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Local bit-plane decoded convolutional neural network features for biomedical image retrieval

机译：用于生物医学图像检索的本地位平面解码卷积神经网络特征

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摘要

Biomedical image retrieval is a challenging problem due to the varying contrast and size of structures in the images. The approaches for biomedical image retrieval generally rely on the feature descriptors to characterize the images. The feature descriptor of query image is compared with the descriptors of images from the database, to find the best matches. Several hand-crafted feature descriptors have been proposed so far for biomedical image retrieval by exploiting the local relationship of neighboring image pixels. It is observed in the literature that the local bit-plane decoded features are well suited for this retrieval task. Moreover, in recent past, it is also observed that the convolutional neural network-based features such as AlexNet, Vgg16, GoogleNet and ResNet perform well in many computer vision-related tasks. Motivated by the success of the deep learning-based approaches, this paper proposes a local bit-plane decoding-based AlexNet descriptor (LBpDAD) for biomedical image retrieval. The proposed LBpDAD is computed by max-fusing the ReLU operated feature maps of pre-trained AlexNet at a particular layer, obtained from the original and local bit-plane decoded images. The proposed approach is also compared with Vgg16, GoogleNet and ResNet models. The experiments on the proposed method over three benchmark biomedical databases of different modalities such as MRI, CT and microscopic show the efficacy of the proposed descriptor.
机译：生物医学图像检索是由于图像中结构的变化和大小的挑战性问题。生物医学图像检索的方法通常依赖于特征描述符来表征图像。将查询图像的特征描述符与来自数据库的图像的描述符进行比较，以查找最佳匹配。到目前为止已经提出了几种手工制作的特征描述符，以通过利用相邻图像像素的局部关系来提出生物医学图像检索。在文献中观察到局部位平面解码特征非常适合该检索任务。此外，在最近的过去，还观察到，诸如AlexNet，VGG16，Googlenet和Reset等卷积神经网络的特征在许多计算机视觉相关任务中表现良好。基于深度学习的方法的成功激励，本文提出了一种基于位平面解码的基于位平面解码的AlexNet描述符（LBPDAD），用于生物医学图像检索。通过最大限度地融合从原始层和局部位平面解码图像获得的特定层的预训练亚历网的Relu操作的特征映射来计算所提出的LBPDAD。该建议的方法也与VGG16，Googlenet和Reset模型进行了比较。对三个基准测试生物医学数据库的提出方法的实验，例如MRI，CT和微观的不同模式，显示了所提出的描述符的功效。

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