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

Overview of the 2005 cross-language image retrieval track (ImageCLEF)

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Four tasks were offered in the ImageCLEF track: a ad-hoc retrieval from an historic photographic collection, ad-hoc retrieval from a medical collection, an automatic image annotation task, and a user-centered (interactive) evaluation task that is explained in the iCLEF summary. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main fndings

Image Area Reduction for Efficient Medical Image Retrieval

Content-based image retrieval (CBIR) has been one of the most active areas in medical image analysis in the last two decades because of the steadily increase in the number of digital images used. Efficient diagnosis and treatment planning can be supported by developing retrieval systems to provide high-quality healthcare. Extensive research has attempted to improve the image retrieval efficiency. The critical factors when searching in large databases are time and storage requirements. In general, although many methods have been suggested to increase accuracy, fast retrieval has been rather sporadically investigated. In this thesis, two different approaches are proposed to reduce both time and space requirements for medical image retrieval. The IRMA data set is used to validate the proposed methods. Both methods utilized Local Binary Pattern (LBP) histogram features which are extracted from 14,410 X-ray images of IRMA dataset. The first method is image folding that operates based on salient regions in an image. Saliency is determined by a context-aware saliency algorithm which includes folding the image. After the folding process, the reduced image area is used to extract multi-block and multi-scale LBP features and to classify these features by multi-class Support vector machine (SVM). The other method consists of classification and distance-based feature similarity. Images are firstly classified into general classes by utilizing LBP features. Subsequently, the retrieval is performed within the class to locate the most similar images. Between the retrieval and classification processes, LBP features are eliminated by employing the error histogram of a shallow (n/p/n) autoencoder to quantify the retrieval relevance of image blocks. If the region is relevant, the autoencoder gives large error for its decoding. Hence, via examining the autoencoder error of image blocks, irrelevant regions can be detected and eliminated. In order to calculate similarity within general classes, the distance between the LBP features of relevant regions is calculated. The results show that the retrieval time can be reduced, and the storage requirements can be lowered without significant decrease in accuracy

Uma metodologia eficiente para recuperação de exames médicos DICOM por similaridade de caracteristicas visuais

Dissertação (Mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computaçao.À medida que se iniciou o processo de popularização de exames médicos em formato digital, surgiu à necessidade de se desenvolver técnicas capazes de facilitar o processo de tomada de decisão médica. Nesse contexto, técnicas de Recuperação de Imagens Médicas Baseada no Conteúdo - Content-Based Medical Image Retrieval (CBMIR) [MULLER, 2004a] têm sido empregadas

Using medGIFT and easyIR

Abstract. This article describes the use of medGIFT and easyIR for three of four ImageCLEF 2005 tasks. All results rely on two systems: the GNU Image Finding Tool (GIFT) for visual retrieval, and easyIR for text. For ad–hoc retrieval, two visual runs were submitted. No textual retrieval was attempted, resulting in lower scores than those using text retrieval but the best visual run. For medical retrieval, visual retrieval was performed with several configurations of Gabor filters and grey level/color quantisations as well as combinations of text and visual features. Due to a lack of resources no feedback runs were created, an area where medGIFT performed best in 2004. For classification, a retrieval with the target image was performed and the first N = 1; 5; 10 results used to calculate scores for classes by simply adding up the scores for each class. No machine learning was performed, so results were surprisingly good and only topped by systems with optimised learning strategies.