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EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
An introduction to boosting and leveraging
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Robust Real-Time Face Detection
International Journal of Computer Vision
Digital Image Processing (3rd Edition)
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Results of an Adaboost Approach on Alzheimer's Disease Detection on MRI
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Intravascular ultrasound images vessel characterization using Adaboost
FIMH'03 Proceedings of the 2nd international conference on Functional imaging and modeling of the heart
Gallbladder boundary segmentation from ultrasound images using active contour model
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
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This article presents the application of the AdaBoost method to recognise gallbladder lesions such as lithiasis and polyps in USG images. The classifier handles rectangular input image areas of a specific length. If the diameter of areas segmented is much greater than the diameter expected on the input, wavelet approximation of input images is used. The classification results obtained by using the AdaBoost method are promising for lithiasis classification. In the best case, the algorithm achieved the accuracy of 91% for lithiasis and of 80% when classifyingpolyps, as well as the accuracy of 78.9% for polyps and lithiasis jointly.