Post on 27-Jan-2015
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Image Analysis and Interpretation 28/06/13 Melanie Torres Bisbal
¡ 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
¡ 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
¡ 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
¡ 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)
¡ 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
¡ 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
¡ Efficient computation from Integral Image ¡ In this paper implemented using the Intel OpenCV @:
¡ The features are:
¡ 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:
¡ 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