Retrieval and Ranking of Biomedical Images using Boosted Haar Features
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Image Analysis and Interpretation 28/06/13 Melanie Torres Bisbal
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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.
Transcript of Retrieval and Ranking of Biomedical Images using Boosted Haar Features
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
