Abstract
Bone scintigraphy is widely used to diagnose bone tumor and metastasis. Accurate bone scan image retrieval is of great importance for tumor metastasis diagnosis. In this paper, we propose a framework to retrieve images by integrating the techniques of texture feature extraction and supervised hashing with kernels (KSH). We first use a filter bank to extract the texture features. Then KSH is used to train a set of hashing functions with constructed features, which can convert images to hashing codes. We can obtain the most similar retrieval images by comparing Hamming distance of these hashing codes. We evaluate the proposed framework quantitatively on the testing dataset and compare it with other methods.
This research is partly supported by NSFC, China (No: 61375048), National Key R&D Program of China (No. 2019YFB1311503), Committee of Science and Technology, Shanghai, China (No. 19510711200).
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Xu, H., Qiao, Y., Gu, Y., Yang, J. (2020). Combining Filter Bank and KSH for Image Retrieval in Bone Scintigraphy. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_17
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