper with complicated markup. Based on ADASS XI (01) version. % % This is a comprehensive example, meaning that we have made use of each % of the capabilities of the LaTeX + the ASPCONF macro package that we think % you may need to use. If you want to see a "base-bones" sample paper, % take a look at sample1.tex. % % Much of the input will be enclosed by braces (i.e., { }). The % percent sign, "%", denotes the start of a comment; text after it % will be ignored by LaTeX. You might also notice in some of the % examples below the use of "\ " after a period; this prevents LaTeX % from interpreting the period as the end of a sentence and putting % extra space after it. % % You should check your paper by processing it with LaTeX. For % details about how to run LaTeX as well as how to print out the User % Guide, consult the README file. % % If you do not have access to the LaTeX software or a laser printer % at your site, you can still prepare your paper following the % instructions in the User Guide. In such cases, the editors will % process the file and make any necessary editorial adjustments. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % \documentclass[11pt,twoside]{article} % Leave intact \usepackage{adassconf} % If you have the old LaTeX 2.09, and not the current LaTeX2e, comment % out the \documentclass and \usepackage lines above and uncomment % the following: %\documentstyle[11pt,twoside,adassconf]{article} \begin{document} % Leave intact %----------------------------------------------------------------------- % Paper ID Code %----------------------------------------------------------------------- % Enter the proper paper identification code. The ID code for your % paper is the session number associated with your presentation as % published in the official conference proceedings. You can % find this number locating your abstract in the printed proceedings % that you received at the meeting or on-line at the conference web % site; the ID code is the letter/number sequence proceeding the title % of your presentation. % % This will not appear in your paper; however, it allows different % papers in the proceedings to cross-reference each other. Note that % you should only have one \paperID, and it should not include a % trailing period. % \paperID{P7-5} %%%% ID=P7-5 %----------------------------------------------------------------------- % Paper Title %----------------------------------------------------------------------- % Enter the title of the paper. % % EXAMPLE: \title{A Breakthrough in Astronomical Software Development} % % If your title is so long as to fill the page header when you print it, % then please supply a short form as a \titlemark. % % EXAMPLE: % \title{Rapid Development for Distributed Computing, with Implications % for the Virtual Observatory} % \titlemark{Rapid Development for Distributed Computing} % \title{Advances in Automated Algorithms For Morphological Classification of Galaxies Based on Shape Features} \titlemark{Automated Classification of Galaxies} %----------------------------------------------------------------------- % Authors of Paper %----------------------------------------------------------------------- % Enter the authors followed by their affiliations. The \author and % \affil commands may appear multiple times as necessary. List each % author by giving the first name or initials first followed by the % last name. Authors with the same affiliations should grouped % together. % % Try to limit the front matter to no more than three \author % commands. Group authors with the same affiliations. Too many % \author commands fills the first page of the paper with little % actual text. \author{S.\ Goderya and Jonathan D.\ Andreasen} \affil{Physics Department, Illinois State University, Normal, IL 61790-4560, Email: goderya@phy.ilstu.edu} \author{N.\ S.\ Philip} \affil{St. Thomas College, Kozhencherry, India} % Notice that some of these authors have alternate affiliations, which % are identified by the \altaffilmark after each name. The actual alternate % affiliation information is typeset in footnotes at the bottom of the % first page, and the text itself is specified in \altaffiltext commands. % There is a separate \altaffiltext for each alternate affiliation % indicated above. %----------------------------------------------------------------------- % Contact Information %----------------------------------------------------------------------- % This information will not appear in the paper but will be used by % the editors in case you need to be contacted concerning your % submission. Enter your name as the contact along with your email % address. \contact{Shaukat Goderya} \email{goderya@phy.ilstu.edu} %----------------------------------------------------------------------- % Author Index Specification %----------------------------------------------------------------------- % Specify how each author name should appear in the author index. The % \paindex{ } should be used to indicate the primary author, and the % \aindex for all other co-authors. You MUST use the following % syntax: % % SYNTAX: \aindex{LASTNAME, F. M.} % % where F is the first initial and M is the second initial (if % used). This guarantees that authors that appear in multiple papers % will appear only once in the author index. \paindex{Goderya, S.} \aindex{Andreasen, J. D.} \aindex{Philip, N. S.