FUZZY BINARY PATTERNS FOR UNCERTAINTY-AWARE TEXTURE REPRESENTATION

The Local Binary Pattern (LBP) representation of textures has been proved useful for a wide range of pattern recognition applications, including texture segmentation, face detection, and biomedical image analysis. The interest of the research community in the LBP texture representation gave rise to plenty of LBP and other binary pattern (BP)-based variations. However, noise sensitivity is still a major concern to their applicability on the analysis of real world images. To cope with this problem we propose a generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models. The proposed methodology assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values. The texture discrimination capability of four representative FBP-based approaches has been evaluated on the basis of comprehensive classification experiments on three reference datasets of natural textures under various types and levels of additive noise. The results reveal that the FBP-based approaches lead to consistent improvement in texture classification as compared with the original BP-based approaches for various degrees of uncertainty. This improved performance is also validated by illustrative unsupervised segmentation experiments on natural scenes

A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images

cDNA microarrays is one of the most fundamental and powerful tools in biotechnology. Despite its relatively late discovery in 1995, it has since been utilized in many biomedical applications such as cancer research, infectious disease diagnosis and treatment, toxicology research, pharmacology research, and agricultural development. The reason for its broa

Autopilot spatially-adaptive active contour parameterization for medical image segmentation

In this work, a novel framework for automated, spatially-adaptive adjustment of active contour regularization and data fidelity parameters is proposed and applied for medical image segmentation. The proposed framework is tailored upon the isomorphism observed between these parameters and the eigenvalues of diffusion tensors. Since such eigenvalues reflect the diffusivity of edge regions, we embed this information in regularization and data fidelity parameters by means of entropy-based, spatially-adaptive `heatmaps'. The latter are able to repel an active contour from randomly directed edge regions and guide it towards structured ones. Experiments are conducted on endoscopic as well as mammographic images. The segmentation results demonstrate that the proposed framework bypasses iterations dedicated to false local minima associated with noise, artifacts and inhomogeneities, speeding up contour convergence, whereas it maintains a high segmentation quality

M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

Multi-modality curative treatment of salivary gland cancer liver metastases with drug-eluting bead chemoembolization, radiofrequency ablation, and surgical resection: a case report

<p>Abstract</p> <p>Introduction</p> <p>Liver metastases are rare in salivary gland tumors and have been reported only once to be the first manifestation of the disease. They are usually treated with surgical resection of the primary tumor and systemic chemotherapy. Drug-eluting bead chemoembolization has an evolving role in the treatment of hepatocellular carcinoma, as well as in the treatment of metastatic disease of the liver. Nevertheless, it has never been used in a patient with salivary gland liver metastases.</p> <p>Case presentation</p> <p>We report a case of a 51-year-old Caucasian Greek woman who presented to our hospital with liver metastases as the first manifestation of an adenoid cystic carcinoma of the left submandibular gland. The liver lesions were deemed inoperable because of their size and multi-focality and proved resistant to systemic chemotherapy. She was curatively treated with a combination of doxorubicin eluting bead (DC Beads) chemoembolization, intra-operative and percutaneous radiofrequency ablation, and radiofrequency-assisted surgical resection. The patient remained disease-free one year after the surgical resection.</p> <p>Conclusion</p> <p>In conclusion, this complex case is an example of inoperable liver metastatic disease from the salivary glands that was refractory to systemic chemotherapy but was curatively treated with a combination of locoregional therapies and surgery. A multi-disciplinary approach and the adoption of modern radiological techniques produced good results after conventional therapies failed and there were no other available treatment modalities.</p

Language learning strategies in EFL.

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