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

Statistical Methods in Medical Research.

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.

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Computer-aided diagnosis in chest radiography: a survey

Members of the phylum Proteobacteria are most prominent among bacteria causing plant diseases that result in a diminution of the quantity and quality of food produced by agriculture. To ameliorate these losses, there is a need to identify infections in early stages. Recent developments in next generation nucleic acid sequencing and mass spectrometry open the door to screening plants by the sequences of their macromolecules. Such an approach requires the ability to recognize the organismal origin of unknown DNA or peptide fragments. There are many ways to approach this problem but none have emerged as the best protocol. Here we attempt a systematic way to determine organismal origins of peptides by using a machine learning algorithm. The algorithm that we implement is a Support Vector Machine (SVM). The amino acid compositions of proteobacterial proteins were found to be different from those of plant proteins. We developed an SVM model based on amino acid and dipeptide compositions to distinguish between a proteobacterial protein and a plant protein. The amino acid composition (AAC) based SVM model had an accuracy of 92.44% with 0.85 Matthews correlation coefficient (MCC) while the dipeptide composition (DC) based SVM model had a maximum accuracy of 94.67% and 0.89 MCC. We also developed SVM models based on a hybrid approach (AAC and DC), which gave a maximum accuracy 94.86% and a 0.90 MCC. The models were tested on unseen or untrained datasets to assess their validity. The results indicate that the SVM based on the AAC and DC hybrid approach can be used to distinguish proteobacterial from plant protein sequences.

/pdf/a-support-vector-machine-based-method-to-distinguish-6vdk38lo7h.pdf

A Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins

Storage phosphor radiography is a digital technique that uses photo-stimulable phosphor screens to substitute for conventional screen-film combinations. While the technique is more than 15 years old, it is only recently that technological and economic aspects of these systems have become favourable enough to envisage a more widespread clinical application.

Storage phosphor radiography

PURPOSE To examine the extent to which digital luminescence radiography (DLR) can be used for the imaging of pulmonary nodules and interstitial lung disease in chest radiography without any loss of image quality. Additionally: to examine whether post-processing of image data can optimise the recognizability of varied image details. MATERIALS AND METHODS Detail perceptibility studies were performed on an anthropomorphic thorax phantom with simulated nodules and small linear and reticular details. Under standard conditions, digital luminescence radiographs were obtained in 7 different image modes, and these were compared with a 200-speed screen-film system. The detection of these systems was evaluated in an ROC analysis on the basis of 19,200 individual observations. RESULTS Edge enhancement or application of high-frequency-enhancing small filter kernels (S 5) slightly improves the detection of linear structures; however, the illustration of nodular details is markedly reduced. Larger filter kernels (S 20, S 40) make a definitive detection possible--not only of circular, but also of linear details. CONCLUSIONS Storage phosphor radiographs are equal to the tested analog screen-film-system. The optimization of post-processing can be helpful in the prevention of routine multiple documentations.

ROC-Analyse zur Bildnachverarbeitung digitaler Thoraxaufnahmen

170 patients who had suffered trauma to bones or soft tissues were examined by a digital and a conventional film/screen technique. In addition to "standard" and edge-enhanced images, post-processing was carried out and compared with conventional radiographs. Four radiologists and two surgeons evaluated the compacta, spongiosa, soft tissues, fractures and osteo-synthetic materials as shown on the two systems. The "standard" digital images are inferior to film images, but edge-enhanced images showed definite advantages for demonstrating soft tissues. Evaluation of osteo-synthetic material is limited by the creation of artifacts. Recognition of detail can be significantly improved by using post-processing with optimized parameters.

Digitale Lumineszenzradiographie (DLR) zur Skelettdiagnostik in der Traumatologie

The extent to which the diagnostic performance of low-dosed storage phosphors can be improved by suitable image postprocessing was investigated. Storage phosphors radiographs and conventional 200- and 400-speed screen-film combinations of an antropomorphic chest phantom with simulated nodules and linear structures were compared. The detection of test bodies was determined in an ROC analysis based on 16 200 individual observations. The two analog screen-film systems did not differ significantly. As far as the storage phosphor radiographs are concerned, a higher diagnostic performance was achieved with a large filter kernel (S 40), whereas small filter kernels and edge-enhanced imaging modes reduced the detection of detail. The image quality of low-dosed storage phosphors is thus similar to high-amplification screen-film combinations if large filter kernels are used in postprocessing of the image.

Unsharp masking of low-dosed digital luminiscence radiographs: results of a receiver operating characteristics analysis

Purpose: The aim of the study was to optimize the postprocessing of digital luminescence radiographs of interstitial infiltrates of the lung.Material and Methods: Ninety-seven patients with established diseases of the lung parenchyma were examined: 514 pathological details were investigated, using a 200-speed screen-film system and a storage phosphor plate. In digital postprocessing, the size of the filter kernel (S) varied between S 5 and S 70. Based upon 32896 individual evaluations, the image quality was evaluated in multifactorial variance analyses.Results: The digital standards were significantly inferior to the tested screen-film system. The large filter kernel S 70 and the small filter kernels S 5 and S 10, as well as an algorithm for edge enhancement, showed selective advantages in the depiction of nodular or small linear structures. For micronodular and linear structures, kernel sizes of S 20 and S 40 produced equally good results (p>0.05).Conclusion: Whenever optimized postprocessing is involved...

Effects of varying filter kernel sizes on the image quality of interstitial lung diseases.

In the course of a comparative study 231 patients with traumatic lesions in skeletal and soft tissue areas were X-rayed using conventional film radiography and digital luminescence radiography (DLR). The results were evaluated by radiologists and traumatologists on the basis of optimised image post-processing in addition to the standard digital presentations. The imaging quality of compacta, spongiosa, soft tissues, fracture lines and osteosynthesis material was assessed in comparison with the screen-film images. Whereas the standard digital presentation in the “conventionally adjusted” imaging mode was inferior to the screen-film radiograph, the “edge-enhanced” standard presentation proved to be advantageous in the imaging of soft tissues (frequency of first-place ratings: DLR conventional 6%, screen-film system 21%, DLR edge-enhanced 31%). In the overall evaluation high-frequency filtered inverse image post-processing showed the highest diagnostic potential (frequency of first-place ratings: 41%). A conclusive assessment of metal implants is hindered by overshoot artefacts and can only be achieved through time-consuming post-processing of the stored image data.

M. Voss

Papers

ROC-Analyse zur Bildnachverarbeitung digitaler Thoraxaufnahmen

Digitale Lumineszenzradiographie (DLR) zur Skelettdiagnostik in der Traumatologie

Unsharp masking of low-dosed digital luminiscence radiographs: results of a receiver operating characteristics analysis

Effects of varying filter kernel sizes on the image quality of interstitial lung diseases.

Digital luminescent radiography for diagnosis of skeletal injuries