Automatic categorization of medical images for content-based retrieval and data mining

Güld, Mark Oliver; Deselaers, Thomas; Keysers, Daniel; Schubert, Henning; Spitzer, Klaus; Ney, Hermann; Wein, B.

doi:10.1016/j.compmedimag.2004.09.010

Cited by 190 publications

(113 citation statements)

References 36 publications

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“…Recently, the application of machine learning techniques to medical image retrieval and analysis has received more and more attention (Lehmann et al, 2005). Due to the rapid development of computer technology, it is becoming more and more convenient to acquire, digitally store and transfer medical imagery.…”

Section: Introductionmentioning

confidence: 99%

Batch mode active learning and its application to medical image classification

Hoi

Jin

Zhu

et al. 2006

Proceedings of the 23rd International Conference on Machine Learning - ICML '06

303

190

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The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher information. To resolve this challenge, we propose an efficient greedy algorithm that is based on the property of submodular functions. Our empirical studies with five UCI datasets and one realworld medical image classification show that the proposed batch mode active learning algorithm is more effective than the state-ofthe-art algorithms for active learning.

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Section: Introductionmentioning

confidence: 99%

Batch mode active learning and its application to medical image classification

Hoi

Jin

Zhu

et al. 2006

Proceedings of the 23rd International Conference on Machine Learning - ICML '06

303

190

View full text Add to dashboard Cite

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“…And this kind of algorithms have also been explored for medical image classification, considering that most information in medical images is local [1]. Unlike global features, local features are always unordered.…”

Section: Spatial Pyramid Matching For Medical Image Classificationmentioning

confidence: 99%

Medical Image Annotation and Retrieval Using Visual Features

Jing

Hiu

et al. 2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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In this article, we present the algorithms and results of our participation in the medical image annotation and retrieval tasks of ImageCLEFmed 2006. We exploit both global features and local features to describe medical images in the annotation task. We examine different kinds global features and extract the most descriptive ones, which effectively capture the intensity, texture and shape characters of the image content, to represent the radiographs. We also evaluate the descriptive power of local features, i.e. local image patches, for medical images. A newly developed spatial pyramid matching algorithm is applied to measure the similarity between images represented by sets of local features. Both descriptors use multi-class SVM to classify the images. The error rate is 17.6% for global description and 18.2% for the local one, which rank sixth and ninth respectively among all the submissions. For the medical image retrieval task, we only use visual features to describe the images. No textual information is considered. Different features are used to describe gray images and color images. Our submission achieves a mean average precision (MAP) of 0.0681, which ranks second in the 11 runs that also only use visual features.

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“…In [4][5] there are some similar categories of features. The feature 'layout' is the absolute or relative spatial position of the color.…”

Section: Feature Setsmentioning

confidence: 99%

An Automatic Classification System Applied in Medical Images

Qiu

2006

2006 IEEE International Conference on Multimedia and Expo

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In this paper, a multi-class classification system is developed for medical images. We have mainly explored ways to use different image features, and compared two classifiers: Principle Component Analysis (PCA) and Supporting Vector Machines (SVM) with RBF (radial basis functions) kernels. Experimental results showed that SVM with a combination of the middle-level blob feature and low-level features (down-scaled images and their texture maps) achieved the highest recognition accuracy. Using the 9000 given training images from ImageCLEF05, our proposed method has achieved a recognition rate of 88.9% in a simulation experiment. And according to the evaluation result from the ImageCLEF05 organizer, our method has achieved a recognition rate of 82% over its 1000 testing images.

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Automatic categorization of medical images for content-based retrieval and data mining

Cited by 190 publications

References 36 publications

Batch mode active learning and its application to medical image classification

Batch mode active learning and its application to medical image classification

Medical Image Annotation and Retrieval Using Visual Features

An Automatic Classification System Applied in Medical Images

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