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GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING

GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING. Gorgi Kakasevski †, Aneta Buckovska *, Suzana Loskovska *, Ivica Dimitrovski * †EU, Faculty of Informatics, Skopje, Macedonia

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GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING

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  1. GRID ENABLED SYSTEM FORMEDICAL IMAGE GATHERING,ANALYZING, RETRIEVAL AND PROCESSING GorgiKakasevski†, AnetaBuckovska*, SuzanaLoskovska*, IvicaDimitrovski* †EU, Faculty of Informatics, Skopje, Macedonia *UKIM, Faculty of Electrotechnics and Information Technology, Skopje, Macedonia †gorgik@etf.ukim.edu.mk, *anbuc@etf.ukim.edu.mk

  2. GRID ENABLED SYSTEM FORMEDICAL IMAGE GATHERING,ANALYZING, RETRIEVAL AND PROCESSING Keyword-based and Content-based image retrieval Feature extraction Classifying Clustering Web image gathering Large-scale image processing

  3. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Images on the Web Commercial Web image search engines • Google Image Search • Yahoo Image Search • Altavista Image Search • Picsearch Content-based image retrieval • Image Rover • WebSEEK • ImageScape • Mirror • PicToSeek • Diogenes

  4. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Disadvantages • Large number of Web sites with images • Large number of Web image search engines • Many keywords and their combinations • Need of Web crawler for gathering images in DICOM format or gathering images from specific Web site • Lack of availability for searching by query image • By some criteria the search is difficult or impossible • Saving and organizing of images on local computer has to be done • manually • When the images are organized locally, searching by specific criteria is difficult (ex. Site where the image is placed, notices from the users…) • In order to classify (or cluster) images, different programs should be used

  5. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING System architecture

  6. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING System architecture

  7. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING System architecture

  8. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING System architecture

  9. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Web image gathering Dictionary of keywords Group 1 Group 2 Group 3 . . . kw1 kw2 kw3 . . . kwn

  10. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Web image gathering Dictionary of keywords Group 1 Group 2 Group 3 . . . kw1 kw2 kw3 . . . kwn Selecting parameters • Keyword permutations (r=1 to r=3): • r=1; (kw1, kw2), (kw1, kw3), ..., (kw1, kw10); • r=2; (kw1, kw2, kw3), (kw1, kw2, kw4), ..., (kw1, kw9, kw10); • r=3; (kw1, kw2, kw3, kw4), (kw1, kw2, kw3, kw5), ..., (kw1, kw8, kw9, kw10). • Other parameters: • image size, file type, color or b&w, number of images, type of safe • search, filter and location of search

  11. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Web image gathering

  12. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Feature extraction • MPEG-7 descriptors, standard set (ISO/IEC standard) • of descriptors that can be used to describe multimedia information: • - DominantColor • - ScalableColor • - ColorStructure • - ColorLayout • - EdgeHistogram • Skin type recognition • Color moments

