A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH by uyk41809

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									A NEW ALGORITHM FOR CONTENT-BASED
  REGION QUERY IN DATABASES WITH
          MEDICAL IMAGES

  Dumitru Dan BURDESCU, Liana STANESCU
     Faculty of Automation, Computers and Electronics
               University of Craiova, Romania
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




•   This article presents an original method of implementation of the
    color set back-projection algorithm that is one of the most efficient
    method of automated detection of color regions from an image.
•   The detected regions are then used in the content-based region
    query.
•   The query is realized on one or more regions, having into
    consideration the color feature.
•   The efficiency of the method was studied by means of a number of
    experiments effectuated with the help of a software system realized
    for this purpose, on a collection of medical images collected with an
    endoscope.
•   The new method for the implementation of the algorithm is
    compared with the traditional one, not only from the point of view of
    the execution time, but also from the point of view of the retrieval
    process quality
  A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Introduction
• In present there are a variety of activity fields in which massive
   databases with gray level or color images were created. One of
   these domains is the medical field.
• For querying these imagistic collections, the traditional simple
   methods based on text are not sufficient. This is due to the fact that
   the information from images and in general from multimedia data, is
   not structured and in consequence the utilization of some attributes
   for describing its content is not possible.
• From this appears the big necessity of using alternative methods for
   retrieving with accuracy and rapidity the relevant information, from a
   massive imagistic collection, such that the user’s query could be
   satisfied.
• These techniques are known under the name of content-based
   visual information retrieval and they were centered in the attention of
   a lot of researchers, in the last years.
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Introduction


•   In the medical field, images, and especially digital images, are
    produced and used for diagnostics and therapy in large amounts.
•   In some medical areas, hundreds or even thousands of images are
    daily produced.
•   A big part of them are color images, like the images collected with
    the endoscope’s help, so to take into consideration the color
    characteristic in the content–based visual retrieval presents
    importance.
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Introduction

There are argued some important reasons that explain the need for
  supplementary methods for image retrieval:

•   in the process of taking clinical decision, it may be very important to
    specify an image like query or some regions like query regions and
    to retrieve those images from the database that are most similar to
    the specified image query or region query, together with the afferent
    diagnoses.
•   the education and the research activity can be improved by using
    the access visual methods.
•   the visual characteristics allow not only the retrieving of the patients
    having the same disease, but also the cases where the visual
    similitude exists, but the diagnosis differs.
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Introduction

•   In a content-based region query, the images are compared on their
    regions.
•   For realizing the content-based region query on a database with
    medical images, it is necessary an automated algorithm for
    detecting the color regions, significant for the diagnosis.
•   It was chosen the color set back-projection algorithm, introduced
    initially by Swain and Ballard and then developed in the research
    projects at Columbia University, in the content-based visual retrieval
    domain. This technique provides the automated extraction of regions
    and the representation of their color content.
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Introduction

The extraction system for color regions has four steps:

•   The image transformation, quantization and filtering (the
    transformation from RGB to HSV color space and the quantization at
    166 colors)
•   Back-projection of binary color sets
•   The labeling of regions
•   The extraction of region features ( the binary color set, the area, the
    centroid coordinates and the minimum bounding rectangle
    coordinates)
  A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




A method for the implementation of the color set back-projection
    algorithm
•   In the first implementation of the color set back-projection
    algorithm (Method1), the image is read in a .bmp format.
•   Each pixel from the initial image is transformed in HSV format and
    quantized. At the end of this processing there are obtained the
    global histogram and the color set of the image.
•   On the matrix that memorizes only the quantized colors from 0 to
    165 it is applied a 5x5 median filter, which has the role of
    eliminating the isolated points.
•   Having the HSV quantized matrix it is possible to begin the
    process of regions extraction presented above.
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




•   In the first implementation (Method1), it may be observed that this process is in fact a
    depth – first traversal, described in pseudo-cod in the following way:
•   procedure FindRegions (Image I, colorset C) is:
•   InitStack(S)
•   Visited = ∅
•    for *each node P in the I do
•       if *color of P is in C then
•           PUSH(P)
•           Visited ← Visited ∪ {P}
•           while not Empty(S) do
•               CrtPoint <- POP()
•               Visited ← Visited ∪ {CrtPoint}
•               For *each unvisited neighbor S of
                CrtPoint do
•                             if *color of S is in C then
•                           Visited ← Visited ∪ {S}
•                           PUSH(S)
•                 * Output detected region
   A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Proposition 1
• The total running time of a call of the procedure FindRegions (Image I,
   colorset C) is O(m2*n2), where “m” is the width and “n” is the height of image.
Proof
• Recall that the number of pixels of image is m*n, where “m” is the width and
   “n” is the height of image. Observe next, that the first loop FOR of the
   algorithm is executed at most once for each pixel P in the image. Hence,
   the total time spent in this loop is O(n*m). The WHILE loop processes the
   stack S for each pixel which has the same color of its neighbor. The inner
   loop FOR processes the pixels of an unvisited neighbor. So, the total time
   spent in these loops is O(m*n), because are processed all pixels of image at
   most once. The result of the previous statements is that the total running
   time of this procedure is O(m2*n2).
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




A new method for the implementation of the color set back-
   projection algorithm

•   In the new original implementation of the algorithm (Method2), the
    image pixels were arranged into hexagons.
•   The edge of a hexagon has a certain number of pixels (3, 4, 5).
•   Only the pixels which correspond to the vertices of the hexagons
    with an established edge are taken into consideration.
•   The image is viewed as a graph not as a pixel matrix. The vertices
    represent the pixels and the edges represent neighborhoods
    between pixels.
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




