Image Analysis Methods for Sugar Beet Phenotyping

In un mondo che richiede sempre maggiormente un'automazione delle attività della catena produttiva industriale, la computer vision rappresenta uno strumento fondamentale perciò che viene già riconosciuta internazionalmente come la Quarta Rivoluzione Industriale o Industry 4.0. Avvalendomi di questo strumento ho intrapreso presso l'azienda Syngenta lo studio della problematica della conta automatica del numero di foglie di una pianta. Il problema è stato affrontato utilizzando due differenti approcci, ispirandosi alla letteratura. All'interno dell'elaborato è presente anche la descrizione progettuale di un ulteriore metodo, ad oggi non presente in letteratura. Le metodologie saranno spiegate in dettaglio ed i risultati ottenuti saranno confrontati utilizzando i primi due approcci. Nel capitolo finale si trarranno le conclusioni sulle basi dei risultati ottenuti e dall'analisi degli stessi.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

A new approach to detect structural differences in lung parenchyma using digital image analysis

Morphometric investigations using a point and intersection counting strategy in the lung often are not able to reveal the full set of morphologic changes. This happens particularly when structural modifications are not expressed in terms of volume density changes and when rough and fine surface density alterations cancel each other at different magnifications. Making use of digital image processing, we present a methodological approach that allows to easily and quickly quantify changes of the geometrical properties of the parenchymal lung structure and reflects closely the visual appreciation of the changes. Randomly sampled digital images from light microscopic sections of lung parenchyma are filtered, binarized, and skeletonized. The lung septa are thus represented as a single-pixel wide line network with nodal points and end points and the corresponding internodal and end segments. By automatically counting the number of points and measuring the lengths of the skeletal segments, the lung architecture can be characterized and very subtle structural changes can be detected. This new methodological approach to lung structure analysis is highly sensitive to morphological changes in the parenchyma: it detected highly significant quantitative alterations in the structure of lungs of rats treated with a glucocorticoid hormone, where the classical morphometry had partly failed.

Glucocorticoid induced impairment of lung structure assessed by digital image analysis

Glucocorticoids (GC) are successfully applied in neonatology to improve lung maturation in preterm born babies. Animal studies show that GC can also impair lung development. In this investigation, we used a new approach based on digital image analysis. Microscopic images of lung parenchyma were skeletonised and the geometrical properties of the septal network characterised by analysing the 'skeletal' parameters. Inhibition of the process of alveolarisation after extensive administration of small doses of GC in newborn rats was confirmed by significant changes in the 'skeletal' parameters. The induced structural changes in the lung parenchyma were still present after 60 days in adult rats, clearly indicating a long lasting or even definitive impairment of lung development and maturation caused by GC. Conclusion: digital image analysis and skeletonisation proved to be a highly suited approach to assess structural changes in lung parenchyma.

Mapping material distribution in a heterogeneous sand tank by image analysis

Water flow and solute transport through soils are strongly influenced by the spatial arrangement of soil materials with different hydraulic and chemical properties. Knowing the specific or statistical arrangement of these materials is considered as a key toward improved predictions of solute transport. Our aim was to obtain two-dimensional material maps from photographs of exposed profiles. We developed a segmentation and classification procedure and applied it to the images of a very heterogeneous sand tank, which was used for a series of flow and transport experiments. The segmentation was based on thresholds of soil color, estimated from local median gray values, and of soil texture, estimated from local coefficients of variation of gray values. Important steps were the correction of inhomogeneous illumination and reflection, and the incorporation of prior knowledge in filters used to extract the image features and to smooth the results morphologically. We could check and confirm the success of our mapping by comparing the estimated with the designed sand distribution in the tank. The resulting material map was used later as input to model flow and transport through the sand tank. Similar segmentation procedures may be applied to any high-density raster data, including photographs or spectral scans of field profiles.

Development of automated image analysis tools for verification of radiotherapy field accuracy with an electronic portal imaging device

The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5$\sp\circ$ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1$\sp\circ$ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross-correlation technique were implemented within an experimental radiotherapy picture archival and communication system (RT-PACS) and were used clinically to evaluate the setup variability of two groups of cancer patients treated with and without an alpha-cradle immobilization aid. The tools developed in this project have proven to be very effective and have played an important role in detecting patient alignment errors and field-shape errors in treatment fields formed by a multileaf collimator (MLC). ^

A New Technique for Firn Grain-Size Measurement Using SEM Image Analysis

Firn microstructure is accurately characterized using images obtained from scanning electron microscopy (SEM). Visibly etched grain boundaries within images are used to create a skeleton outline of the microstructure. A pixel-counting utility is applied to the outline to determine grain area. Firn grain sizes calculated using the technique described here are compared to those calculated using the techniques of Cow (1969) and Gay and Weiss (1999) on samples of the same material, and are found to be substantially smaller. The differences in grain size between the techniques are attributed to sampling deficiencies (e.g. the inclusion of pore filler in the grain area) in earlier methods. The new technique offers the advantages of greater accuracy and the ability to determine individual components of the microstructure (grain and pore), which have important applications in ice-core analyses. The new method is validated by calculating activation energies of grain boundary diffusion using predicted values based on the ratio of grain-size measurements between the new and existing techniques. The resulting activation energy falls within the range of values previously reported for firn/ice.

