Digital Data Collection and Processing in Field and Lab: The Approach of Kinneret Regional Project

The paper showcases the field- and lab-documentation system developed for Kinneret Regional Project, an international archaeological expedition to the Northwestern shore of the Sea of Galilee (Israel) under the auspices of the University of Bern, the University of Helsinki, Leiden University and Wofford College. The core of the data management system is a fully relational, server-based database framework, which also includes time-based and static GIS services, stratigraphic analysis tools and fully indexed document/digital image archives. Data collection in the field is based on mobile, hand-held devices equipped with a custom-tailored stand-alone application. Comprehensive three-dimensional documentation of all finds and findings is achieved by means of total stations and/or high-precision GPS devices. All archaeological information retrieved in the field – including tachymetric data – is synched with the core system on the fly and thus immediately available for further processing in the field lab (within the local network) or for post-excavation analysis at remote institutions (via the WWW). Besides a short demonstration of the main functionalities, the paper also presents some of the key technologies used and illustrates usability aspects of the system’s individual components.

Laser scanning microscopy combined with image restoration to analyse a 3D model of the human epithelial airway barrier

A laser scanning microscope collects information from a thin, focal plane and ignores out of focus information. During the past few years it has become the standard imaging method to characterise cellular morphology and structures in static as well as in living samples. Laser scanning microscopy combined with digital image restoration is an excellent tool for analysing the cellular cytoarchitecture, expression of specific proteins and interactions of various cell types, thus defining valid criteria for the optimisation of cell culture models. We have used this tool to establish and evaluate a three dimensional model of the human epithelial airway wall.

Mesh-based vs. Image-based Statistical Appearance Model of the Human Femur: a Preliminary Comparison Study for the Creation of Finite Element Meshes

Statistical models have been recently introduced in computational orthopaedics to investigate the bone mechanical properties across several populations. A fundamental aspect for the construction of statistical models concerns the establishment of accurate anatomical correspondences among the objects of the training dataset. Various methods have been proposed to solve this problem such as mesh morphing or image registration algorithms. The objective of this study is to compare a mesh-based and an image-based statistical appearance model approaches for the creation of nite element(FE) meshes. A computer tomography (CT) dataset of 157 human left femurs was used for the comparison. For each approach, 30 finite element meshes were generated with the models. The quality of the obtained FE meshes was evaluated in terms of volume, size and shape of the elements. Results showed that the quality of the meshes obtained with the image-based approach was higher than the quality of the mesh-based approach. Future studies are required to evaluate the impact of this finding on the final mechanical simulations.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Quantitative analysis of OCT characteristics in patients with achromatopsia and blue-cone monochromatism

PURPOSE: To quantify optical coherence tomography (OCT) images of the central retina in patients with blue-cone monochromatism (BCM) and achromatopsia (ACH) compared with healthy control individuals. METHODS: The study included 15 patients with ACH, 6 with BCM, and 20 control subjects. Diagnosis of BCM and ACH was established by visual acuity testing, morphologic examination, color vision testing, and Ganzfeld ERG recording. OCT images were acquired with the Stratus OCT 3 (Carl Zeiss Meditec AG, Oberkochen, Germany). Foveal OCT images were analyzed by calculating longitudinal reflectivity profiles (LRPs) from scan lines. Profiles were analyzed quantitatively to determine foveal thickness and distances between reflectivity layers. RESULTS: Patients with ACH and BCM had a mean visual acuity of 20/200 and 20/60, respectively. Color vision testing results were characteristic of the diseases. The LRPs of control subjects yielded four peaks (P1-P4), presumably representing the RPE (P1), the ovoid region of the photoreceptors (P2), the external limiting membrane (ELM) (P3), and the internal limiting membrane (P4). In patients with ACH, P2 was absent, but foveal thickness (P1-P4) did not differ significantly from that in the control subjects (187 +/- 20 vs. 192 +/- 14 microm, respectively). The distance from P1 to P3 did not differ significantly (78 +/- 10 vs. 82 +/- 5 microm) between ACH and controls subjects. In patients with BCM, P3 was lacking, and P2 advanced toward P1 compared with the control subjects (32 +/- 6 vs. 48 +/- 4 microm). Foveal thickness (153 +/- 16 microm) was significantly reduced compared with that in control subjects and patients with ACH. CONCLUSIONS: Quantitative OCT image analysis reveals distinct patterns for controls subjects and patients with ACH and BCM, respectively. Quantitative analysis of OCT imaging can be useful in differentiating retinal diseases affecting photoreceptors. Foveal thickness is similar in both normal subjects and patients with ACH but is decreased in patients with BCM.