  13. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Feature extraction • Download images • Feature extraction (eXperimentation Model - XM) DominantColor <?xml version='1.0' encoding='ISO-8859-1' ?> <Mpeg7 xmlns = "urn:mpeg:mpeg7:schema:2001" xmlns:xml = "http://www.w3.org/XML/1998/namespace" xmlns:xsi = "http://www.w3.org/2001/XMLSchema-instance" xmlns:mpeg7 = "urn:mpeg:mpeg7:schema:2001" xsi:schemaLocation = "urn:mpeg:mpeg7:schema:20 01 Mpeg7-2001.xsd"><DescriptionUnitxsi:type = "DescriptorCollectionType"><Descriptorxsi:type = "DominantColorType"><SpatialCoherency>0</SpatialCoherency> <Value><Percentage>2</Percentage> <Index>1 1 1 </Index></Value> <Value><Percentage>3</Percentage> <Index>28 27 28 </Index></Value> <Value><Percentage>2</Percentage> <Index>26 14 13 </Index></Value> <Value><Percentage>17</Percentage> <Index>11 9 7 </Index></Value> <Value><Percentage>5</Percentage> <Index>26 22 21 </Index></Value> </Descriptor></DescriptionUnit> </Mpeg7> EdgeHistogram <?xml version='1.0' encoding='ISO-8859-1' ?> <Mpeg7 xmlns = "http://www.mpeg7.org/2001/MPEG-7_Schema" xmlns:xsi = "http://www.w3.org/2000/10/XMLSchema-instance"><DescriptionUnit xsi:type = "DescriptorCollectionType"><Descriptor xsi:type = "EdgeHistogramType"><BinCounts>0 4 7 1 2 3 5 5 3 4 2 5 2 5 6 1 2 0 7 6 5 2 7 3 4 4 3 5 4 4 4 3 5 5 3 4 1 2 7 5 4 3 4 7 4 3 3 4 6 4 2 2 6 5 5 5 2 6 4 4 0 2 2 7 4 2 4 5 7 5 2 4 6 2 6 2 2 7 2 4 </BinCounts> </Descriptor> </DescriptionUnit> </Mpeg7>

  14. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Image mining • Clustering – Images are grouped into several clusters • - Take a keyword permutation; • - Gather images from the Web; • - Extract image features; • - Cluster first 100 images into m clusters; • - Add the other images into some of the clusters; • - For clusters which exceed the threshold l% from total number of images, make • a relation between the images in the cluster, the cluster and the keywords; • - Images from the clusters that do not exceed the threshold are placed into • one cluster; • EM method, WEKA (open source datamining software)

  15. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Image mining • Classifying – Each image is assigned into certain class or discarded • - The users define class; • - The users assign keywords to the class; • - The users assign certain images into classes; • - The users select descriptors which will describe the images into the class; • - Build classification model (k-nearest neighbor, IBk implementation in WEKA); • - Take a keyword permutation for certain class; • - Gather images from the Web; • - Extract image features; • - Assign classes to each image (for each descriptor); • - Place the image into the class which mostly appears. • IBk method, WEKA (open source datamining software)

  16. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Building relations • All information are returned to the server and relations between images, keywords, descriptors, classes and clusters are saved in the local database • When the all relations are saved in the local database the content-based and keyword-based image retrieval are enabled. Content-based image retrieval • Upload query image • Extract image features • Place the image into certain class/cluster (by use of classification model) • Retrieve images from the class where query image belong, sorted by similarity to the query image Keyword-based image retrieval • Enter keyword • Retrieve clusters and classes which correspond to entered keywords

  17. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Experimental results • The users must set system parameters (to create dictionary with group of • keywords, to define classes, to enter Web sites, to enter search • engines, to select descriptors which describe classes, to set Grid parameters • etc.)

  18. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Experimental results • Classification (ex. Malaria cells)

  19. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Experimental results • Grid execution • - SEEGRID infrastructure • - Sites: • c01.grid.etfbl.net; ce.ulakbim.gov.tr; ce01.info.uvt.ro; cluster1.csk.kg.ac.yu; tbit01.nipne.ro; • ce01.isabella.grnet.gr; seegrid2.fie.upt.al; grid01.elfak.ni.ac.yu; rti29.etf.bg.ac.yu; ce001.imbm.bas.bg.

  20. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING Conclusion and future work • Our system yields much better results unlike the commercial search engines • It does Web image gathering through search engines or specific sites • It cluster and classify images • Has a search service with CBIR • Searching of images from different criteria is very easy • Images with their thumbnails are saved in local database • Easily upgradable • Test the Grid infrastructure with multimedia data • Complex testing of the system • Modification of used algorithms and descriptors for feature extraction • Creating database with web sites where medical images can be found • Job optimization • Improvement in usage of replicas

  21. GRID ENABLED SYSTEM FOR MEDICAL IMAGE GATHERING, ANALYZING, RETRIEVAL AND PROCESSING THANKS! Q U E S T I O N S ?

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