Image analysis of immunohistochemistry is superior to visual scoring as shown for patient outcome of esophageal adenocarcinoma.

Quantification of protein expression based on immunohistochemistry (IHC) is an important step in clinical diagnoses and translational tissue-based research. Manual scoring systems are used in order to evaluate protein expression based on staining intensities and distribution patterns. However, visual scoring remains an inherently subjective approach. The aim of our study was to explore whether digital image analysis proves to be an alternative or even superior tool to quantify expression of membrane-bound proteins. We analyzed five membrane-binding biomarkers (HER2, EGFR, pEGFR, β-catenin, and E-cadherin) and performed IHC on tumor tissue microarrays from 153 esophageal adenocarcinomas patients from a single center study. The tissue cores were scored visually applying an established routine scoring system as well as by using digital image analysis obtaining a continuous spectrum of average staining intensity. Subsequently, we compared both assessments by survival analysis as an end point. There were no significant correlations with patient survival using visual scoring of β-catenin, E-cadherin, pEGFR, or HER2. In contrast, the results for digital image analysis approach indicated that there were significant associations with disease-free survival for β-catenin, E-cadherin, pEGFR, and HER2 (P = 0.0125, P = 0.0014, P = 0.0299, and P = 0.0096, respectively). For EGFR, there was a greater association with patient survival when digital image analysis was used compared to when visual scoring was (visual: P = 0.0045, image analysis: P < 0.0001). The results of this study indicated that digital image analysis was superior to visual scoring. Digital image analysis is more sensitive and, therefore, better able to detect biological differences within the tissues with greater accuracy. This increased sensitivity improves the quality of quantification.

Aortic morphometry at endograft position as assessed by 3D image analysis affects risk of type I endoleak formation after TEVAR

PURPOSE The purpose of this study was to identify morphologic factors affecting type I endoleak formation and bird-beak configuration after thoracic endovascular aortic repair (TEVAR). METHODS Computed tomography (CT) data of 57 patients (40 males; median age, 66 years) undergoing TEVAR for thoracic aortic aneurysm (34 TAA, 19 TAAA) or penetrating aortic ulcer (n = 4) between 2001 and 2010 were retrospectively reviewed. In 28 patients, the Gore TAG® stent-graft was used, followed by the Medtronic Valiant® in 16 cases, the Medtronic Talent® in 8, and the Cook Zenith® in 5 cases. Proximal landing zone (PLZ) was in zone 1 in 13, zone 2 in 13, zone 3 in 23, and zone 4 in 8 patients. In 14 patients (25%), the procedure was urgent or emergent. In each case, pre- and postoperative CT angiography was analyzed using a dedicated image processing workstation and complimentary in-house developed software based on a 3D cylindrical intensity model to calculate aortic arch angulation and conicity of the landing zones (LZ). RESULTS Primary type Ia endoleak rate was 12% (7/57) and subsequent re-intervention rate was 86% (6/7). Left subclavian artery (LSA) coverage (p = 0.036) and conicity of the PLZ (5.9 vs. 2.6 mm; p = 0.016) were significantly associated with an increased type Ia endoleak rate. Bird-beak configuration was observed in 16 patients (28%) and was associated with a smaller radius of the aortic arch curvature (42 vs. 65 mm; p = 0.049). Type Ia endoleak was not associated with a bird-beak configuration (p = 0.388). Primary type Ib endoleak rate was 7% (4/57) and subsequent re-intervention rate was 100%. Conicity of the distal LZ was associated with an increased type Ib endoleak rate (8.3 vs. 2.6 mm; p = 0.038). CONCLUSIONS CT-based 3D aortic morphometry helps to identify risk factors of type I endoleak formation and bird-beak configuration during TEVAR. These factors were LSA coverage and conicity within the landing zones for type I endoleak formation and steep aortic angulation for bird-beak configuration.

Icebergs in the Amundsen Sea (Antarctica) determined from ENVISAT ASAR WSM data using object-based image analysis approach, link to ESRI GDB files

ENVISAT ASAR WSM images with pixel size 150 × 150 m, acquired in different meteorological, oceanographic and sea ice conditions were used to determined icebergs in the Amundsen Sea (Antarctica). An object-based method for automatic iceberg detection from SAR data has been developed and applied. The object identification is based on spectral and spatial parameters on 5 scale levels, and was verified with manual classification in four polygon areas, chosen to represent varying environmental conditions. The algorithm works comparatively well in freezing temperatures and strong wind conditions, prevailing in the Amundsen Sea during the year. The detection rate was 96% which corresponds to 94% of the area (counting icebergs larger than 0.03 km**2), for all seasons. The presented algorithm tends to generate errors in the form of false alarms, mainly caused by the presence of ice floes, rather than misses. This affects the reliability since false alarms were manually corrected post analysis.

Benthic cover map of Heron Reef derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.