Publications

Tissue decellularization is typically assessed through absorbance-based DNA quantification after tissue digestion. This method has several disadvantages, namely its destructive nature and inadequacy in experimental situations where tissue is scarce. Here, we present an image processing algorithm for quantitative analysis of DNA content in (de)cellularized tissues as a faster, simpler and more comprehensive alternative. Our method uses local entropy measurements of a phase contrast image to create a mask, which is then applied to corresponding nuclei labelled (UV) images to extract average fluorescence intensities as an estimate of DNA content. The method can be used on native or decellularized tissue to quantify DNA content, thus allowing quantitative assessment of decellularization procedures. We confirm that our new method yields results in line with those obtained using the standard DNA quantification method and that it is successful for both lung and heart tissues. We are also able to accurately obtain a timeline of decreasing DNA content with increased incubation time with a decellularizing agent. Finally, the identified masks can also be applied to additional fluorescence images of immunostained proteins such as collagen or elastin, thus allowing further image-based tissue characterization.

JTD Keywords: decellularization, differentiation, fluorescence image, image processing, microscopic image, Decellularization, Fluorescence image, Image processing, Matrix, Microscopic image, Segmentation

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

JTD Keywords: cardiovascular system, diseases, feature extraction, geriatrics, medical signal processing, oscillations, pneumodynamics, signal classification, time-frequency analysis, Cheyne-Stokes respiration, apnea, cardiac systems, chronic heart failure, classification problems, discriminant band, diseases, elderly patients, feature selection process, frequency variability, interquartile ranges, linear discriminant analysis, nonperiodic breathing, oscillatory breathing pattern, periodic breathing, respiratory How signal, respiratory systems, signal segmentation, time 5 min, time-frequency parameters, Accuracy, Aging, Frequency modulation, Heart, Senior citizens, Time-frequency analysis

A new method for obtaining a three-dimensional volumetric reconstruction from a set of views improving the classical Shape from Silhouette method (SFS) is presented. SFS approaches can be easily accelerated through hardware and software techniques but they are very sensible to errors arising during calibration and segmentation processes so they present difficulties when dealing with real images. This paper proposes a new algorithm which uses the information about pixel segmentation uncertainty contained in each view in order to get a reliable 3D reconstruction of the scene. Aggregation of the projected uncertainties permits to classify scene's voxels by means of a decision rule but also makes it possible to create a three-dimensional confidence map of the scene. As a consequence, the regions where more information is needed can be foreseen. Sample reconstructions from real image sets are presented and evaluated.

JTD Keywords: Calibration, Image classification, Image reconstruction, Image segmentation, 3D reconstruction, Calibration process, Decision rule, Hardware technique, Pixel segmentation, Pixel-level certainty measures, Scene voxel classification, Segmentation process, Shape from silhouette method, Software technique, Three-dimensional confidence map, Three-dimensional volumetric reconstruction

This paper presents a manipulation and measurement aid for tasks carried out in micro-nano environments operating with scanning AFM. In teleoperated manipulation or measurement over a given point of the target, where a slow and precise movement is necessary, the developed system increases the accuracy in this point producing a space deformation. In automatic scanning, the adjusted selection of the target, through assisted image segmentation, enables to reduce the working time.

JTD Keywords: Assisted teleoperation, Image segmentation, Micro-nano manipulation, Workspace deformation