The medGIFT Group in ImageCLEFmed 2013

This article presents the participation of the medGIFT groupin ImageCLEFmed 2013. Since 2004, the group has participated in themedical image retrieval tasks of ImageCLEF each year. There are fourtypes of tasks for ImageCLEFmed 2013: modality classi cation, image{based retrieval, case{based retrieval and a new task on compound gureseparation. The medGIFT group participated in all four tasks. MedGIFTis developing a system named ParaDISE (Parallel Distributed ImageSearch Engine), which is the successor of GIFT (GNU Image FindingTool). The alpha version of ParaDISE was used to run the experimentsin the competition. The focus was on the use of multiple features incombinations with novel strategies, i.e, compound gure separation formodality classi cation or modality ltering for ad{hoc image and case{based retrieval

Overview of the ImageCLEF 2013 medical tasks

In 2013, the tenth edition of the medical task of the Image-CLEF benchmark was organized. For the first time, the ImageCLEFmedworkshop takes place in the United States of America at the annualAMIA (American Medical Informatics Association) meeting even thoughthe task was organized as in previous years in connection with the otherImageCLEF tasks. Like 2012, a subset of the open access collection ofPubMed Central was distributed. This year, there were four subtasks:modality classification, compound figure separation, image–based andcase–based retrieval. The compound figure separation task was includeddue to the large number of multipanel images available in the literatureand the importance to separate them for targeted retrieval. More com-pound figures were also included in the modality classification task tomake it correspond to the distribution in the full database. The retrievaltasks remained in the same format as in previous years but a largernumber of tasks were available for image–based and case–based tasks.This paper presents an analysis of the techniques applied by the tengroups participating 2013 in ImageCLEFmed

Comparing Fusion Techniques for the ImageCLEF 2013 Medical Case Retrieval Task

Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case-based retrieval approaches. This paper focuses on the case-based task and adds results of the compound figure separation and modality classification tasks. Several fusion approaches are compared to identify the approaches best adapted to the heterogeneous data of the task. Fusion of visual and textual features is analyzed, demonstrating that the selection of the fusion strategy can improve the best performance on the case-based retrieval task

Case-based fracture image retrieval

Purpose: Case-based fracture image retrieval can assist surgeons in decisions regarding new cases by supplying visually similar past cases. This tool may guide fracture fixation and management through comparison of long-term outcomes in similar cases. Methods: A fracture image database collected over 10years at the orthopedic service of the University Hospitals of Geneva was used. This database contains 2,690 fracture cases associated with 43 classes (based on the AO/OTA classification). A case-based retrieval engine was developed and evaluated using retrieval precision as a performance metric. Only cases in the same class as the query case are considered as relevant. The scale-invariant feature transform (SIFT) is used for image analysis. Performance evaluation was computed in terms of mean average precision (MAP) and early precision (P10, P30). Retrieval results produced with the GNU image finding tool (GIFT) were used as a baseline. Two sampling strategies were evaluated. One used a dense 40×40 pixel grid sampling, and the second one used the standard SIFT features. Based on dense pixel grid sampling, three unsupervised feature selection strategies were introduced to further improve retrieval performance. With dense pixel grid sampling, the image is divided into 1,600 (40×40) square blocks. The goal is to emphasize the salient regions (blocks) and ignore irrelevant regions. Regions are considered as important when a high variance of the visual features is found. The first strategy is to calculate the variance of all descriptors on the global database. The second strategy is to calculate the variance of all descriptors for each case. A third strategy is to perform a thumbnail image clustering in a first step and then to calculate the variance for each cluster. Finally, a fusion between a SIFT-based system and GIFT is performed. Results: A first comparison on the selection of sampling strategies using SIFT features shows that dense sampling using a pixel grid (MAP = 0.18) outperformed the SIFT detector-based sampling approach (MAP = 0.10). In a second step, three unsupervised feature selection strategies were evaluated. A grid parameter search is applied to optimize parameters for feature selection and clustering. Results show that using half of the regions (700 or 800) obtains the best performance for all three strategies. Increasing the number of clusters in clustering can also improve the retrieval performance. The SIFT descriptor variance in each case gave the best indication of saliency for the regions (MAP = 0.23), better than the other two strategies (MAP = 0.20 and 0.21). Combining GIFT (MAP = 0.23) and the best SIFT strategy (MAP = 0.23) produced significantly better results (MAP = 0.27) than each system alone. Conclusions: A case-based fracture retrieval engine was developed and is available for online demonstration. SIFT is used to extract local features, and three feature selection strategies were introduced and evaluated. A baseline using the GIFT system was used to evaluate the salient point-based approaches. Without supervised learning, SIFT-based systems with optimized parameters slightly outperformed the GIFT system. A fusion of the two approaches shows that the information contained in the two approaches is complementary. Supervised learning on the feature space is foreseen as the next step of this stud

Evaluating performance of biomedical image retrieval systems - an overview of the medical image retrieval task at ImageCLEF 2004-2013

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created

Fusion Techniques in Biomedical Information Retrieval

For difficult cases clinicians usually use their experience and also the information found in textbooks to determine a diagnosis. Computer tools can help them supply the relevant information now that much medical knowledge is available in digital form. A biomedical search system such as developed in the Khresmoi project (that this chapter partially reuses) has the goal to fulfil information needs of physicians. This chapter concentrates on information needs for medical cases that contain a large variety of data, from free text, structured data to images. Fusion techniques will be compared to combine the various information sources to supply cases similar to an example case given. This can supply physicians with answers to problems similar to the one they are analyzing and can help in diagnosis and treatment planning

Shangri-La: a medical case-based retrieval tool

Large amounts of medical visual data are produced in hospitals daily and made available continuously via publications in the scientific literature, representing the medical knowledge. However, it is not always easy to find the desired information and in clinical routine the time to fulfil an information need is often very limited. Information retrieval systems are a useful tool to provide access to these documents/images in the biomedical literature related to information needs of medical professionals. Shangri–La is a medical retrieval system that can potentially help clinicians to make decisions on difficult cases. It retrieves articles from the biomedical literature when querying a case description and attached images. The system is based on a multimodal retrieval approach with a focus on the integration of visual information connected to text. The approach includes a query–adaptive multimodal fusion criterion that analyses if visual features are suitable to be fused with text for the retrieval. Furthermore, image modality information is integrated in the retrieval step. The approach is evaluated using the ImageCLEFmed 2013 medical retrieval benchmark and can thus be compared to other approaches. Results show that the final approach outperforms the best multimodal approach submitted to ImageCLEFmed 2013

Medical image modality classification using discrete Bayesian Networks

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes