X-Ray analysis to assess mechanical damage in sweet corn seeds

The X-ray test is a precise, fast and non-destructive method to detect mechanical damage in seeds. In the present study, the efficiency of X-ray analysis in identifying the extent of mechanical damage in sweet corn seeds and its relationship with germination and vigor was evaluated. Hybrid 'SWB 551' (sh2) seeds with round (R) and flat (F) shapes were classified as large (L), medium (M1, M2 and M3) and small (S), using sieves with round and oblong screens. After artificial exposure to different levels of damage (0, 1, 3, 5 and 7 impacts), seeds were X-rayed (15 kV, 5 min) and submitted to germination (25 °C/5 days) and cold (10 °C/7 days) tests. Digital images of normal and abnormal seedlings and ungerminated seeds from germination and cold tests were jointly analyzed with the seed X-ray images. Results showed that damage affecting the embryonic axis resulted in abnormal seedlings or dead seeds in the germination and cold tests. The X-ray analysis is efficient for identifying mechanical damage in sweet corn seeds, allowing damage severity to be associated with losses in germination and vigor.

Quantitative analysis of photodynamic therapy effects in rat mammary tumor vascular density using image-pro plus software

Photodynamic therapy (PDT) is a treatment modality that has advanced rapidly in recent years. It causes tissue and vascular damage with the interaction of a photosensitizing agent (PS), light of a proper wavelength, and molecular oxygen. Evaluation of vessel damage usually relies on histopathology evaluation. Results are often qualitative or at best semi-quantitative based on a subjective system. The aim of this study was to evaluate, using CD31 immunohistochem- istry and image analysis software, the vascular damage after PDT in a well-established rodent model of chemically induced mammary tumor. Fourteen Sprague-Dawley rats received a single dose of 7,12-dimethylbenz(a)anthraxcene (80 mg/kg by gavage), treatment efficacy was evaluated by comparing the vascular density of tumors after treatment with Photogem® as a PS, intraperitoneally, followed by interstitial fiber optic lighting, from a diode laser, at 200 mW/cm and light dose of 100 J/cm directed against his tumor (7 animals), with a control group (6 animals, no PDT). The animals were euthanized 30 hours after the lighting and mammary tumors were removed and samples from each lesion were formalin-fixed. Immunostained blood vessels were quantified by Image Pro-Plus version 7.0. The control group had an average of 3368.6 ± 4027.1 pixels per picture and the treated group had an average of 779 ± 1242.6 pixels per area (P < 0.01), indicating that PDT caused a significant decrease in vascular density of mammary tumors. The combined immu- nohistochemistry using CD31, with selection of representative areas by a trained pathology, followed by quantification of staining using Image Pro-Plus version 7.0 system was a practical and robust methodology for vessel damage evalua- tion, which probably could be used to assess other antiangiogenic treatments.

Combining fractal and deterministic walkers for texture analysis and classification

In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets.

Structural analysis of a UAV wing through image processing and FEM

Analisi strutturale dell’ala di un UAV (velivolo senza pilota a bordo), sviluppata usando varie metodologie: misurazioni sperimentali statiche e dinamiche, e simulazioni numeriche con l’utilizzo di programmi agli elementi finiti. L’analisi statica è stata a sua volta portata avanti seguendo due differenti metodi: la classica e diretta determinazione degli spostamenti mediante l’utilizzo di un catetometro e un metodo visivo, basato sull’elaborazione di immagini e sviluppato appositamente a tale scopo in ambiente Matlab. Oltre a ciò è stata svolta anche una analisi FEM volta a valutare l’errore che si ottiene affrontando il problema con uno studio numerico. Su tale modello FEM è stata svolta anche una analisi di tipo dinamico con lo scopo di confrontare tali dati con i dati derivanti da un test dinamico sperimentale per ottenere informazioni utili per una seguente possibile analisi aeroelastica.

