Automatic object segmentation of unstructured scenes using colour and depth maps

This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.

GrabCutSFM : how 3D information improves unsupervised object segmentation

In this paper, we present an unsupervised graph cut based object segmentation method using 3D information provided by Structure from Motion (SFM), called Grab- CutSFM. Rather than focusing on the segmentation problem using a trained model or human intervention, our approach aims to achieve meaningful segmentation autonomously with direct application to vision based robotics. Generally, object (foreground) and background have certain discriminative geometric information in 3D space. By exploring the 3D information from multiple views, our proposed method can segment potential objects correctly and automatically compared to conventional unsupervised segmentation using only 2D visual cues. Experiments with real video data collected from indoor and outdoor environments verify the proposed approach.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

A study of the diffusion characteristics of normal, delipidized and relipidized articular cartilage using magnetic resonance imaging

This paper assesses the capacity to provide semipermeability of the synthetic layer of surface-active phospholipids created to replace the depleted surface amorphous layer of articular cartilage. The surfaces of articular cartilage specimens in normal, delipidized, and relipidized conditions following incubation in dipalmitoyl-phosphatidylcholine and palmitoyl-oleoyl-phosphatidylcholine components of the joint lipid mixture were characterized nanoscopically with the atomic force microscope and also imaged as deuterium oxide (D2O) diffused transiently through these surfaces in a magnetic resonance imaging enclosure. The MR images were then used to determine the apparent diffusion coefficients in a purpose-built MATLAB®-based algorithm. Our results revealed that all surfaces were permeable to D2O, but that there was a significant difference in the semipermeability of the surfaces under the different conditions, relative to the apparent diffusion coefficients. Based on the results and observations, it can be concluded that the synthetic lipid that is deposited to replace the depleted SAL of articular cartilage is capable of inducing some level of semipermeability.

In situ imaging of catalytic etching on silver during methanol oxidation conditions by environmental scanning electron microscopy

Polycrystalline silver is used to catalytically oxidise methanol to formaldehyde. This paper reports the results of extensive investigations involving the use of environmental scanning electron microscopy (ESEM) to monitor structural changes in silver during simulated industrial reaction conditions. The interaction of oxygen, nitrogen, and water, either singly or in combination, with a silver catalyst at temperatures up to 973 K resulted in the appearance of a reconstructed silver surface. More spectacular was the effect an oxygen/methanol mixture had on the silver morphology. At a temperature of ca. 713 K pinholes were created in the vicinity of defects as a consequence of subsurface explosions. These holes gradually increased in size and large platelet features were created. Elevation of the catalyst temperature to 843 K facilitated the wholescale oxygen induced restructuring of the entire silver surface. Methanol reacted with subsurface oxygen to produce subsurface hydroxyl species which ultimately formed water in the subsurface layers of silver. The resultant hydrostatic pressure forced the silver surface to adopt a "hill and valley" conformation in order to minimise the surface free energy. Upon approaching typical industrial operating conditions widespread explosions occurred on the catalyst and it was also apparent that the silver surface was extremely mobile under the applied conditions. The interaction of methanol alone with silver resulted in the initial formation of pinholes primarily in the vicinity of defects, due to reaction with oxygen species incorporated in the catalyst during electrochemical synthesis. However, dramatic reduction in the hole concentration with time occurred as all the available oxygen became consumed. A remarkable correlation between formaldehyde production and hole concentration was found.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Numerical simulation of anomalous diffusion with application to medical imaging

The first objective of this project is to develop new efficient numerical methods and supporting error and convergence analysis for solving fractional partial differential equations to study anomalous diffusion in biological tissue such as the human brain. The second objective is to develop a new efficient fractional differential-based approach for texture enhancement in image processing. The results of the thesis highlight that the fractional order analysis captured important features of nuclear magnetic resonance (NMR) relaxation and can be used to improve the quality of medical imaging.

Prospective imaging study of asymptomatic patellar tendinopathy in elite junior basketball players

To evaluate the ability of ultrasonography to predict eventual symptoms in an at-risk population, 52 elite junior basketball players' patellar tendons were studied at baseline and again 16 months later. The group consisted of 10 study tendons (ultrasonographically hypoechoic at baseline) and 42 control tendons (ultrasonographically normal at baseline). By design, all tendons were asymptomatic at baseline. No differences were noted between subjects and controls at baseline for age, height, weight, training hours, and vertical jump. Functional (P < 0.01) and symptomatic outcome (P < 0.05) were poorer for subjects' tendons than for controls. Relative risk for developing symptoms of jumper's knee was 4.2 times greater in case tendons than in control tendons. Men were more likely to develop ultrasonographic changes than women (P < 0.025), and they also had significantly increased training hours per week (P < 0.01) in the study period. Half (50%) of abnormal tendons in women became ultrasonographically normal in the study period. Our data suggest that presence of an ultrasonographic hypoechoic area is associated with a greater risk of developing jumper's knee symptoms. Ultrasonographic patellar tendon changes may resolve, but this is not necessary for an athlete to become asymptomatic. Qualitative or quantitative analysis of baseline ultrasonographic images revealed it was not possible to predict which tendons would develop symptoms or resolve ultrasonographically.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Quantitative ultrasound computed tomography imaging of PAGAT radiation dosimetry gel

This research developed and scientifically validated a new ultrasound transmission computed tomography system with the aim of quantitative assessment of a polymer gel dosimeter including dose response verification of ultrasonic parameters of attenuation, velocity and broadband ultrasound attenuation (BUA). This work was the first to investigate and report ultrasound frequency dependent attenuation in a gel dosimeter, demonstrating a dose dependence.

