Content-based Similarity Retrieval Research Articles

Effective and efficient content-based similarity retrieval of large lung CT images based on WSSLN model.

The in-depth combination and application of AI technology and medical imaging, especially high- definition CT imaging technology, make accurate diagnosis and treatment possible. Retrieving similar CT image(CI)s to an input one from the large-scale CI database of labeled diseases is helpful to realize a precise computer-aided diagnosis. In this paper, we take lung CI as an example and propose progressive content-based similarity retrieval(CBSR) method of the lung CIs based on a Weakly Supervised Similarity Learning Network (WSSLN) model. Two enabling techniques (i.e., the WSSLN model and the distance- based pruning scheme) are proposed to facilitate the CBSR processing of the large lung CIs. The main result of our paper is that, our approach is about 45% more effective than the state-of-the-art methods in terms of the mean average precision(mAP). Moreover, for the retrieval efficiency, the WSSLN-based CBSR method is about 150% more efficient than the sequential scan.

An effective and efficient framework of content-based similarity retrieval of large CT image sequences based on WSLEN model

An effective and efficient framework of content-based similarity retrieval of large CT image sequences based on WSLEN model

Progressive detail-content-based similarity retrieval over large lung CT image database based on WSLN model

With the explosive growth of the medical images, the high-resolution CT image(CI) data is of great value for medical research as well as a clinical diagnosis. Finding CIs similar to the input one from the large- scale CI database can effectively assist physicians to diagnose. The state-of-the-art content-based similarity retrieval of CIs often ignores the effect of image details on retrieval accuracy, and the retrieval accuracy and efficiency are usually not satisfactory. To address this challenge, in this paper, we take lung CI as an example and propose a progressive Detail-Content-based Similarity Retrieval (DCSR) of the lung CIs based on a Weakly Supervised deep Learning Network(WSLN) model. Two enabling techniques (i.e., the WSLN model and the DIndex scheme) are proposed to facilitate the DCSR processing of the large lung CIs. The experimental dataset is from three public datasets. Extensive experiments show that the WSLN-based DCSR method is about 45% more effective than the state-of-the-art methods in terms of the mean average precision(MAP). Meanwhile, the retrieval efficiency of the DIndex scheme is about 200% higher than that of sequential retrieval.

RevHashNet: Perceptually de-hashing real-valued image hashes for similarity retrieval

Image hashing has attracted increasing popularity in recent years. Some off-the-shelf image hashing methods are able to generate more compact and robust hashes for fast indexing and content-based similarity retrieval. However, the ability to infer original image contents from their real-valued image hashes has seldom been examined. Inherited from cryptographic hashing for image privacy protection, general image hashing is supposed to be a non-revertible function. Should there be a way to revert (or perceptually reconstruct) images from the corresponding real-valued image hashes? This paper explores the feasibility of perceptually image hashing reversion, and fill this gap by proposing a deep learning based framework, entitled RevHashNet. Given real-valued image hashes from certain image hashing methods, the proposed RevHashNet can automatically reconstruct perceptually similar images with respect to the original ones with high visual quality. Experiments and simulations on real image datasets support the de-hashing effectiveness of the proposed RevHashNet.

Content-based retrieval in picture archiving and communication systems.

A COntent-Based Retrieval Architecture (COBRA) for picture archiving and communication systems (PACS) is introduced. COBRA improves the diagnosis, research, and training capabilities of PACS systems by adding retrieval by content features to those systems. COBRA is an open architecture based on widely used health care and technology standards. In addition to regular PACS components, COBRA includes additional components to handle representation, storage, and content-based similarity retrieval. Within COBRA, an anatomy classification algorithm is introduced to automatically classify PACS studies based on their anatomy. Such a classification allows the use of different segmentation and image-processing algorithms for different anatomies. COBRA uses primitive retrieval criteria such as color, texture, shape, and more complex criteria including object-based spatial relations and regions of interest. A prototype content-based retrieval system for MR brain images was developed to illustrate the concepts introduced in COBRA.

Color and spatial feature for content-based image retrieval

Most of the currently available image database systems provide a text-based retrieval function called keyword retrieval, where users specify `keywords' such as titles, attributes, and categories of themes. But many times it is not easy for users to specify suitable keywords for a particular retrieval. Besides, building a large image database with complete description of contents is a very difficult task. In this paper, we present a content-based retrieval method which obviates the need to describe certain contents of an image to be archived and retrieved. The proposed method computes image features automatically from a given image and they can be used to archive and/or retrieve images. These features are based on color and its spatial distribution information in an image. We have also developed a similarity measure to compare the color and spatial feature similarity of two images. This technique has been developed and tested for content-based similarity retrieval of images on two databases consisting of: (i) 100 test images and (ii) 800 actual trademarks images. The experimental results show a high efficiency of retrieval.