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

A MICROFLUIDIC METHOD TO MEASURE DIFFUSION IN HYDROGELS

A novel microfluidic method is proposed for studying diffusion of small molecules in a hydrogel. Microfluidic devices were prepared with semi-permeable microchannels defined by crosslinked poly(ethylene glycol) (PEG). Uptake of dye molecules from aqueous solutions flowing through the microchannels was observedoptically and diffusion of the dye into the hydrogel was quantified. To complement the diffusion measurements from the microfluidic studies, nuclear magnetic resonance(NMR) characterization of the diffusion of dye in the PEG hydrogels was performed. The diffusion of small molecules in a hydrogel is relevant to applications such asdrug delivery and modeling transport for tissue-engineering applications. The diffusion of small molecules in a hydrogel is dependent on the extent of crosslinking within the gel, gel structure, and interactions between the diffusive species and the hydrogel network. These effects were studied in a model environment (semi-infinite slab) at the hydrogelfluid boundary in a microfluidic device. The microfluidic devices containing PEG microchannels were fabricated using photolithography. The unsteady diffusion of small molecules (dyes) within the microfluidic device was monitored and recorded using a digital microscope. The information was analyzed with techniques drawn from digital microscopy and image analysis to obtain concentration profiles with time. Using a diffusion model to fit this concentration vs. position data, a diffusion coefficient was obtained. This diffusion coefficient was compared to those from complementary NMR analysis. A pulsed field gradient (PFG) method was used to investigate and quantify small molecule diffusion in gradient (PFG) method was used to investigate and quantify small molecule diffusion in hydrogels. There is good agreement between the diffusion coefficients obtained from the microfluidic methods and those found from the NMR studies. The microfluidic approachused in this research enables the study of diffusion at length scales that approach those of vasculature, facilitating models for studying drug elution from hydrogels in blood-contacting applications.

Impact of optic media opacities and image compression on quantitative analysis of optical coherence tomography

To analyze the impact of opacities in the optical pathway and image compression of 32-bit raw data to 8-bit jpg images on quantified optical coherence tomography (OCT) image analysis.

BEYOND ROOTS ALONE: NOVEL METHODOLOGIES FOR ANALYZING COMPLEX SOIL AND MINIRHIZOTRON IMAGERY USING IMAGE PROCESSING AND GIS TOOLS

Quantifying belowground dynamics is critical to our understanding of plant and ecosystem function and belowground carbon cycling, yet currently available tools for complex belowground image analyses are insufficient. We introduce novel techniques combining digital image processing tools and geographic information systems (GIS) analysis to permit semi-automated analysis of complex root and soil dynamics. We illustrate methodologies with imagery from microcosms, minirhizotrons, and a rhizotron, in upland and peatland soils. We provide guidelines for correct image capture, a method that automatically stitches together numerous minirhizotron images into one seamless image, and image analysis using image segmentation and classification in SPRING or change analysis in ArcMap. These methods facilitate spatial and temporal root and soil interaction studies, providing a framework to expand a more comprehensive understanding of belowground dynamics.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time?frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12 s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time?intensity curves from .84 ± .19 before registration to .96 ± .06 after registration

A method for incorporating residual stresses into patient-specific finite element simulations of arteries with an example on AAAs

Through progress in medical imaging, image analysis and finite element (FE) meshing tools it is now possible to extract patient-specific geometries from medical images of abdominal aortic aneurysms(AAAs), and thus to study clinically-relevant problems via FE simulations. Such simulations allow additional insight into human physiology in both healthy and diseased states. Medical imaging is most often performed in vivo, and hence the reconstructed model geometry in the problem of interest will represent the in vivo state, e.g., the AAA at physiological blood pressure. However, classical continuum mechanics and FE methods assume that constitutive models and the corresponding simulations begin from an unloaded, stress-free reference condition.

New perspectives on quantitative textural analysis

Process mineralogy provides the mineralogical information required by geometallurgists to address the inherent variation of geological data. The successful benefitiation of ores mostly depends on the ability of mineral processing to be efficiently adapted to the ore characteristics, being liberation one of the most relevant mineralogical parameters. The liberation characteristics of ores are intimately related to mineral texture. Therefore, the characterization of liberation necessarily requieres the identification and quantification of those textural features with a major bearing on mineral liberation. From this point of view grain size, bonding between mineral grains and intergrowth types are considered as the most influential textural attributes. While the quantification of grain size is a usual output of automated current technologies, information about grain boundaries and intergrowth types is usually descriptive and difficult to quantify to be included in the geometallurgical model. Aiming at the systematic and quantitative analysis of the intergrowth type within mineral particles, a new methodology based on digital image analysis has been developed. In this work, the ability of this methodology to achieve a more complete characterization of liberation is explored by the analysis of chalcopyrite in the rougher concentrate of the Kansanshi copper-gold mine (Zambia). Results obtained show that the method provides valuable textural information to achieve a better understanding of mineral behaviour during concentration processes. The potential of this method is enhanced by the fact that it provides data unavailable by current technologies. This opens up new perspectives on the quantitative analysis of mineral processing performance based on textural attributes.