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Segmentation of Complex Images using SOM for Clustering


... Resonance Images (MRIs) of the Human Brain using Self-Organizing Maps (SOMs) ... 22nd Asian Conference on Remote Sensing, Singapore (2001) ... – PowerPoint PPT presentation

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Title: Segmentation of Complex Images using SOM for Clustering

Segmentation of Complex Images using SOM for
Final Project for CS 7650 Instructor Dr. Heng-Da
  • Manasi Datar
  • April 22, 2004

Presentation Outline
  • Core Concepts
  • Segmentation
  • Segmentation and clustering
  • Self-Organizing Map (SOM)
  • Clustering and SOM
  • Segmentation and SOM
  • My Idea )
  • Segmentation of complex images using SOM for
  • Approach not novel… but applied to a new domain
  • Application pre-processing of images for
    knowledge discovery

  • What is it?
  • A process to distinguish meaningful objects in
    an image from the background
  • How is it done?
  • threshold techniques
  • edge-based methods
  • region-based techniques
  • connectivity-preserving relaxation-based methods

Segmentation Techniques
  • Threshold techniques
  • Make decisions based on local pixel information
  • Are effective when the intensity levels of the
    objects fall squarely outside the range of levels
    in the background
  • Spatial information is ignored
  • Edge-based techniques
  • Center around contour detection
  • Weakness connecting together broken contour
  • Prone to failure in the presence of blurring

Segmentation Techniques… contd
  • Region-based Techniques
  • Connected regions grouping neighboring pixels of
    similar intensity levels
  • Region merging adjacent regions are merged under
    some criterion
  • Overstringent criteria fragmentation
  • Lenient blurred boundaries overlooked
  • Connectivity-preserving Relaxation-based
  • Active contour model
  • start with some initial boundary shape and
    iteratively modify according to some energy
  • Getting caught in a local minimum is a risk

Segmentation and Clustering
  • Clustering
  • data set as a group of clusters collections of
    data points that belong together
  • Segmentation as clustering
  • represent an image in terms of clusters of pixels
    that belong together

Clustering Algorithms
  • Make each point a separate cluster
  • Until the clustering is satisfactory
  • Merge the two clusters with the smallest
    inter- cluster distance
  • End
  • Agglomerative
  • Construct a single cluster containing all points
  • Until the clustering is satisfactory
  • Split the cluster that yields the two
    components with the largest inter-cluster
  • End
  • Divisive

Clustering for Segmentation
  • K-means algorithm
  • A natural objective function can be obtained by
    assuming that we know there are k clusters, where
    k is known.
  • Each cluster is assumed to have a center we
    write the center of the ith cluster as ci. The
    ith element to be clustered is described by a
    feature vector xi
  • We now assume that elements are close to the
    center of their cluster, yielding the objective
  • Note if the allocation of points to clusters is
    known, it is easy to compute the best center for
    each cluster

Clustering for Segmentation… contd
  • K-means Algorithm
  • Choose k data points to act as cluster centers
  • Until the cluster centers are unchanged
  • Allocate each data point to cluster whose center
    is nearest
  • Now ensure that every cluster has at least one
    data point possible techniques for doing this
    include supplying empty clusters with a point
    chosen at random from points far from their
    cluster center
  • Replace the cluster centers with the mean of the
    elements in their clusters
  • End

Clustering for Segmentation
  • k-means algorithm example
  • On the left, an image of mixed vegetables, which
    is segmented using k-means to produce the images
    at center and on the right.
  • Each pixel is replaced with the mean value of its
  • In the center, a segmentation obtained using only
    the intensity information.
  • At the right, a segmentation obtained using color
  • Each segmentation assumes 5 clusters.

Structure of the SOM
  • The SOM arranges feature vectors according to
    their internal similarity, creating a continuous
    topological map of the input space.
  • In this topological map, the vectors that are
    similar in the input space are grouped together,
    or clustered.
  • The map is a two dimensional grid of processing
    elements, called neurons.
  • Each processing element is connected to each
    element of the input layer and to its neighboring
    processing elements.
  • The connections between the layers (weights)
    represent the strength of the connection.

