Rafeef Abugharbieh

Bone Surface Localization in Ultrasound Using Image Phase-Based Features

Current practice in orthopedic surgery relies on intraoperative fluoroscopy as the main imaging modality for localization and visualization of bone tissue, fractures, implants and surgical tool positions. Ultrasound (US) has recently emerged as a potential nonionizing imaging alternative that promises safer operation while remaining relatively cheap and widely available. US images, however, often depict bone structures poorly, making automatic, accurate and robust localization of bone surfaces quite challenging. In this paper, we present a novel technique for automatic bone surface localization in US that uses local phase image information to derive symmetry-based features corresponding to tissue/bone interfaces through the use of 2-D Log-Gabor filters. We validate the performance of the proposed approach quantitatively using realistic phantom and in vitro experiments as well as qualitatively on in vivo data. Results demonstrate that the proposed technique detects bone surfaces with a localization mean error below 0.40 mm. Furthermore, small gaps between bone fragments can be detected with fracture displacement mean error below 0.33 mm for vertical misalignments, and 0.47 mm for horizontal misalignments.

Live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images

This paper incorporates multiscale vesselness filtering into the Livewire framework to simultaneously compute optimal medial axes and boundaries in vascular images. To this end, we extend the existing 2D graph search to 3D space to optimize not only for spatial variables (x, y), but also for radius values r at each node. In addition, we minimize change for both scale and the smallest principle curvature and incorporate vessel boundary evidence in our optimization. When compared to two sets of DRIVE expert manual tracings, our proposed technique reduced the overall segmentation task time by 68.2%, had a similarity ratio of 0.772 (0.775 between manual), and was 98.2% reproducible.

Symmetry-Based Scalable Lossless Compression of 3D Medical Image Data

We propose a novel symmetry-based technique for scalable lossless compression of 3D medical image data. The proposed method employs the 2D integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub-bands by exploiting the anatomical symmetries typically present in structural medical images. A modified version of the embedded block coder with optimized truncation (EBCOT), tailored according to the characteristics of the data, encodes the residual data generated after prediction to provide resolution and quality scalability. Performance evaluations on a wide range of real 3D medical images show an average improvement of 15% in lossless compression ratios when compared to other state-of-the art lossless compression methods that also provide resolution and quality scalability including 3D-JPEG2000, JPEG2000, and H.264/AVC intra-coding.

Motor reserve and novel area recruitment: amplitude and spatial characteristics of compensation in Parkinson's disease.

Motor symptoms of Parkinson’s disease (PD) do not appear until the majority of dopaminergic cells in the substantia nigra pars compacta are lost, suggesting significant redundancy or compensation in the motor systems affected by PD. Using functional magnetic resonance imaging, we examined whether compensation in PD is manifested by changes in amplitude and/or spatial extent of activity within normal networks (active motor reserve) and/or newly recruited regions [novel area recruitment (NAR)]. Ten PD subjects off and on medication and 10 age‐matched controls performed a visually guided sinusoidal force task at 0.25, 0.5 and 0.75 Hz. Regression was used to determine the combination of regions where activation amplitude scaled linearly with movement speed in controls. We then determined the activation of PD subjects in this network, as well as the corresponding PD network. To measure the spatial variance of activation, we used an invariant spatial feature approach. Control subjects monotonically increased activity within striato‐thalamo‐cortical and cerebello‐thalamo‐cortical regions with increasing movement speed. In PD subjects, the activity of this network at low speeds was similar to that in controls at higher speeds. Additionally, PD subjects off medication demonstrated NARs of the bilateral cerebellum and primary motor cortex, which were incompletely normalized by levodopa. Our results suggest that PD subjects tap into motor reserve, increase the spatial extent of activation and demonstrate NAR to maintain near‐normal motor output.

Shape (but not volume) changes in the thalami in Parkinson disease

BackgroundRecent pathological studies have suggested that thalamic degeneration may represent a site of non-dopaminergic degeneration in Parkinsons Disease (PD). Our objective was to determine if changes in the thalami could be non-invasively detected in structural MRI images obtained from subjects with Parkinson disease (PD), compared to age-matched controls.ResultsNo significant differences in volume were detected in the thalami between eighteen normal subjects and eighteen PD subjects groups. However significant (p < 0.03) shape differences were detected between the Left vs. Right thalami in PD, between the left thalami in PD and controls, and between the right thalami in PD and controls using a recently-developed, spherical harmonic-based representation.ConclusionSystematic changes in thalamic shape can be non-invasively assessed in PD in vivo. Shape changes, in addition to volume changes, may represent a new avenue to assess the progress of neurodegenerative processes. Although not directly discernable at the resolution of standard MRI, previous pathological studies would suggest that the shape changes detected in this study represent degeneration in the centre median-parafascicular (CM-Pf) complex, an area known to represent selective non-dopaminergic degeneration in PD.

