Pengyuan Li

Brain CT Image Similarity Retrieval Method Based on Uncertain Location Graph

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

Medical Image Retrieval Method Based on Relevance Feedback

The current image retrieval systems are almost based on content, and facing the main problem of semantic gap between low level features and high level semantic. So the relevance feedback technology is used to solve this problem. In this paper, we propose a medical image retrieval system based on relevance feedback framework. In the framework, Region of Interest (ROI) is extracted in the preprocessing as the semantic information of medical images, and then the Genetic Algorithm is designed for ROI clustering. According to user’s feedback information, the Diverse Density algorithm proposed in the Multiple Instance Learning Framework is adopted to capture user’s real intention and realize effectively medical image relevance. Experimental results show that our algorithm has higher precision and recall ratio.

A medical image retrieval method based on texture block coding tree

Abstract Content-based medical image retrieval (CBMIR) has been widely studied for computer aided diagnosis. Accurate and comprehensive retrieval results are effective to facilitate diagnosis and treatment. Texture is one of the most important features used in CBMIR. Most of existing methods utilize the distances between matching point pairs for texture similarity measurement. However, the distance based similarity measurements are of low tolerance to slight texture shifts, which result in an excessive sensitivity. Furthermore, with the increase of the number of texture points, their time complexity is in explosive growth. In this paper, a new medical image retrieval model is presented based on an iterative texture block coding tree. The corresponding methods for coarse-grained and fine-grained similarity matching are also proposed. Moreover, a multi-level index structure is designed to enhance the retrieval efficiency. Experimental results show that, our methods are of high efficiency and appropriate tolerance on slight shifts, and achieve a relative better retrieval performance in comparison of other existing methods.

Medical Image Clustering Algorithm Based on Graph Model

The algorithm of medical image is an important part of special field image clustering. There are many problems of technical aspects and the problem of specific area, so that the study of this direction is very challenging. The existing algorithm of clustering has requirement about shape and density of data object, and it cannot get a good result to the application of medical image clustering. In view of the above problem and under the guidance of knowledge of medical image, at first, detects texture from image, and T-LBP method is put forward. Then divides the preprocessed image into many spaces, and calculates LBP value of spaces. At last build spatial sequence LBP histogram. Based on the LBP histogram, the clustering method of MCST is proposed. The result of experiment shows that there are good result at time complexity and clustering result in the algorithm of this paper.

Compound image segmentation of published biomedical figures

Motivation Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. Results In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. Availability and implementation The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. Contact shatkay@udel.edu. Supplementary information Supplementary data are available online at Bioinformatics.

A novel model for medical image similarity retrieval

Finding the similar medical images from medical image database can help doctors diagnose based on the cases before. However the similarity retrieval for medical images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph is presented for medical image modeling and similarity retrieval. Then a scheme for uncertain location graph retrieval is introduced. Furthermore, an index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on medical images similarity retrieval with higher accuracy and efficiency.

Identifying Experimental Evidence in Biomedical Abstracts Relevant to Drug-Drug Interactions

Drug-drug interactions (DDIs) may cause significant adverse effects. As prescribing multiple drugs becomes increasingly common, it is necessary to verify potential interactions among drugs that are used at the same time. Likely DDIs can be identified with higher confidence if supporting experimental evidence is provided. Such information is usually published in biomedical literature. While current retrieval and classification methods can identify publications related to DDIs, not all articles that discuss DDIs contain experimental evidence. A publication that does present evidence typically contains sentences conveying information about specific experimental methods and their results. A classifier that can readily identify such sentences can be useful for obtaining explicit and reliable information concerning DDIs. In this work, we develop two text classifiers to distinguish scientific sentence-fragments bearing experimental evidence from fragments that do not present such evidence. We focus on a corpus of text containing biomedical abstracts related to drug interactions. The classifiers are trained and tested on a manually curated set of sentence-fragments in these abstracts. Our experiments demonstrate a high level of performance (at least 89% precision and recall) suggesting the applicability of these classifiers toward improving retrieval of reliable information pertaining to drug interactions.

Extracting Figures and Captions from Scientific Publications

Figures and captions convey essential information in scientific publications. As such, there is a growing interest in mining published figures and in utilizing their respective captions as a source of knowledge. There is also much interest in image captioning systems that can automatically generate captions for images, whose training requires large datasets of image-caption pairs. Notably, the first fundamental step of obtaining figures and captions from publications is neither well-studied nor yet well-addressed. In this paper, we introduce a new and effective system for figure and caption extraction, PDFigCapX. Unlike current methods that extract figures by handling raw encoded contents of PDF documents, we separate text from graphical contents and utilize layout information to detect and disambiguate figures and captions. Files containing the figures and their associated captions are then produced as output to the end-user. We test PDFigCapX on both a previously used generic dataset and on two new sets of publications within the biomedical domain. Our experiments and results show a significant improvement in performance compared to the state-of-the-art, and demonstrate the effectiveness of our approach. Our system will be available for use at: https://www.eecis.udel.edu/~compbio/PDFigCapX.

Segmenting Compound Biomedical Figures into Their Constituent Panels

Many of the figures in biomedical publications are compound figures consisting of multiple panels. Segmenting such figures into constituent panels is an essential first step for harvesting the visual information within the biomedical documents. Current figure separation methods are based primarily on gap-detection and suffer from over- and under-segmentation. In this paper, we propose a new compound figure segmentation scheme based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentations are inaccurate. Experiments and results comparing the performance of our method to that of other top methods demonstrate the effectiveness of our approach.