Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated three-dimensional (3D) lumbar intervertebral disc (IVD) segmentation strategy from Magnetic Resonance Imaging (MRI) data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based template matching approach. Based on the estimated two-dimensional (2D) geometrical parameters, a 3D variable-radius soft tube model of the lumbar spine column is built by model fitting to the 3D data volume. Taking the geometrical information from the 3D lumbar spine column as constraints and segmentation initialization, the disc segmentation is achieved by a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

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.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

Improved Techniques for Acquisition and Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Detecting Vascular Permeability in the Central Nervous System

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique for quantitative assessment of the integrity of blood-brain barrier and blood-spinal cord barrier (BSCB) in the presence of central nervous system pathologies. However, the results of DCE-MRI show substantial variability. The high variability can be caused by a number of factors including inaccurate T1 estimation, insufficient temporal resolution and poor contrast-to-noise ratio. My thesis work is to develop improved methods to reduce the variability of DCE-MRI results. To obtain fast and accurate T1 map, the Look-Locker acquisition technique was implemented with a novel and truly centric k-space segmentation scheme. In addition, an original multi-step curve fitting procedure was developed to increase the accuracy of T1 estimation. A view sharing acquisition method was implemented to increase temporal resolution, and a novel normalization method was introduced to reduce image artifacts. Finally, a new clustering algorithm was developed to reduce apparent noise in the DCE-MRI data. The performance of these proposed methods was verified by simulations and phantom studies. As part of this work, the proposed techniques were applied to an in vivo DCE-MRI study of experimental spinal cord injury (SCI). These methods have shown robust results and allow quantitative assessment of regions with very low vascular permeability. In conclusion, applications of the improved DCE-MRI acquisition and analysis methods developed in this thesis work can improve the accuracy of the DCE-MRI results.

(Table 1) Isotopic composition of Sm and Nd in mantle restites of the Central Atlantic and in associated plutonic and volcanic rocks

Ultrabasic rock samples collected from two areas of the crustal zone of the Mid-Atlantic Ridge (MAR): (1) 13-17°N (near the intersection of the ridge axis with the 15°20'N prime fracture zone), and (2) 33°40'N prime (the western intersection of the MAR crest with the Heis fracture zone) were objects of this study. Samples of peridotite and of plutonic and volcanic rocks associated with it were used to measure their Sm/Nd, 143Nd/144Nd, and 147Sm/144Nd ratios, which allowed to test time and genetic relationships between evolution of mantle material under the ridge crest and products of its magmatic activity. Results of this work proved ubiquitous discrepancy between melting degree values of extremely depleted mantle peridotites in the MAR area between 14°N and 16°N, obtained using petrologic and geochemical methods. This discrepancy suggests large-scale interaction between mantle material and magmatic melts and fluids enriched in incompatible elements or fluids. The results obtained suggest that repeated melting of the mantle under the axial MAR zone is an universal characteristic of magmatism in low-velocity spreading centers. The results of this study also proved the crestal MAR zone in the Central Atlantic region show distinct indications of isotope-geochemical segmentation of the mantle. It is suggested that the geochemically anomalous MAR mantle peridotite in the zone of the MAR intersection with the 15°20'N prime fracture zone can be interpreted as fragments of mantle substrate, foreign for the Atlantic mantle north of the equator.

Sedimentology and mineralogy of surface sediments from the Chilean continental margin

The regional patterns of texture and composition of modern continental slope and pelagic sediments off Chile between 25°S and 43°S reflect the latitudinal segmentation of geological, morphological, and climatic features of the continental hinterland. Grain-size characteristics are controlled by the grain-size of source rocks, the weathering regime, and mode of sediment input (eolian off northern Chile vs fluvial further south). Bulk-mineral assemblages reveal a low grade of maturity. Regional variations are governed by the source-rock composition of the different geological terranes and the relative source-rock contribution of the Coastal Range and Andes, as controlled by the continental hydrology. The relative abundance of clay minerals is also predominantly influenced by the source-rock composition and partly by continental smectite neoformation. Latitudinal variations of illite crystallinities along the Chilean continental slope (and west of the Peru-Chile trench) clearly reflect modifications of the weathering regime which correspond to the strong climatic zonation of Chile.

Geochemistry of dikes and lavas from the north wall of the Hess Deep Rift

An investigation of ~1-m.y.-old dikes and lavas from the north wall of the Hess Deep Rift (2°15'N, 101°30'W) collected during Alvin expeditions provides a detailed view of the evolution of fast spreading oceanic crust. The study area encompasses 25 km of an east-west flow line, representing ~370,000 years of crustal accretion at the East Pacific Rise. Samples analyzed exhibit depleted incompatible trace element abundances and ratios [(La/Sm)N < 1]. Indices of fractionation (MgO), and incompatible element ratios (La/Sm, Nb/Ti) show no systematic trends along flow line. Rather, over short (<4 m) and long (~25 km) distances, significant variations are observed in major and trace element concentrations and ratios. Modeling of these variations attests to the juxtaposition of dikes of distinct parental magma compositions. These findings, combined with studies of segmentation of the subaxial magma chamber and lateral magma transport in dikes along rift-dominated systems, suggest a more realistic model of the magmatic system underlying the East Pacific Rise relative to the commonly assumed twodimensional model. In this model, melts from a heterogeneous mantle feed distinct portions of a segmented axial magma reservoir. Dikes emanating from these distinct reservoirs transport magma along axis, resulting in interleaved dikes and host lavas with different evolutionary histories. This model suggests the use of axial or flow line lava compositions to infer the evolution of axial magma chambers should be approached with caution because dikes may never erupt lava or may transport magma significant distances along axis and erupt lavas far from their axial magma chamber of origin.

Yangtze River Delta's system integration: institutional barriers and countermeasures

This paper addresses the issue of institutional barriers to the Yangtze River Delta integration and the resulting slow development. It analyzes the problems including the coordination of local interests and regional interests, market segmentation during the regional integration, competition for the local government‘s investment on the public goods, labor movement within the delta. The paper argues that to reduce the negative impacts of these barriers and to promote the further integration of the Yangtze Delta region, the central government should strengthen the coordination between local governments, regulate their disorderly competition and reform the official evaluation system.

MRI texture analysis as means for addressing rehydration and milk diffusion of cereals

Cereals microstructure is one of the primary quality attributes of cereals. Cereals rehydration and milk diffusion depends on such microstructure and thus, the crispiness and the texture, which will make it more palatable for the final consumer. Magnetic Resonance Imaging (MRI) is a very powerful topographic tool since acquisition parameter leads to a wide possibility for identifying textures, structures and liquids mobility. It is suited for non-invasive imaging of water and fats. Rehydration and diffusion cereals processes were measured by MRI at different times and using two different kinds of milk, varying their fat level. Several images were obtained. A combination of textural analysis (based on the analysis of histograms) and segmentation methods (in order to understand the rehydration level of each variety of cereals) were performed. According to the rehydration level, no advisable clustering behavior was found. Nevertheless, some differences were noticeable between the coating, the type of milk and the variety of cereals

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance