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CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
 

CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR

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    CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR Presentation Transcript

    • CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR Savvas Chatzichristofis and Yiannis Boutalis Department of Electrical & Computer Engineering Democritus University of Thrace – Greece Signal Processing, Pattern Recognition and Applications SPPRA 2009 Presenter: Savvas A. Chatzichristofis
      • Compact Composite Descriptors (CCD) are global image descriptors capturing more than one features at the same time, in a very compact representation.
      Natural Images CEDD FCTH Artificial Images SpCL Medical Images BTDH CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Proposed Descriptor BTDH
      • This descriptor uses brightness and texture features in one compact vector.
      • Its size does not exceed 48 bytes per image.
      • This characteristic makes the descriptor appropriate for use in large medical image databases.
      CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Proposed Descriptor BTDH
      • To extract the brightness information, a fuzzy unit classifies the brightness value of the image’s pixels into 8 clusters.
      • The texture information embodied in the proposed descriptor is a Fuzzy approach of the Tamura Directionality Histogram.
      CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Descriptor Implementation CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Pre-Filtering unit
      • Auto brightness correction
      This method is partially inspired by the HVS (Human Vision System). It particularly adopts some of the shunting characteristics of the on-center off-surround networks, in order to define the response function for a new artificial center-surround network. CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Pre-Filtering unit
      • Edge enhancement
      A coordinate logic filter ( CLF ) ‘ OR’ is applied to the image. This filter enhances the edges of the image. Thus, it aims to help the texture information extraction unit to reach weaker texture alterations. CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Brightness information extraction unit
      • This unit purposes to classify the brightness of the pixels into 8 clusters using a fuzzy classification system.
      • The fuzzy system output is an 8 bin histogram.
      • The centre of these clusters was calculated using Gustafson - Kessel algorithm on a sample of 1000 (8 bit greyscale) medical images.
      Brightness Classification System CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR Fuzzy system output V(A) V(B) C(0) C(1) C(2) C(3) C(4) C(5) C(6) C(7) 3.18 22.68 54.00 90.13 125.80 162.57 202.25 243.64
    • Texture Information extraction unit
      • For every image block entered into the texture information extractor unit, an 16-bin histogram that describes the directionality of the image block is extracted.
      • Directionality histogram is a graph of local edge probabilities against their directional angle.
      • The fuzzy system output is an 16 bin histogram.
      Texture Classification System CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR Fuzzy system output V(B) V(A)
    • Descriptor Structure
      • The descriptor’s structure has n=16 regions determined by the Directionality Unit. Each Directionality Unit region contains m=8 individual regions defined by the Brightness Unit. Overall, the proposed descriptor contains n X m = 128 bins.
      CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Descriptor Implementation The procedure is repeated for all the Blocks. On the completion of the process, the descriptor's histograms bin values are normalized within the interval [0,1] To restrict the proposed descriptor storage requirements, the bin values of the descriptor are quantized for binary representation using a three bits/bin quantization. CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR 1. Auto brightness correction 2. Edge enhancement 3. Divided into 3X3 Blocks 4. Directionality Form n=2 5. Brightness Form m=3 6. Bin(19) is Activated
