Shinde Amita, Rahulkar Amol, Patil Chetankumar
1Instrumentation and Control, College of Engineering Pune, Pune, India.
Electrical and Electronics Engineering, National Institute of Technology Goa, Farmagudi, India.
Biomed Eng Lett. 2019 May 6;9(3):387-394. doi: 10.1007/s13534-019-00112-0. eCollection 2019 Aug.
This paper presents a new class of local neighborhood based wavelet feature descriptor (LNWFD) for content based medical image retrieval (CBMIR). To retrieve images effectively from large medical databases is backbone of diagnosis. Existing wavelet transform based medical image retrieval methods suffer from high length feature vector with confined retrieval performance. Triplet half-band filter bank (THFB) enhanced the properties of wavelet filters using three kernels. The influence of THFB has employed in the proposed method. First, triplet half-band filter bank (THFB) is used for single level wavelet decomposition to obtain four sub-bands. Next, the relationship among wavelet coefficients is exploited at each sub-band using 3 × 3 neighborhood window to form LNWFD pattern. The novelty of the proposed descriptor lies in exploring relation between wavelet transform values of pixels rather than intensity values which gives more detail local information in wavelet sub-bands. Thus, proposed feature descriptor is robust against illumination. Manhattan distance is used to compute similarity between query feature vector and feature vector of database. The proposed method is tested for medical image retrieval using OASIS-MRI, NEMA-CT, and Emphysema-CT databases. The average retrieval precisions achieved are 71.45%, 99.51% of OASIS-MRI and NEMA-CT databases for top ten matches considered respectively and 55.51% of Emphysema-CT database for top 50 matches. The superiority in terms of performance of the proposed method is confirmed by the experimental results over the well-known existing descriptors.
本文提出了一种用于基于内容的医学图像检索（CBMIR）的新型局部邻域小波特征描述符（LNWFD）。从大型医学数据库中有效检索图像是诊断的核心。现有的基于小波变换的医学图像检索方法存在特征向量长度大且检索性能受限的问题。三重半带滤波器组（THFB）使用三个内核增强了小波滤波器的特性。该方法中采用了THFB的影响。首先，使用三重半带滤波器组（THFB）进行单级小波分解以获得四个子带。接下来，在每个子带使用3×3邻域窗口利用小波系数之间的关系来形成LNWFD模式。所提出描述符的新颖之处在于探索像素的小波变换值之间的关系而非强度值，这在小波子带中给出了更详细的局部信息。因此，所提出的特征描述符对光照具有鲁棒性。使用曼哈顿距离来计算查询特征向量与数据库特征向量之间的相似度。所提出的方法使用OASIS - MRI、NEMA - CT和肺气肿 - CT数据库进行医学图像检索测试。对于分别考虑的前十匹配项，所实现的平均检索精度在OASIS - MRI和NEMA - CT数据库中分别为71.45%、99.51%，对于肺气肿 - CT数据库前50匹配项为55.51%。实验结果证实了所提出方法在性能方面优于著名的现有描述符。