Implementation and comparison of four different boundary detection algorithms for quantitative ultrasonic measurements of the human carotid artery

In this paper we examine four algorithms for automated ultrasonic boundary detection, and describe the application of these algorithms to the quantification of the intima-media thickness (IMT) in the human carotid artery. The first algorithm uses a dynamic programming approach to identify the boundary that minimizes a certain cost function. The second algorithm is based on finding points of maximum gradient. The third algorithm employs a mathematical model describing the intensity profile perpendicular to the two boundaries defining the IMT. The last algorithm is based on defining a template representing the intensity profile across boundary and applying a matched filter procedure to find the image region that best matches it. The authors also present a quantitative and qualitative comparison between the four algorithms examined. It is shown that the dynamic programming algorithm provides superior performance in terms of accuracy and robustness. The correlation coefficients between automated measurements and manually obtained reference values were 0.96, 0.94, 0.63, and 0.85 for the dynamic programming, the maximum gradient the model-based, and the matched filter algorithm, respectively (n=30).

Bone Segmentation and Fracture Detection in Ultrasound Using 3D Local Phase Features

3D ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted orthopaedic surgery (CAOS) applications. Automatic bone segmentation from US images, however, remains a challenge due to speckle noise and various other artifacts inherent to US. In this paper, we present intensity invariant three dimensional (3D) local image phase features, obtained using 3D Log-Gabor filter banks, for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. Our contributions include the novel extension of 2D phase symmetry features to 3D and their use in automatic extraction of bone surfaces and fractured fragments in 3D US. We validate our technique using phantom, in vitro, and in vivo experiments. Qualitative and quantitative results demonstrate remarkably clear segmentations results of bone surfaces with a localization accuracy of better than 0.62 mm and mean errors in estimating fracture displacements below 0.65 mm, which will likely be of strong clinical utility.

A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p<0.0001) and 10.18% (p<0.0001), respectively.

Focusing Effects of L-Dopa in Parkinson's Disease

Previous fMRI motor studies in Parkinsons disease (PD) have suggested that L‐dopa may “normalize” areas of hypo‐ and hyperactivity. However, results from these studies, which were largely based on analyzing BOLD signal amplitude, have been conflicting. Examining only amplitude changes at distinct loci may thus be inadequate in fully capturing the activation changes induced by L‐dopa. In this article, we extended prior analyses on the effects of L‐dopa by investigating both amplitude and spatial changes of brain activation before and after L‐dopa. Ten subjects with PD, both on and off medication, and ten healthy, age‐matched controls performed a visuo‐motor tracking task in which they sinusoidally squeezed a bulb at 0.25, 0.5, and 0.75 Hz. This task was contrasted with static squeezing to generate fMRI activation maps. To investigate the effects of L‐dopa, we examined the amplitude and spatial variance of the BOLD response within anatomically‐defined regions of interest (ROIs). L‐dopa had significant main effects on the amplitude of BOLD signal in bilateral primary motor cortex and left SMA. In contrast, L‐dopa‐mediated spatial changes were apparent in bilateral cerebellar hemispheres, M1, SMA, and right prefrontal cortex. Moreover, L‐dopa appeared to normalize the spatial distribution of ROI activation in PD to that of the controls. Specifically, L‐dopa had a “focusing” effect on activity—an effect more pronounced than the typically‐measured fMRI amplitude changes. This observation is consistent with modeling studies, which demonstrated that dopamine increases the signal‐to‐noise ratio at the neuronal level with a resultant focusing of representations at the macroscopic level. Hum Brain Mapp, 2010.

Efficient interactive 3D Livewire segmentation of complex objects with arbitrary topology

We present a novel interactive method based on a 3D Livewire approach for segmenting complex objects of arbitrary topologies. Our proposed technique automatically and seamlessly handles objects with branchings, concavities, protrusions, and non-spherical topologies with minimal user-input. Given sparse interactively segmented contours on orthogonal slices, our proposed method determines Livewire seedpoints on all slices in the third orthogonal direction, which are used to mimic user-guided segmentation. In doing so, our method pre-processes these points to increase algorithm robustness, and uses a novel seedpoint sorting method using ideas from L-systems Turtle algorithm. Moreover, we present a segmentation tool based on our proposed framework and demonstrate the robustness of our approach on real medical data. Results highlight the superior performance of the proposed method with validation tests on synthetic and real MRI and CT data, with segmentation reproducibility exceeding 95% and segmentation task time decreasing to less than 20% when compared to performing 2D Livewire on each volume slice.