Mathematics and Medicine: How mathematics, modelling and simulations can lead to better diagnosis and treatments

Starting with the discovery of X-rays by Röntgen in 1895, the progress in medical imaging has been extraordinary and immensely beneficial to diagnosis and therapy. Parallel to the increase of imaging accuracy, there is the quest of moving from qualitative to quantitative analysis and patient-tailored therapy. Mathematics, modelling and simulations are increasing their importance as tools in this quest. In this paper we give an overview of relations between mathematical modelling and imaging and focus particularly on the estimation of perfusion in the brain. In the forward model, the brain is treated as a porous medium and a two compartment model (arterial/venous) is used. Motivated by the similarity with techniques in reservoir modelling, we propose an ensemble Kalman filter to perform the parameter estimation and apply the method to a simple example as an illustrative example.acceptedVersio

Rotation Invariant Texture Classification Using Binary Filter Response Pattern (BFRP)

13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2-4 September 2009Using statistical textons for texture classification has shown great success recently. The maximal response 8 (MR8) method, which extracts an 8-dimensional feature set from 38 filters, is one of state-of-the-art rotation invariant texture classification methods. However, this method has two limitations. First, it require a training stage to build a texton library, thus the accuracy depends on the training samples; second, during classification, each 8-dimensional feature is assigned to a texton by searching for the nearest texton in the library, which is time consuming especially when the library size is big. In this paper, we propose a novel texton feature, namely Binary Filter Response Pattern (BFRP). It can well address the above two issues by encoding the filter response directly into binary representation. The experimental results on the CUReT database show that the proposed BFRP method achieves better classification result than MR8, especially when the training dataset is limited and less comprehensive.Department of ComputingRefereed conference pape

Multimodal microscopy for automated histologic analysis of prostate cancer

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is the single most prevalent cancer in US men whose gold standard of diagnosis is histologic assessment of biopsies. Manual assessment of stained tissue of all biopsies limits speed and accuracy in clinical practice and research of prostate cancer diagnosis. We sought to develop a fully-automated multimodal microscopy method to distinguish cancerous from non-cancerous tissue samples.</p> <p>Methods</p> <p>We recorded chemical data from an unstained tissue microarray (TMA) using Fourier transform infrared (FT-IR) spectroscopic imaging. Using pattern recognition, we identified epithelial cells without user input. We fused the cell type information with the corresponding stained images commonly used in clinical practice. Extracted morphological features, optimized by two-stage feature selection method using a minimum-redundancy-maximal-relevance (mRMR) criterion and sequential floating forward selection (SFFS), were applied to classify tissue samples as cancer or non-cancer.</p> <p>Results</p> <p>We achieved high accuracy (area under ROC curve (AUC) >0.97) in cross-validations on each of two data sets that were stained under different conditions. When the classifier was trained on one data set and tested on the other data set, an AUC value of ~0.95 was observed. In the absence of IR data, the performance of the same classification system dropped for both data sets and between data sets.</p> <p>Conclusions</p> <p>We were able to achieve very effective fusion of the information from two different images that provide very different types of data with different characteristics. The method is entirely transparent to a user and does not involve any adjustment or decision-making based on spectral data. By combining the IR and optical data, we achieved high accurate classification.</p

Non-Hodgkin lymphoma response evaluation with MRI texture classification

<p>Abstract</p> <p>Background</p> <p>To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.</p> <p>Methods</p> <p>A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests.</p> <p>Results</p> <p>NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.</p> <p>Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue.</p> <p>Conclusion</p> <p>Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.</p

A proteome-wide protein interaction map for Campylobacter jejuni

'Systematic identification of protein interactions for the bacterium Campylobacter jejuni using high-throughput yeast two-hybrid screens detected interactions for 80% of the organism's proteins

Robust Resampling Detection in Digital Images

Part 1: Research PapersInternational audienceTo create convincing forged images, manipulated images or parts of them are usually exposed to some geometric operations which require a resampling step. Therefore, detecting traces of resampling became an important approach in the field of image forensics. In this paper, we revisit existing techniques for resampling detection and design some targeted attacks in order to assess their reliability. We show that the combination of multiple resampling and hybrid median filtering works well for hiding traces of resampling. Moreover, we propose an improved technique for detecting resampling using image forensic tools. Experimental evaluations show that the proposed technique is good for resampling detection and more robust against some targeted attacks