Bionanocomposites from electrospun PVA/pineapple nanofibers/Stryphnodendron adstringens bark extract for medical applications

Tissue engineering has been defined as an interdisciplinary field that applies the principles of engineering and life sciences for the development of biological substitutes to restore, maintain or improve tissue function. This area is always looking for new classes of degradable biopolymers that are biocompatible and whose activities are controllable and specific, more likely to be used as cell scaffolds, or in vitro tissue reconstruction. In this paper, we developed a novel bionanocomposite with homogeneous porous distribution and prospective natural antimicrobial properties by electrospinning technique using Stryphodedron barbatimao extract (Barbatimão). SEM images showed equally distribution of nanofibres. DSC and TGA showed higher thermal properties and change crystallinity of the developed bionanocomposite mainly because these structural modification. © 2012 Elsevier B.V.

Automated diagnosis of human intestinal parasites using optical microscopy images

Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes. © 2013 IEEE.

The use of instantaneous phase for improving sparse arrays images

Sparse arrays have pitch larger than half-wavelength (lambda/2) and there is a reduced number of elements in comparison with a full-populated array. Consequently, there is a reduction in cost, data acquisition and processing. However, conventional beamforming techniques result in large side and grating lobes, and consequently in image artifacts. In this work the instantaneous phase of the signals is used in a beamforming technique instead of the instantaneous amplitudes to improve images obtained from sparse arrays configurations. A threshold based on a statistical analysis and the number of signals used for imaging is applied to each pixel, in order to determine if that pixel is related to a defect or not. Three sets of data are used to evaluate the technique, considering medical and non-destructive testing: a simulated point spread function, a medical phantom and an aluminum plate with 2 lambda-, 7 lambda- and lambda-pitch, respectively. The conventional amplitude image is superposed by the image improved by the instantaneous phase, increasing the reflectors detectability and reducing artifacts for all cases, as well as dead zone for the tested plate.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Abstract Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

Spontaneous combined rupture of a pelvicalyceal cyst into the collector system and retroperitoneal space during the acquisition of computed tomography scan images: a case report

Abstract Introduction Pelvicalyceal cysts are common findings in autopsies and can manifest with a variety of patterns. These cystic lesions are usually a benign entity with no clinical significance unless they enlarge enough to cause compression of the adjacent collecting system and consequently obstructive uropathy. Few cases of the spontaneous rupture of pelvicalyceal renal cysts have been published and to the best of our knowledge there is no report of a combined rupture to collector system and retroperitoneal space documented during a multiphase computed tomography. Case presentation We report a case of a ‘real-time’ spontaneous rupture of a pelvicalyceal cyst into the collecting system with fistulization into the retroperitoneum. The patient was a 78-year-old Caucasian man with a previous history of renal stones and a large pelvicalyceal renal cyst who was admitted to our Emergency department with acute right flank pain. A multiphase computed tomography was performed and the pre-contrast images demonstrated a right pelvicalyceal renal cyst measuring 12.0 × 6.1cm in the lower pole causing moderate dilation of the upper right renal collection system. In addition, a partially obstructive stone on the left distal ureter with mild left hydronephrosis was noted. The nephrographic phase did not add any new information. The excretory phase (10-minute delay) demonstrated a spontaneous rupture of the cyst into the pelvicalyceal system with posterior fistulization into the retroperitoneal space. Conclusion In this case study we present time-related changes of a rare pelvicalyceal cyst complication, which to the best of our knowledge has fortunately not been previously documented. Analysis of the sequential images and comparison with an earlier scan allowed us to better understand the physiopathological process of the rupture, the clinical presentation and to elaborate hypotheses for its etiopathogenesis.

Statistical methods for analysis and processing of medical ultrasound: applications to segmentation and restoration

In this thesis two major topics inherent with medical ultrasound images are addressed: deconvolution and segmentation. In the first case a deconvolution algorithm is described allowing statistically consistent maximum a posteriori estimates of the tissue reflectivity to be restored. These estimates are proven to provide a reliable source of information for achieving an accurate characterization of biological tissues through the ultrasound echo. The second topic involves the definition of a semi automatic algorithm for myocardium segmentation in 2D echocardiographic images. The results show that the proposed method can reduce inter- and intra observer variability in myocardial contours delineation and is feasible and accurate even on clinical data.

Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.

A Hybrid Multimodal Non-Rigid Registration of MR Images Based on Diffeomorphic Demons

n this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.

Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries

Robust and accurate identification of intervertebral discs from low resolution, sparse MRI scans is essential for the automated scan planning of the MRI spine scan. This paper presents a graphical model based solution for the detection of both the positions and orientations of intervertebral discs from low resolution, sparse MRI scans. Compared with the existing graphical model based methods, the proposed method does not need a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on 25 low resolution, sparse spine MRI data sets verified its performance.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

You Can't Touch This: Touch-free Navigation Through Radiological Images

Keyboards, mice, and touch screens are a potential source of infection or contamination in operating rooms, intensive care units, and autopsy suites. The authors present a low-cost prototype of a system, which allows for touch-free control of a medical image viewer. This touch-free navigation system consists of a computer system (IMac, OS X 10.6 Apple, USA) with a medical image viewer (OsiriX, OsiriX foundation, Switzerland) and a depth camera (Kinect, Microsoft, USA). They implemented software that translates the data delivered by the camera and a voice recognition software into keyboard and mouse commands, which are then passed to OsiriX. In this feasibility study, the authors introduced 10 medical professionals to the system and asked them to re-create 12 images from a CT data set. They evaluated response times and usability of the system compared with standard mouse/keyboard control. Users felt comfortable with the system after approximately 10 minutes. Response time was 120 ms. Users required 1.4 times more time to re-create an image with gesture control. Users with OsiriX experience were significantly faster using the mouse/keyboard and faster than users without prior experience. They rated the system 3.4 out of 5 for ease of use in comparison to the mouse/keyboard. The touch-free, gesture-controlled system performs favorably and removes a potential vector for infection, protecting both patients and staff. Because the camera can be quickly and easily integrated into existing systems, requires no calibration, and is low cost, the barriers to using this technology are low.