Characterisation of microvasulature changes after closed soft tissue trauma by contrast enhanced micro-CT imaging

INTRODUCTION It is known that the vascular morphology and functionality are changed following closed soft tissue trauma (CSTT) [1], and bone fractures [2]. The disruption of blood vessels may lead to hypoxia and necrosis. Currently, most clinical methods for the diagnosis and monitoring of CSTT with or without bone fractures are primarily based on qualitative measures or practical experience, making the diagnosis subjective and inaccurate. There is evidence that CSTT and early vascular changes following the injury delay the soft tissue tissue and bone healing [3]. However, a precise qualitative and quantitative morphological assessment of vasculature changes after trauma is currently missing. In this research, we aim to establish a diagnostic framework to assess the 3D vascular morphological changes after standardized CSTT in a rat model qualitatively and quantitatively using contrast-enhanced micro-CT imaging. METHODS An impact device was used for the application of a controlled reproducible CSTT to the left thigh (Biceps Femoris) of anaesthetized male Wistar rats. After euthanizing the animals at 6 hours, 24 hours, 3 days, 7 days, or 14 days after trauma, CSTT was qualitatively evaluated by macroscopic visual observation of the skin and muscles. For visualization of the vasculature, the blood vessels of sacrificed rats were flushed with heparinised saline and then perfused with a radio-opaque contrast agent (Microfil, MV 122, Flowtech, USA) using an infusion pump. After allowing the contrast agent to polymerize overnight, both hind-limbs were dissected, and then the whole injured and contra-lateral control limbs were imaged using a micro-CT scanner (µCT 40, Scanco Medical, Switzerland) to evaluate the vascular morphological changes. Correlated biopsy samples were also taken from the CSTT region of both injured and control legs. The morphological parameters such as the vessel volume ratio (VV/TV), vessel diameter (V.D), spacing (V.Sp), number (V.N), connectivity (V.Conn) and the degree of anisotropy (DA) were then quantified by evaluating the scans of biopsy samples using the micro-CT imaging system. RESULTS AND DISCUSSION A qualitative evaluation of the CSTT has shown that the developed impact protocols were capable of producing a defined and reproducible injury within the region of interest (ROI), resulting in a large hematoma and moderate swelling in both lateral and medial sides of the injured legs. Also, the visualization of the vascular network using 3D images confirmed the ability to perfuse the large vessels and a majority of the microvasculature consistently (Figure 1). Quantification of the vascular morphology obtained from correlated biopsy samples has demonstrated that V.D and V.N and V.Sp were significantly higher in the injured legs 24 hours after impact in comparison with the control legs (p<0.05). The evaluation of the other time points is currently progressing. CONCLUSIONS The findings of this research will contribute to a better understanding of the changes to the vascular network architecture following traumatic injuries and during healing process. When interpreted in context of functional changes, such as tissue oxygenation, this will allow for objective diagnosis and monitoring of CSTT and serve as validation for future non-invasive clinical assessment modalities.

A novel characterisation model for microvasulature changes following closed soft tissue trauma using micro-CT imaging

INTRODUCTION There is evidence that the reduction of blood perfusion caused by closed soft tissue trauma (CSTT) delays the healing of the affected soft tissues and bone [1]. We hypothesise that the characterisation of vascular morphology changes (VMC) following injury allows us to determine the effect of the injury on tissue perfusion and thereby the severity of the injury. This research therefore aims to assess the VMC following CSTT in a rat model using contrast-enhanced micro-CT imaging. METHODOLOGY A reproducible CSTT was created on the left leg of anaesthetized rats (male, 12 weeks) with an impact device. After euthanizing the animals at 6 and 24 hours following trauma, the vasculature was perfused with a contrast agent (Microfil, Flowtech, USA). Both hind-limbs were dissected and imaged using micro-CT for qualitative comparison of the vascular morphology and quantification of the total vascular volume (VV). In addition, biopsy samples were taken from the CSTT region and scanned to compare morphological parameters of the vasculature between the injured and control limbs. RESULTS AND DISCUSSION While the visual observation of the hindlimb scans showed consistent perfusion of the microvasculature with microfil, enabling the identification of all major blood vessels, no clear differences in the vascular architecture were observed between injured and control limbs. However, overall VV within the region of interest (ROI)was  measured to be higher for the injured limbs after 24h. Also, scans of biopsy samples demonstrated that vessel diameter and density were higher in the injured legs 24h after impact. CONCLUSION We believe these results will contribute to the development of objective diagnostic methods for CSTT based on changes to the microvascular morphology as well as aiding in the validation of future non-invasive clinical assessment modalities.

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

Diffusion-sensitive magnetic resonance spectroscopy and imaging in biomedical sciences

We present a mini-review of the development and contemporary applications of diffusion-sensitive nuclear magnetic resonance (NMR) techniques in biomedical sciences. Molecular diffusion is a fundamental physical phenomenon present in all biological systems. Due to the connection between experimentally measured diffusion metrics and the microscopic environment sensed by the diffusing molecules, diffusion measurements can be used for characterisation of molecular size, molecular binding and association, and the morphology of biological tissues. The emergence of magnetic resonance was instrumental to the development of biomedical applications of diffusion. We discuss the fundamental physical principles of diffusion NMR spectroscopy and diffusion MR imaging. The emphasis is placed on conceptual understanding, historical evolution and practical applications rather than complex technical details. Mathematical description of diffusion is presented to the extent that it is required for the basic understanding of the concepts. We present a wide range of spectroscopic and imaging applications of diffusion magnetic resonance, including colloidal drug delivery vehicles; protein association; characterisation of cell morphology; neural fibre tractography; cardiac imaging; and the imaging of load-bearing connective tissues. This paper is intended as an accessible introduction into the exciting and growing field of diffusion magnetic resonance.