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Bone Scintigraphy Retrieval Using SIFT-Based Fly Local Sensitive Hashing

Bone scintigraphy is widely used in the diagnosis of bone diseases. With the expansion of bone scan database, there has been a increasingly demand for efficient technique to retrieve from the database. However, previous image retrieval methods mainly focused on nature images, and properties of nature images usually differ greatly from bone scintigraphy. In this paper, we apply a data-independent hashing method called Fly Local Sensitive Hashing (FLSH) for bone scan images retrieval. It's a computational strategies for solving approximate similarity search problem inspired by the olfactory system of fruit fly. To increase robustness for transformation and rotation, we also introduce SIFT features into retrieval procedure. SIFT is robust to various image transformation including scaling and rotating. A 128 dimension SIFT vector is used to represent the input images. We evaluate FLSH on our bone scan dataset containing 8091 images. The experimental results demonstrate that images retrieved by FLSH preserve more Euclidean similarity of query, and achieve higher precision compared to traditional LSH method. It indicates the effectiveness of SIFT-based FLSH in the area of bone scintigraphy retrieval.