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Medical Image Retrieval Based on Bidimensional Empirical Mode Decomposition

机译:基于双压力经验模式分解的医学图像检索

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An approach of medical image decomposition and texture feature extraction based on the Bidimensional Empirical Mode Decomposition (BEMD), which can decompose the image into a set of functions denoted Intrinsic Mode Functions (IMF) and a residue, was presented. Features extracted were the mean and standard deviation of the amplitude matrix, phase matrix and instantaneous frequency matrix of the IMFs and their Hilbert transformations. The extracted features were used for medical image retrieval. Moreover, according to the spatial relationship between local extrema points, a new boundary processing method based on clustering algorithm was proposed. In order to evaluate the proposed BEMD-based feature, we also presented a new multiscale fractal dimension feature. Preliminary comparison experimental results showed that the retrieval results of the BEMD-based feature were encouraged.
机译:呈现了一种基于BIDIMINIINION经验模式分解(BEMD)的医学图像分解和纹理特征提取方法,其可以将图像分解为一组函数,表示为内在模式功能(IMF)和残留物。提取的特征是IMFS及其希尔伯特转化的幅度矩阵,相矩阵和瞬时频率矩阵的平均值和标准偏差。提取的特征用于医学图像检索。此外,根据局部极值点之间的空间关系,提出了一种基于聚类算法的新边界处理方法。为了评估所提出的基于BEMD的功能,我们还提出了一个新的多尺度分形尺寸特征。初步比较实验结果表明,鼓励基于BEMD的特征的检索结果。

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