An Efficient Multiscale Wavelet Local Binary Pattern for Biomedical Image Retrieval

Nath, Vijay Kumar; Hatibaruah, Rakcinpha; Hazarika, Deepika

doi:10.1007/978-981-10-6890-4_47

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Figure 12 is the result of wavelet transform algorithm combined with Hausdorff distance, and Figure 13 is the result of Tamura texture feature combined with Hausdorff distance, Figure 14 is the result of wavelet transform algorithm combined with Euclidean distance, and Figure 15 is the result of Tamura texture feature combined with Euclidean distance. The retrieved results of the 10th, 8th, 6th, 5th, 5th, and 4th image in the brain MRI database are, respectively, given in Figures 10,11,12,13,14,and 15. According to the experimental results, it is obvious that the Tamura texture feature and wavelet transform algorithm combined with Hausdorff distance is priority. The Tamura texture feature and wavelet transform algorithm combined with Euclidean distance is lower.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Medical Image Retrieval Approach by Texture Features Fusion Based on Hausdorff Distance

Sun

Zhang

et al. 2018

Mathematical Problems in Engineering

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Medical images play an important role in the hospital diagnosis and treatment, which include a lot of valuable medical information. Manually annotated viewing is obviously not effective in managing large amounts of medical imaging data. Hence it is an important task to establish an efficient and accurate medical image retrieval system. In this paper, a medical image retrieval approach based on Hausdorff distance combining Tamura texture features and wavelet transform algorithm is proposed. The combination of Tamura texture features and wavelet transform features can extract the texture features of medical images more effectively, and Hausdorff distance can reflect the overall similarity of medical image feature set. In this paper, 6 group experiments of brain MRI database and the lung CT database were conducted separately. Experiments show that the proposed approach has higher accuracy than a single feature texture algorithm and is also higher than the approach of Tamura texture features and wavelet transform features combined with Euclidean distance.

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Section: Experimental Results and Analysismentioning

confidence: 99%