Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases
Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases
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DOI：
10.1109/tip.2015.2493446
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发表时间：
2015-12-01
影响因子：
10.6
通讯作者：
Singh, Rajat Kumar
中科院分区：
文献类型：
--
作者：
Dubey, Shiv Ram;Singh, Satish Kumar;Singh, Rajat Kumar
本文提出了一种新的基于局部小波模式（LWP）的图像特征描述，用于描述医学计算机断层扫描（CT）图像的特征，以实现基于内容的CT图像检索。在所提出的工作中，LWP推导出的CT图像的每个像素通过利用中心像素的关系与局部相邻信息。与只考虑中心像素与相邻像素之间关系的局部二值模式不同，该方法首先利用相邻像素之间的局部小波分解关系，最后考虑其与中心像素之间的关系。提出了一种中心像素变换方案，使中心值范围与局部小波分解值范围相匹配。此外，引入的局部小波分解方案是中心对称的，适用于CT图像。本文的新奇之处在于：1）利用局部小波分解对局部邻域信息进行编码; 2）利用局部小波分解值和变换后的中心像素值计算LWP。我们测试了我们的方法在三个CT图像数据库的精度和召回率的性能。我们还比较了所提出的LWP描述符与其他国家的最先进的局部图像描述符，实验结果表明，所提出的方法优于其他方法的CT图像检索。
A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.