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To improve the performance of content-based medical image retrieval, herein an algorithm which makes use of latent semantic indexing (LSI) technology on gastroscopic image retrieval is proposed. First extract imagepsilas color histogram and color autocorrelogram of low-level features, and then use normalizing, term weighting and singular value decomposition to realize low-level features mapping into...
This paper casts about for effective retrieving technique from imagepsilas texture, shape and semantic information, in allusion to chest radiographs, the Gray level co-occurrence matrix with Gray-Grads co-occurrence matrix in texture feature and Hu moments with Zernike moments in shape feature are compared. A prototype system is implemented. With experiment, we find the Gray-Grads co-occurrence matrix...
Content-based medical image retrieval is getting more and more importance in aspect of clinical assistant diagnose. This paper in allusion to gastroscopic images, make use of latent semantic indexing technology to implement image retrieval which based on its semantic information. First extract image's histogram of neighborhood color moments of low-level features, and then use normalizing, term weighting...
In allusion to sternum images, herein we describe a system which supports image retrieval by content. Attention is focused on high-level semantic information representation of medical images. Then a feature fusion algorithm of medical image retrieval using high-level semantic information combining low-level features is presented. A prototype system which supports query by example is designed and implemented...
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