MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing.

/pdf/machine-learning-for-the-detection-of-oil-spills-in-2i5ztvpfhn.pdf

Machine Learning for the Detection of Oil Spills in Satellite Radar Images

A general framework for multiresolution image fusion: from pixels to regions

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.

Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. We present herein a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications. The use of image segmentation in different imaging modalities is also described along with the difficulties encountered in each modality. We conclude with a discussion on the future of image segmentation methods in biomedical research.

A Survey of Current Methods in Medical Image Segmentation

Purpose: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. Methods: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. Results: The overall performancemore » of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. Conclusions: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.« less

http://www.etrovub.be/PUB_Files/IRIS/bjansen/medicalPhysicsTan2011.pdf

A novel computer-aided lung nodule detection system for CT images.

A critical view of pyramid segmentation algorithms

Segmentation of medical images

Two different algorithms were tested for the segmentation of cardiac MR (magnetic resonance) images. The first is the pyramid segmentation scheme, which was finally rejected owing to severe inherent shortcomings. The second is called the cavity detector and is based on classical image processing techniques. It is especially sensitive to compact objects in the image, even when these are not completely surrounded by walls. The second scheme is robust with respect to changes in the (automatically or a priori defined) segmentation parameters (such as threshold value or mask for distance transform) and has been validated by comparison with manual segmentation by a medical expert. >

Automated segmentation of cardiac MR images

Two algorithms for medical image processing are discussed: CAVITY DETECTOR, which solves the segmentation problem of regions which are not completely surrounded by walls and EDGMENTATION, which is used for (i) preprocessing before segmentation, (ii) boundary refinement before edge detection, (iii) segmentation based on a modified split-and-merge approach, and (iiii) data reduction. In this paper, we describe the principles of the algorithms and illustrate their generic properties by discussing the results of applications in 2-D and 3-D cardiac MRI and 3-D and 4-D cardiac SPECT.

Michel Bister

Papers

A novel computer-aided lung nodule detection system for CT images.

A critical view of pyramid segmentation algorithms

Segmentation of medical images

Automated segmentation of cardiac MR images

Techniques for cardiac image segmentation