    • Quantization
      • For each image entered into the system, the proposed descriptor is extracted. This descriptor is separated into the 16 texture regions. The value of each bin of the descriptor is assigned to one of the values [0,7] according to the minimum distance of the value from one of the eight entries in the corresponding row of the quantization table.
      Bin(19)=3 CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR 000 001 010 011 100 101 110 111 3.6E-04 2.3E-03 4.5E-03 7.2E-03 1.1E-02 1.6E-02 3.1E-02 5.7E-01 3.2E-04 1.9E-03 3.7E-03 5.7E-03 8.4E-03 1.3E-02 2.4E-02 5.3E-01 3.3E-04 1.9E-03 3.5E-03 5.3E-03 7.6E-03 1.1E-02 1.6E-02 4.0E-02 3.7E-04 2.1E-03 4.1E-03 6.3E-03 8.8E-03 1.2E-02 2.0E-02 6.9E-02 3.2E-04 1.8E-03 3.4E-03 5.4E-03 7.9E-03 1.1E-02 1.7E-02 3.9E-02 3.1E-04 1.7E-03 3.3E-03 5.3E-03 7.8E-03 1.1E-02 1.8E-02 5.6E-02 3.6E-04 2.0E-03 3.9E-03 6.1E-03 8.6E-03 1.3E-02 2.1E-02 7.8E-02 3.6E-04 2.1E-03 4.0E-03 6.2E-03 9.0E-03 1.3E-02 2.4E-02 1.9E-01 3.7E-04 2.3E-03 4.4E-03 7.0E-03 1.0E-02 1.6E-02 2.9E-02 2.4E-01 4.2E-04 2.5E-03 4.9E-03 7.6E-03 1.1E-02 1.9E-02 5.3E-02 6.8E-01 3.6E-04 2.1E-03 3.9E-03 6.3E-03 9.3E-03 1.4E-02 2.7E-02 4.4E-01 3.7E-04 2.2E-03 4.3E-03 6.9E-03 1.1E-02 1.6E-02 3.5E-02 3.8E-01 3.3E-04 2.0E-03 3.8E-03 6.2E-03 9.4E-03 1.4E-02 2.8E-02 3.7E-01 2.7E-04 1.8E-03 3.6E-03 5.7E-03 8.9E-03 1.3E-02 2.6E-02 3.6E-01 3.0E-04 2.0E-03 3.9E-03 6.1E-03 8.8E-03 1.3E-02 2.6E-02 3.7E-01 3.2E-04 2.1E-03 4.2E-03 6.8E-03 1.0E-02 1.5E-02 3.1E-02 4.3E-01 Bin(19)=0.006 n=2 The final size of the proposed descriptor is
    • Similarity Measure
      • The similarity between the images was calculated using the non-binary Tanimoto Coefficient
      • Where x t is the transpose vector of X.
      • In the absolute congruence of the vectors the Tanimoto coefficient is equal to 1, while in the maximum deviation the coefficient tends to zero.
      CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Experiments
      • The proposed descriptor has been implemented and is available as open source library under GNU - General public License (GPL) in the image retrieval system img(Rummager) and the on line application img(Anaktisi).
      CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Experiments
      • To evaluate the performance of the proposed descriptor, the objective measure called ANMRR is used.
      • The experiments were carried out in a group of 5000 images with 120 queries.
      • A set of ground truth images that are most relevant to the query were identified. The ground truth data is a set of visually similar images.
      CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR Descriptor ANMRR Proposed Method Using Tanimoto 0.272 Proposed Method Using Jensen-Shannon 0.283 Proposed Method Using Euclidian 0.287 Tamura Directionality Histogram 0.321 Gabor Vector 0.328 MPEG7: Edge Histogram 0.381 Gray Value Histogram 0.448
    • Experiments on IRMA 2005 Medical Image Database
      • The IRMA database consists of 10000 annotated radiographs taken randomly from medical routine at the RWTH Aachen University Hospital-Germany. The images are separated into 9000 training images and 1000 test images. The images are subdivided into 57 classes.
      CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR Descriptor MAP Proposed Descriptor 28.1 Gabor vector 27.7 Gray value histogram 26.1 Gabor histogram 25.2 inv. feature histogram (mon.) 24.4 inv. feature histogram (relational) 24.1 LF patches signature 23.0 Tamura Directionality Histo. 21.6 LF SIFT global search 20.9 LF patches global 17.6 global texture feature 16.4 LF SIFT signature 10.9 MPEG7: edge histogram 10.9
    • Demonstration CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR
    • Conclusions
      • The experimental results showed that the proposed descriptor can be used for the retrieval of medical images more successfully than the Tamura Directionality Histogram .
      • The proposed method can be used as part of a broader retrieval system that uses more characteristics, replacing the Tamura Directionality Histogram .
      • Download the img(Rummager) application from
      • http://www.img-rummager.com
      Thank You Ευχαριστώ Πολύ CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMPOSITE DESCRIPTOR