Working of the SOM
  • The training algorithm is based on competitive
  • only winning processing elements are allowed to
    adjust weights or learn
  • After presenting an input vector, the processing
    elements response, or activation, is calculated
    by multiplying the input vector by a set of
  • The processing element with the highest
    activation is declared winner
  • The winning processing element and its neighbors
    are allowed to adjust their weights, while the
    remaining ones are left unchanged
  • The neighborhood is the group of processing
    elements adjacent to the winning neuron
  • During training the size of the neighborhood is
    reduced to stabilize the effect of the input
    vectors in the topological map
  • The result of the training is a centroid
    (representative vector) for each cluster that
    minimizes the quantization error
  • The quantization error is defined as the square
    root of the sum of the squared difference of
    individual input patterns and calculated centroids

SOM Demo
  • I meant to show this in Matlab, but
    unfortunately this laptop does not have it
  • So, here is a Java version instead… demo

SOM for Clustering
Input image
Topological map
SOM for clustering
  • Properties of SOM
  • Classification
  • Visualization
  • Can be used as a clustering tool )
  • Will help in gaining useful information form
    segmenting complex images.

? My Idea ?
  • Segmentation of Landsat-7 TM images using SOM
  • Landsat information
  • images freely available online
  • Main reference
  • Automatic Segmentation of Magnetic Resonance
    Images (MRIs) of the Human Brain using
    Self-Organizing Maps (SOMs) by Evangelou

Proposed Mechanism
Landsat 7 image (raw data)
Rectified Landsat 7 image
Conventional Classifier
Classified image
Classified image
Post processing
Comparison and Assessment
Some Results…
Original Image
Segmented Image
In Conclusion…
  • Results
  • Comparison based on
  • Time complexity
  • Effectiveness
  • SOM will be helpful in identifying intensity
    patterns that exist in image data
  • Knowledge of such patterns will prove useful
    interpretation of such images

  • Evangelou, I.E. Automatic Segmentation of
    Magnetic Resonance Images (MRIs) of the Human
    Brain using Self-Organizing Maps (SOMs). MS
    Thesis, Clarkson University, Potsdam, NY, USA
  • Fayyad, U.M. Piatetsky-Shapiro, G. Smyth, P.
    and R. Uthurusamy, R., (Eds.). Advances in
    Knowledge Discovery and Data Mining. AAAI Press,
    the MIT Press, CA, USA (1996).
  • Rangsanseri, Y et al. Multispectral Image
    Segmentation using ART1/ART2 Neural Networks.
    22nd Asian Conference on Remote Sensing,
    Singapore (2001)
  • Solaiman, B Mouchot, M.C. A Comparative Study
    of Conventional and Neural Network Classification
    of Multispectral Data. IGARSS94, Pasadena, USA.
  • Vesanto, J. Data Mining Techniques Based on the
    Self-Organizing Map. MSc Thesis, Helsinki
    University of Technology, Espoo, Finland (1997)
  • Goldberg, M. Shlien, S. A clustering scheme for
    multi-spectral images. IEEE Transactions on
    Systems, Man and Cybernetics SMC 8. (1978)
  • Theiler, J. Gisler, G. A contiguity-enhanced
    k-means clustering algorithm for unsupervised
    Multispectral image segmentation. Proc SPIE 3159.
  • Landsat project info http//

  • Jain, A. K. Dubes, R. C. Algorithms for
    Clustering Data. Prentice-Hall Inc., Englewood
    Cliffs, NJ, USA. (1988)
  • Haykin, S. Neural Networks A Comprehensive
    Foundation, Pearson Education (Singapore) Pte.
    Ltd. (2001)
  • Brown, D. A. Craw I. Lewthwaite, J. Towards
    Core Image Processing with Self-Organizing Maps.
  • Su, M. Clustering Analysis, Department of
    Computer Science and Information Engineering,
    National Central University (2003) available at
  • Mather P. Computer Processing of Remotely Sensed
    Images, John Wiley Sons, Inc. New York, NY, USA
  • Paola, J. D. Schowenderdt, R. A. A review and
    analysis of back propagation neural networks for
    classification of remotely sensed multi-spectral
    imagery, International Journal of Remote Sensing.
  • Landsat data http//
  • SOM toolbox for Matlab http//

!!! Thank you for your patience !!! Your
comments and suggestions are valuable… please
send them to me )