} %----------------------------------------------------------------------- % Author list for page header %----------------------------------------------------------------------- % Please supply a list of author last names for the page header. in % one of these formats: % % EXAMPLES: % \authormark{LASTNAME} % \authormark{LASTNAME1 \& LASTNAME2} % \authormark{LASTNAME1, LASTNAME2, ... \& LASTNAMEn} % \authormark{LASTNAME et al.} % % Use the "et al." form in the case of seven or more authors, or if % the preferred form is too long to fit in the header. \authormark{Goderya, Andreasen, \& Philip} %----------------------------------------------------------------------- % Subject Index keywords %----------------------------------------------------------------------- % Enter up to 6 keywords describing your paper. These will NOT be % printed as part of your paper; however, they will be used to % generate the subject index for the proceedings. There is no % standard list; however, you can consult the indices for past ADASS % proceedings (http://iraf.noao.edu/ADASS/adass.html). \keywords{galaxies: automated classification, data analysis, artificial neural networks} \begin{abstract} Among the many celestial objects in the universe, galaxies offer insights as to how the universe was formed and is continuing to develop. The morphological classification of galaxies is important just for this reason. The challenge lies in classifying the estimated billions of galaxies that are in the universe, a very small amount of which are now being studied by various sky survey like the Hubble Deep Field and the Sloan Digital Sky Survey. The automated procedure described here uses an image enhancing technique, segmentation, shape feature extraction and a supervised artificial neural network to classify the galaxies. When trained to classify galaxies as E/S0 or S, the network is able to learn 98.3\% of the galaxies correctly and identify 89.9\% of galaxy images in a test set. The major challenge is in the development of robust and automated segmentation schemes. With manually threshold images and Difference Boosting Neutral Networks we were able to achieve considerable success in developing a supervised classifier capable of sorting galaxies into subclasses. \end{abstract} \section{Introduction} When defining a galaxy's classification, a human classifier makes precise measurements. This process can take a fairly long time, and the human vision system is not especially suited for repetitive procedures such as extracting mathematical features of galaxies. Abraham et al. (1996), described how just two features of galaxies, the Asymmetry Index and Central Concentration of Light, can be used to distinguish between E/S0 and S galaxies. Properties such as these which are characteristic of galaxy types can be given to an Artificial Neural Network (ANN) (Angstenberger 1996 \& Mahonen 1995), which in turn can classify the galaxies. Our scheme for classification can be seen in Figure \ref{P7-5:fig1}. This paper focuses on all the parts of this scheme and demonstrates how an ANN is able to easily distinguish between E/S0 and S galaxies. We discuss important image processing techniques, shape extraction methods and ANN that allowed the implementation of this scheme. All the code has been implemented in C language on Linux platform, with the exception of interactive thresholding module that was performed by using Khoros Image Processing software \footnote{Khoros Software: http://www.khoral.com}. \begin{figure} \epsscale{0.50} \caption{Overall Automated Galaxy Classification Scheme.} \plotone{P7-5_f1.eps} \label{P7-5:fig1} \end{figure} \section{Image Processing} The images used in this study were obtained from the Zsolt Frei Catalog (Frei 1999) which contains approximately 113 different galaxy images. This database is often used as a benchmark for astronomical study. The images are carefully calibrated CCD images. The images downloaded from the Zsolt Frei website are processed in order to extract the necessary shape features. The first step is a window to window histogram equalization process which performs a local histogram equalization on each defined subsection of the image (Parker 1994). This is used primarily to enhance the arms of spiral galaxies. We have tried several other algorithms for image enhancement, however this algorithm produces the best results. After enhancement, the images are thresholded to convert the gray scale information to binary scale information. Two automated methods which rely on the image having a bi-modal histogram, Isodata algorithm (Ridler 1978) and the Triangle algorithm (Zack 1977) were tried. In the case of galaxy images, the Isodata technique tends to choose a threshold too high, turning too many of the necessary pixels that carry information about the galaxy into background pixels. The triangle algorithm is quite the opposite, choosing a threshold too low which includes too many pixels from the background that are not part of the galaxy. We adopted a midway approach by taking the average of the two values for our automated threshold value. In order to test the reliability of the shape feature extraction by automated techniques, we also decided to threshold the images interactively using Khoros Image Processing Software. After thresholding, the images are processed further to eliminate noise and other celestial objects via erosion. Next the galaxy's foreground is made uniformly white by eliminating black pixels in it via dilation. The images are then furthered filtered via a blob coloring routine which only keeps