@techreport{TR-IC-11-07,
   number = {IC-11-07},
   author  =  {Anderson Rocha and Tiago Carvalho and Siome Goldenstein
                   and Jacques Wainer},
   title  =  {{Points  of  Interest  and  Visual Dictionary for Retina
                   Pathology Detection}},
   month = {March},
   year = {2011},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 28 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Diabetic  retinopathy  (DR)  is  a  diabetes  development  that 
       affects  the  retina’s  blood  flow.  The  effect  of DR is the
       weakening of retina’s vessels, resulting on anything from small
       hemorrhages  to  the  growth  of  new  blood  vessels.  If left
       untreated,  DR eventually lead to blindness, and, in fact, this
       is  the  leading cause of blindness in persons in the age range
       of  20  to  74  years  in  developed countries. One of the most
       successful  means  for  fighting DR is early diagnosing through
       the  analysis  of ocular- fundus images of the human retina. In
       this  paper, we present a new approach to detect retina-related
       pathologies  from  ocular-  fundus images. Our work is intended
       for  an  automatic triage scenario, where patients whose retina
       is  considered  not-normal by the system will see a specialist.
       This  implies  that  automatic  screening  needs  an evaluation
       criteria that rewards low false negative rates, i.e., we should
       avoid  images  incorrectly  classified  as  normal  as  much as
       possible.  Our  solution  constructs a visual dictionary of the
       desired pathology’ important features and classifies whether an
       ocular-  fundus  image is normal or a DR candidate. We evaluate
       the methodology on hard exudates, deep hemorrhages, and microa-
       neurysms,  test  different parameter configurations, and demon-
       strate   the   robustness   and  reliability  of  the  approach 
       performing  cross-data-set  validation  (using both our own and
       other publicly available data-sets).
  }
}