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Exploiting Evolutionary Approaches for Content-Based Medical Image Retrieval

机译：利用演化方法进行基于内容的医学图像检索

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Content-based image retrieval can be applied to assist radiologists to improve the efficiency and accuracy of interpreting the images. However, it presents some intrinsic problems. The two main problems are the so-called semantic gap that occurs due to the semantic interpretation of an image is still far to be reach, because it is based on the user's perception about the image. The other one is the dimensionality curse which leads to high dimensional feature vectors used to represent an image, where many of these features present some correlation. To mitigate these problems the paper presents a novel framework for content-based medical image retrieval joining feature selection techniques and image descriptors with optimization methods. It is capable to not only capture the user intention, but also to tune the feature selection process through the optimization method according to each user.
机译：基于内容的图像检索可以应用于协助放射科医生提高解释图像的效率和准确性。但是，它提出了一些内在的问题。两个主要问题是由于图像的语义解释仍是遥不可及的，因此出现了所谓的语义鸿沟，因为它基于用户对图像的感知。另一个是维数诅咒，它导致用于表示图像的高维特征向量，其中许多这些特征呈现出一定的相关性。为了减轻这些问题，本文提出了一种基于内容的医学图像检索的新框架，该框架结合了特征选择技术和具有优化方法的图像描述符。它不仅能够捕获用户意图，而且能够根据每个用户通过优化方法来调整特征选择过程。

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