the largest connected foreground region in the image to obtain the isolated galaxy image. \section{Shape Extraction} Five different shape features which characterize a galaxy were extracted for each galaxy and were used as inputs to the ANN. Three of these parameters (Compactness, Bounding Rectangle to Perimeter ratio and Bounding Rectangle to Fill Factor ratio) have already been previously described (Goderya 2002). The Bounding Polygon, obtained by Jarvis' March method (Parker 1994), is implemented to compute the Bounding Polygon to Perimeter (BP-P) and Bounding Polygon to Fill Factor (BP-FF) ratios. The ratios involving the Bounding Polygon help to further distinguish between ellipticals and spirals, however, because of its tight binding nature it could also potentially be used to distinguish between different subclasses of spiral galaxies. Another benefit of constructing ratios is that the parameters are then invariant to galaxy size and rotation. In order to use the five features, we must implement an Artificial Neural Network which can see the non-linear effects and work in multi-dimensional space. \section{Artificial Neural Network} We experimented with two different types of networks. The first was the standard back-propagation algorithm. Two configurations 5$\times$2$\times$2 and 5$\times$2$\times$3 were tried. We calculated the Mathews correlation coefficients (MC) (Clark 1999). A value of 1 for MC indicates that the network perfectly identified every galaxy, a value of -1 indicates misclassification and a value of 0 indicates that the network is unstable. For our first configuration we find MC=0.944 for the training file and MC=0.690 for the test file. For the second configuration the Mathews coefficients for the training file are MC(E/S0)=0.884, MC(S)=0.871 and MC(SB)=0.819, while for the test file they are MC(E/S0)=0.54, MC(S)=0.280 and MC(SB)=0.184. It appears that the standard back-propagation algorithm does fairly well in classifying ellipticals and spiral galaxies. However it is not able to completely learn all the general characteristics in order to discriminate between simple spirals and barred spiral galaxies. The second network we tried is the Difference Boosting Neural Network \footnote{See P7.10 for details: Sajeeth N. Philip, "Optimal Selection of Training Data for the Difference Boosting Neural Networks" ADASS XIII, October 12-15, 2003}. Our results show 100\% classification accuracy with our training data thereby confirming the the fact that the technique we adopted fro extracting the parameters are valid and very effective. \section{Discussion} We believe that it is possible to develop robust and practical automated galaxy classifiers for large sky surveys. There is considerable amount of effort being put by different people in this type of work. It is also important to keep in mind that shape of the galaxy is dependent on red shift, band pass bias, dust scattering and low surface brightness. The challenge would be make classifiers that would take into consideration all these problems. To do this, it will be important to investigate more parameters that are physically meaningful and correlate with the astrophysical properties of the galaxies. This would result in a classification scheme that would be more elaborate and free of any deficiencies unlike the Hubble classification scheme. We have started looking into these directions and the results will be presented in future meetings. \acknowledgments This research has made use of the Zsolt Frei on-line catalog provided by the Department of Astrophysical Sciences at Princeton University. The authors would like to thank the Khoros Software people for providing their software to the academic community. Finally, thanks are due to the Department of Physics at Illinois State University for providing the computational and manuscript preparation facility for this research. Shaukat Goderya also acknowledges the financial support from American Astronomical Society, The ADASS organizing committee. \begin{references} \reference Abraham, R.\ G., Tanvir, N.\ R. Santiago, B.\ X. Ellis, R.\ S. Glazebrook, K. \& Van den Berg, S., 1996, \mnras, 279, L49 \reference Angstenberger, J., 1996, in Neural Networks \& Their Applications, edited by J. G. Taylor, (Wiley), pp. 143-152 \reference Clark, J. W., 1999, in Scientific Applications of Neural Networks: Springer Lecture Notes in Physics, edited by J. W. Clark, Lindenaw, T. and Ristig, M. L., Vol. 522, (Springer-Verlag, Berlin) \reference Goderya, S.\ N. \& Lolling, S.\ M., 2002, Morphological Classification of Galaxies Using Computer Vision and Artificial Neural Networks: A Computational Scheme, \apss, 279, pp. 377-387 \reference Mahonen, P.\ H. and Hakala, P.\ J., 1995, Automated Source Classification using a Kohonen Network, \apjl, 452, L77-L80 \reference Parker, J. R., 1994, Practical Computer Vision using C, (Wiley) \reference Ridler, T.\ W. and Calvard, S., 1978, Picture thresholding using an interactive selection method, IEEE Trans. on Systems, Man, and Cybernetics, SMC-8(8), 1264-1291 \reference Websource: Frei, Z., 1999, Zsolt Frei Galaxy Catalog. Retrieved 2002 from Princeton University, Department of Astrophysical Sciences Web site: http://www.astro.princeton.edu/~frei/catalog.htm \reference Zack, G.\ W., Rogers, W.\ E. and Latt, S.\ A., 1977, Automatic Measurement of Sister Chromatid Exchange Frequency, Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753 \end{references} \end{document}