A technique to detect and to quantify fasciocutaneous blood vessels in small laboratory animals ex vivo

PURPOSE: A microangiographical technique is described, which allows visualization of small and capillary blood vessels and quantification of fasciocutaneous blood vessels by means of digital computer analysis in very small laboratory animals. MATERIALS AND METHODS: The left carotid artery of 20 nu/nu mice was cannulated (26 gauge) and a mixture of gelatin, bariumsulfate, and green ink was injected according to standardized protocol. Fasciocutaneous blood vessels were visualized by digital mammography and analyzed for vessel length and vessel surface area as standardized units [SU] by computer program. RESULTS: With the described microangiography method, fasciocutaneous blood vessels down to capillary size level can be clearly visualized. Regions of interest (ROIs) can be defined and the containing vascular network quantified. Comparable results may be obtained by calculating the microvascular area index (MAI) and the microvascular length index (MLI), related to the ROIs size. Identical ROIs showed a high reproducibility for measured [SU] < 0.01 +/- 0.0012%. CONCLUSION: Combining microsurgical techniques, pharmacological knowledge, and modern digital image technology, we were able to visualize small and capillary blood vessels even in small laboratory animals. By using our own computer analytical program, quantification of vessels was reliable, highly reproducible, and fast.

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.

Quantification of nanoparticles at the single-cell level: an overview about state-of-the-art techniques and their limitations.

With the increasing production and use of engineered nanoparticles it is crucial that their interaction with biological systems is understood. Due to the small size of nanoparticles, their identification and localization within single cells is extremely challenging. Therefore, various cutting-edge techniques are required to detect and to quantify metals, metal oxides, magnetic, fluorescent, as well as electron-dense nanoparticles. Several techniques will be discussed in detail, such as inductively coupled plasma atomic emission spectroscopy, flow cytometry, laser scanning microscopy combined with digital image restoration, as well as quantitative analysis by means of stereology on transmission electron microscopy images. An overview will be given regarding the advantages of those visualization/quantification systems, including a thorough discussion about limitations and pitfalls.

A computer-assisted microscopic analysis of bone tissue developed inside a polyactive plymer implanted into an equine articular surface

One of the most promising applications for the restoration of small or moderately sized focal articular lesions is mosaicplasty (MP). Although recurrent hemarthrosis is a rare complication after MP, recently, various strategies have been designed to find an effective filling material to prevent postoperative bleeding from the donor site. The porous biodegradable polymer Polyactive (PA; a polyethylene glycol terephthalate - polybutylene terephthalate copolymer) represents a promising solution in this respect. A histological evaluation of the longterm PA-filled donor sites obtained from 10 experimental horses was performed. In this study, attention was primarily focused on the bone tissue developed in the plug. A computer-assisted image analysis and quantitative polarized light microscopic measurements of decalcified, longitudinally sectioned, dimethylmethylene blue (DMMB)- and picrosirius red (PS) stained sections revealed that the coverage area of the bone trabecules in the PA-filled donor tunnels was substantially (25%) enlarged compared to the neighboring cancellous bone. For this quantification, identical ROIs (regions of interest) were used and compared. The birefringence retardation values were also measured with a polarized light microscope using monochromatic light. Identical retardation values could be recorded from the bone trabeculae developed in the PA and in the neighboring bone, which indicates that the collagen orientation pattern does not differ significantly among these bone trabecules. Based on our new data, we speculate that PA promotes bone formation, and some of the currently identified degradation products of PA may enhance osteo-conduction and osteoinduction inside the donor canal.

Reconstruction of 3D Vertebral Models from a Single 2D Lateral Fluoroscopic Image

Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). In this chapter, we present an alternative technique that can reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model. Cadaveric studies are conducted to verify the reconstruction accuracy by comparing the surface models reconstructed from a single lateral fluoroscopic image to the ground truth data from 3D CT segmentation. A mean reconstruction error between 0.7 and 1.4 mm was found.

Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.