A new method for the implementation of the color set back-projection
   algorithm

For each binary set is executed:

•   the graph is inspected until it is found the first vertex having the color
    from the color set
•   starting from this vertex, there are found all the adjacent vertices
    having the same color
•   the process will continue in the same manner for each neighbor,
    until there are not found vertices having the same color
•   it is verified if the detected region satisfies the imposed thresholds;
    in affirmative case, the region is labeled and introduced in the
    database
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES

•   This process of regions extraction from a graph is in fact a breadth – first traversal, described in pseudo-cod in the following
    way
•   procedure construct_graph (Image I, Graph g, Edge edge) is :
•     for * i->0,width/edge
•       for * j->0;height/edge
•
•            if (i mod 3==0)
•       *if(jmod2==0)
•
              g[i][j]=I[edge*i][edge*j+edge-1]
•            *if(jmod2==1)
•                                                       g[i][j]=I[edge*i][edge*j+edge+2]
•
•
•          if (i mod 3==1)
•              * if(j mod 2==0)
•
              g[i][j]=I[edge*i-1][edge*jedge]
•            * if(j mod 2==1)
•
    g[i][j]=I[edge*i-1][edge*j+edge*2]
•
•          if (i mod 3==2)
•                                                 *if(j mod 2==0)
•
    g[i][j]=I[edge*i-2][edge*j+edge-1]
•                                                 *if(j mod 2==1)
•
              g[i][j]=I[edge*i2][edge*j+edge+2]
•
•        //end for * j->0
•       *output the graph g
•   //end for * i->0
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




•   procedure FindRegions (Graph G, colorset C) :
•     InitQueue(Q)
•     Visited = ∅
•     for *each node P in the G do
•       if *color of P is in C then
•           PUSH(P)
•           Visited ← Visited ∪ {P}
•           while not Empty(q) do
•             CrtPoint <- POP()
•             Visited ← Visited ∪ {CrtPoint}
•             for *each unvisited neighbor Q of
•
                                            CrtPoint do
•                       if *color of Q is in C then
•                               Visited ← Visited ∪ {Q}
•                      PUSH(Q)
•
•        *output-detected region
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Proposition 2

• The total running time of a call of the procedure FindRegions (Graph
  G, colorset C) is O(n2), where “n ” is the number of nodes of graph
  attached to an image.
Proof

•   Observe that the first FOR loop of the algorithm is executed at most
    once for each node of the graph. Hence, the total time spent in this
    loop is O(n). The WHILE loop processes the queue Q for each node
    which has the same color of its neighbor. The inner loop FOR
    processes the nodes of an unvisited neighbor. So, the total time
    spent in these loops is O(n), because are processed all nodes of
    graph at most once.
•   From previous statements results that the total running time of this
    procedure is O(n2).
  A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




  Taking into account that the color information of each region is stored
  as a color binary set, the color similitude between two regions may
  be computed either with the quadratic distance between color sets,
  or with Hamming distance between color sets.
  Here, there was used the quadratic distance between binary sets ‘sq’
  and ‘st’ that is given by the following equation :

    M-1 M-1
d1= Σ Σ (sq[m0] –st[m0])am0,m1(sq[m1]-st[m1])                     (1)
   m0=0 m1=0
    A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Experiments and results

•   For testing the efficiency of the new method and for comparing the
    two methods of implementation of the color set back-projection
    algorithm, there have been made some experiments over the
    medical images collection.
•   For Method2 the hexagon edge can be equal to 3, respective 4.
•   For each query, the images from the databases were inspected and
    relevance was assigned to them (1- relevant, 0 – irrelevant) and the
    retrieval effectiveness using recall and precision was recorded
  A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Experiment 1
  The image with detected color regions using Method2 and edge=3; Region6
  (representing the sick area) marked for the content-based region query
  A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Experiment 1
  The retrieved images using Method2 and edge equal to 3, for the Region6
  as query region. All the images are relevant.
   A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Experiment 1
   The graphic of the retrieving efficiency for Method1 and Method2 with the hexagon
   edge equal to 3
   A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Experiment 2:
   The image with detected color regions using Metod2 and edge=3; Region8, Region9,
   Region10 marked for the content-based region query
   A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Experiment 2:
   The retrieved images Method2 with hexagon edge equal to 3, for the Region8,
   Region9, Region10 as query regions. Only the last image is irrelevant.
   A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Experiment 2:
   The graphic of the retrieving efficiency for the two methods, with the hexagon edge
   equal to 3,for Method2.
   A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL IMAGES




Conclusion
• This article presents an original method of implementation of the color set
  back-projection algorithm, algorithm that allows the automated detection of
  the color regions from a color medical image. The detected regions are then
  used in the content-based region query.
• The very good results obtained in the effectuated experiments indicate the
  fact that each of the two implementations methods (Method1 and Method2)
  of the color set back-projection algorithm can be used in the processing of
  the content – based visual query.
• The experiments show that the results obtained with the Method2 and
  edge=3 are closer in quality with those obtained with Method1.
• The advantage of the second method (Method 2 with edge equal to 3) is
  given by the fact that for detecting the color regions it is not necessary the
  pixel-by-pixel image traversal, but only the pixels arranged in the vertices of
  a hexagon with edge equal to 3 pixels.
• If the processing time of the Method 1 is O(m2*n2) (m - is the width and n - is
  the height of image), the processing time for the Method 2 presented here is
  O(n2) (n - is the number of nodes of graph attached to an image).
A NEW ALGORITHM FOR CONTENT-BASED REGION QUERY IN DATABASES WITH MEDICAL




  Software system for content-based region query

  http://193.226.37.211:8080/Medical/index.HTM

								
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