0
Image	
  Analysis	
  and	
  Interpretation	
  
28/06/13	
  
Melanie	
  Torres	
  Bisbal	
  
¡  Ranking	
  and	
  retrieval	
  of	
  medical	
  images	
  
¡  Retrieve	
   information	
   from	
   a	
   database	
 ...
¡  Extracting	
  and	
  understanding	
  the	
  structure	
  and	
  
characteristics	
  of	
  medical	
  images	
  is	
  ...
¡  Since	
  80	
  has	
  been	
  a	
  research	
  topic	
  
¡  But	
  the	
  field	
  of	
  biomedical	
  imaging	
  is	
...
¡  One	
  of	
  the	
  biggest	
  problems	
  is	
  precisely	
  recover	
  
the	
   characteristics	
   that	
   define	
...
¡  Haar-­‐like	
  features,	
  proposed	
  by	
  Viola	
  and	
  Jones	
  
¡  Two	
  advantatges:	
  
§  The	
  system	...
¡  The	
   key	
   steps	
   to	
   construct	
   the	
   algorithm	
  
described	
  in	
  the	
  paper	
  are:	
  
§  E...
¡  Efficient	
  computation	
  from	
  Integral	
  Image	
  
¡  In	
   this	
   paper	
   implemented	
   using	
   the	
 ...
¡  In	
  the	
  training	
  phase	
  boosting	
  applied	
  to	
  each	
  
separate	
  category	
  to	
  find	
  the	
  we...
¡  Chandan	
  K.	
  Reddy	
  and	
  Fahima	
  A.	
  Bhuyan,	
  
Retrieval	
  and	
  Ranking	
  of	
  Biomedical	
  Images...
Retrieval and Ranking of Biomedical Images using Boosted Haar Features
Retrieval and Ranking of Biomedical Images using Boosted Haar Features
Retrieval and Ranking of Biomedical Images using Boosted Haar Features
Retrieval and Ranking of Biomedical Images using Boosted Haar Features
Retrieval and Ranking of Biomedical Images using Boosted Haar Features
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Retrieval and Ranking of Biomedical Images using Boosted Haar Features

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Presentation and summary of the paper:
Retrieval and Ranking of Biomedical Images using Boosted Haar Features, Chandan K. Reddy and Fahima A. Bhuyan

Abstract of the paper:
Abstract— Retrieving similar images from large repository of heterogeneous biomedical images has been a difficult research task. In this paper, we develop a retrieval system that uses Haar features as its weak classifiers and builds strong training models using the adaboost algorithm. Our system is trained for each image category separately and the final boosted model is stored during the training phase. In the test phase, the most similar images for a given query image are computed using these boosted models. The main advantages of the proposed system are (1) cheap computation of the most relevant features for each image category and (2) fast retrieval of similar images for a given query image. Using performance metrics such as sensitivity and specificity, our results demonstrate the robustness and accuracy of the proposed system.

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Transcript of "Retrieval and Ranking of Biomedical Images using Boosted Haar Features"

  1. 1. Image  Analysis  and  Interpretation   28/06/13   Melanie  Torres  Bisbal  
  2. 2. ¡  Ranking  and  retrieval  of  medical  images   ¡  Retrieve   information   from   a   database   that   the   images  are  similar  is  much  difficult   ¡  Paper   proposed   a   new   algorithm   with   the   following  concepts:   §  Integral  image   §  Haar  like  features   §  Adaboost  
  3. 3. ¡  Extracting  and  understanding  the  structure  and   characteristics  of  medical  images  is  challenging   ¡  A   typical   radiology   department   generates   between  100,000  to  10  million  images  per  year   ¡  Applications  in  the  detection  and  classification   ¡  Also  specific  applications  in  image  retrieval  with   pulmonary  nodules   ¡  Retrieve  and  sort  the  information  in  real  time  
  4. 4. ¡  Since  80  has  been  a  research  topic   ¡  But  the  field  of  biomedical  imaging  is  in  a  very   early  stage   ¡  Images  to  train  and  test  the  proposed  algorithm   are  taken  from  the  database  of  IRMA  (Image   Retrieval  in  Medical  Applications)   ¡  Use  a  subset  of  images  to  train  the  different   categories  and  remove  Haar-­‐like  features  to   build  specific  models  
  5. 5. ¡  One  of  the  biggest  problems  is  precisely  recover   the   characteristics   that   define   the   visual   similarity   of   the   anatomical   structure   of   the   different  categories   ¡  Generally   have   used   co-­‐occurrence   matrix   of   gray,  Gabor  filters,  etc..   ¡  In   this   paper   are   based   more   on   reducing   the   time  a  given  question  (query)  
  6. 6. ¡  Haar-­‐like  features,  proposed  by  Viola  and  Jones   ¡  Two  advantatges:   §  The  system  can  be  used  for  a  wide  range  of  biomedical   image  retrieval  as  a  tumor   §  Recovery  time  it  takes  significament  is  low  in   comparison  to  other  methods  
  7. 7. ¡  The   key   steps   to   construct   the   algorithm   described  in  the  paper  are:   §  Efficient  extraction  of  simple  wavelets  (Haar)   §  Train  the  boosting  algorithm  applied  to  each  category   §  Calculate  the  closest  similarity  given  a  query  
  8. 8. ¡  Efficient  computation  from  Integral  Image   ¡  In   this   paper   implemented   using   the   Intel   OpenCV  @:   ¡  The  features  are:  
  9. 9. ¡  In  the  training  phase  boosting  applied  to  each   separate  category  to  find  the  weights  and  the   weak  classifiers   ¡  For  a  query  in  the  test  phase  the  system  will   identify  the  class  it  belongs  to  and  return  the  top   ranking  images  repository   ¡  To  look  at  the  results  is  calculated:  
  10. 10. ¡  Chandan  K.  Reddy  and  Fahima  A.  Bhuyan,   Retrieval  and  Ranking  of  Biomedical  Images   using  Boosted  Haar  Features   http://ieeexplore.ieee.org/xpl/login.jsp? tp=&arnumber=4696834&url=http%3A%2F %2Fieeexplore.ieee.org%2Fxpls %2Fabs_all.jsp%3Farnumber%3D4696834    
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