Abstract
This paper presents an approach to biomedical image retrieval by detecting affine covariant regions and representing them with an invariant fuzzy feature space. These regions refer to a set of pixels or interest points which change covariantly with a class of transformations, such as affinity. A vector descriptor based on Scale-Invariant Feature Transform (SIFT) computed from the intensity pattern within the region. These features are then vector quantized to build a codebbok of keypoints. By mapping the interest points extracted from one image to the keypoints in the codebook, their occurrences are counted and the resulting histogram is called the “bag of keypoints” for that image. Images are finally represented in fuzzy feature space by spreading each region’s membership values through a global fuzzy membership function to all the keypoints in the codebook. The proposed feature extraction and representation scheme is not only invariant to affine transformations but also robust against quantization errors. A systematic evaluation of retrieval results on a heterogeneous medical image collection has shown around 15-20% improvement in precision at different recall levels for the proposed fuzzy feature-based representation when compared to individual color, texture, edge, and keypoint-based features.
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Rahman, M.M., Antani, S.K., Thoma, G.R. (2010). Biomedical Image Retrieval in a Fuzzy Feature Space with Affine Region Detection and Vector Quantization of a Scale-Invariant Descriptor. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_27
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DOI: https://doi.org/10.1007/978-3-642-17277-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
Online ISBN: 978-3-642-17277-9
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