In traditional text based medical image retrieval system, it is hard to find visually similar images in large medical image database. Content-based image retrieval is developed to retrieve similar images and it is based on visual attributes describing the content of an image. Developing a method for content based medical image retrieval is a challenging task. This paper proposed a new method for content-based medical image retrieval based on salient regions and deep learning. The proposed method includes two stages: an offline task to extract local object features and an online task for content-based image retrieval in database. In first stage, we extract local object features of medical image depending on shape, texture and intensity, and features extracted by deep learning applied in saliency of decomposition. Secondly, we make online task for content-based image retrieval in database. The user gives query image as an input and the system will retrieve n top most similar images by similarity comparison with bag of code words feature values obtained in the first stage. Evaluation of the proposed method is based on Precision and Recall values. Our dataset includes 5 groups of medical images with their quality varying from low to high. With the best medical image quality group, the accuray can be 91.61% for Precision and 89.61% for Recall. Comparing the average values with others methods, the results of the proposed method are more than 2 to 5% better.

在传统的基于文本的医学图像检索系统中，很难在大型医学图像数据库中找到视觉上相似的图像。基于内容的图像检索被开发来检索相似的图像，并且它基于描述图像内容的视觉属性。开发用于基于内容的医学图像检索的方法是一项艰巨的任务。本文提出了一种基于显着区域和深度学习的基于内容的医学图像检索新方法。所提出的方法包括两个阶段：用于提取局部对象特征的离线任务和用于在数据库中基于内容的图像检索的在线任务。在第一阶段，我们根据形状，纹理和强度提取医学图像的局部对象特征，并通过深度学习将特征提取出来以进行分解。第二，我们为数据库中基于内容的图像检索提供在线任务。用户提供查询图像作为输入，系统将通过与在第一阶段获得的代码字特征值袋进行相似度比较来检索n个最相似的图像。对所提出方法的评估基于Precision和Recall值。我们的数据集包括5组医学图像，其质量从低到高不等。在最佳医疗图像质量组中，Precision可以达到91.61％，Recall可以达到89.61％。将平均值与其他方法进行比较，所提出的方法的结果要好2％到5％。用户提供查询图像作为输入，系统将通过与在第一阶段获得的代码字特征值袋进行相似度比较来检索n个最相似的图像。对所提出方法的评估基于Precision和Recall值。我们的数据集包括5组医学图像，其质量从低到高不等。在最佳医疗图像质量组中，Precision可以达到91.61％，Recall可以达到89.61％。将平均值与其他方法进行比较，所提出的方法的结果要好2％到5％。用户提供查询图像作为输入，系统将通过与在第一阶段获得的代码字特征值袋进行相似度比较来检索n个最相似的图像。对所提出方法的评估基于Precision和Recall值。我们的数据集包括5组医学图像，其质量从低到高不等。在最佳医疗图像质量组中，Precision可以达到91.61％，Recall可以达到89.61％。将平均值与其他方法进行比较，所提出的方法的结果要好2％到5％。