Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

The Rapakivi texture of feldspars in a plutonic mixing environment - A dissolution-recrystallization process

A new hypothesis is formulated to explain the development of rapakivi texture in and around the mafic enclaves of porphyritic granitoids, i.e. in environments involving magma mixing and mingling. The formation of a plagioclase mantle around alkali feldspar megacrysts is attributed to the localized presence of a melt resulting from the reaction of these megacrysts, with host hybrid magma with which they are in disequilibrium. This feldspathic melt adheres to the resorbed crystals and is virtually immiscible with the surrounding magma. Its composition is modified in terms of the relative proportions of K2O, Na2O, and CaO through selective diffusion of these elements, thus allowing the specific crystallization of andesine. With decreasing temperature, the K-feldspar, again stable, crystallizes along with the plagioclase, leading to mixed mantle structures.

MP2RAGE, a self bias-field corrected sequence for improved segmentation and T-1-mapping at high field

The large spatial inhomogeneity in transmit B, field (B-1(+)) observable in human MR images at hi h static magnetic fields (B-0) severely impairs image quality. To overcome this effect in brain T-1-weighted images the, MPRAGE sequence was modified to generate two different images at different inversion times MP2RAGE By combining the two images in a novel fashion, it was possible to create T-1-weigthed images where the result image was free of proton density contrast, T-2* contrast, reception bias field, and, to first order transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B-1(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T-1-weighted images, acquired within 12 min, high-resolution 3D T-1 maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T-1 maps were validated in phantom experiments. In humans, the T, values obtained at 7 T were 1.15 +/- 0.06 s for white matter (WM) and 1.92 +/- 0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min the T-1 values obtained (0.81 +/- 0.03 S for WM and 1.35 +/- 0.05 for GM) were once again found to be in very good agreement with values in the literature. (C) 2009 Elsevier Inc. All rights reserved.

Lumbar spine texture enhances 10-year fracture probability assessment.

We found that lumbar spine texture analysis using trabecular bone score (TBS) is a risk factor for MOF and a risk factor for death in a retrospective cohort study from a large clinical registry for the province of Manitoba, Canada. INTRODUCTION: FRAX® estimates the 10-year probability of major osteoporotic fracture (MOF) using clinical risk factors and femoral neck bone mineral density (BMD). Trabecular bone score (TBS), derived from texture in the spine dual X-ray absorptiometry (DXA) image, is related to bone microarchitecture and fracture risk independently of BMD. Our objective was to determine whether TBS provides information on MOF probability beyond that provided by the FRAX variables. METHODS: We included 33,352 women aged 40-100 years (mean 63 years) with baseline DXA measurements of lumbar spine TBS and femoral neck BMD. The association between TBS, the FRAX variables, and the risk of MOF or death was examined using an extension of the Poisson regression model. RESULTS: During the mean of 4.7 years, 1,754 women died and 1,872 sustained one or more MOF. For each standard deviation reduction in TBS, there was a 36 % increase in MOF risk (HR 1.36, 95 % CI 1.30-1.42, p < 0.001) and a 32 % increase in death (HR 1.32, 95 % CI 1.26-1.39, p < 0.001). When adjusted for significant clinical risk factors and femoral neck BMD, lumbar spine TBS was still a significant predictor of MOF (HR 1.18, 95 % CI 1.12-1.23) and death (HR 1.20, 95 % CI 1.14-1.26). Models for estimating MOF probability, accounting for competing mortality, showed that low TBS (10th percentile) increased risk by 1.5-1.6-fold compared with high TBS (90th percentile) across a broad range of ages and femoral neck T-scores. CONCLUSIONS: Lumbar spine TBS is able to predict incident MOF independent of FRAX clinical risk factors and femoral neck BMD even after accounting for the increased death hazard.

Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.

We present a method for segmenting white matter tracts from high angular resolution diffusion MR. images by representing the data in a 5 dimensional space of position and orientation. Whereas crossing fiber tracts cannot be separated in 3D position space, they clearly disentangle in 5D position-orientation space. The segmentation is done using a 5D level set method applied to hyper-surfaces evolving in 5D position-orientation space. In this paper we present a methodology for constructing the position-orientation space. We then show how to implement the standard level set method in such a non-Euclidean high dimensional space. The level set theory is basically defined for N-dimensions but there are several practical implementation details to consider, such as mean curvature. Finally, we will show results from a synthetic model and a few preliminary results on real data of a human brain acquired by high angular resolution diffusion MRI.

Detection of matchings in a sequence of underwater images through texture analysis

This paper presents an approach to ameliorate the reliability of the correspondence points relating two consecutive images of a sequence. The images are especially difficult to handle, since they have been acquired by a camera looking at the sea floor while carried by an underwater robot. Underwater images are usually difficult to process due to light absorption, changing image radiance and lack of well-defined features. A new approach based on gray-level region matching and selective texture analysis significantly improves the matching reliability

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

An information theoretic framework for image segmentation

In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram

Segmentation automatique de la matière grise et de la matière blanche par un algorithme génétique : une étude de validation

Résumé Objectif: l'observation des variations de volume de la matière grise (MG), de la matière blanche (MB), et du liquide céphalo-rachidien (LCR) est particulièrement utile dans l'étude de nombreux processus physiopathologiques, la mesure quantitative 'in vivo' de ces volumes présente un intérêt considérable tant en recherche qu'en pratique clinique. Cette étude présente et valide une méthode de segmentation automatique du cerveau avec mesure des volumes de MG et MB sur des images de résonance magnétique. Matériel et Méthode: nous utilisons un algorithme génétique automatique pour segmenter le cerveau en MG, MB et LCR à partir d'images tri-dimensionnelles de résonance magnétique en pondération Ti. Une étude morphométrique a été conduite sur 136 sujets hommes et femmes de 15 à 74 ans. L'algorithme a ensuite été validé par 5 approches différentes: I. Comparaison de mesures de volume sur un cerveau de cadavre par méthode automatique et par mesure de déplacement d'eau selon la méthode d'Archimède. 2. Comparaison de mesures surfaces sur des images bidimensionnelles segmentées soit par un traçage manuel soit par la méthode automatique. 3. Evaluation de la fiabilité de la segmentation par acquisitions et segmentations itératives du même cerveau. 4. Les volumes de MG, MB et LCR ont été utilisés pour une étude du vieillissement normal de la population. 5. Comparaison avec les données existantes de la littérature. Résultats: nous avons pu observer une variation de la mesure de 4.17% supplémentaire entre le volume d'un cerveau de cadavre mesuré par la méthode d'Archimède, en majeure partie due à la persistance de tissus après dissection_ La comparaison des méthodes de comptage manuel de surface avec la méthode automatique n'a pas montré de variation significative. L'épreuve du repositionnement du même sujet à diverses reprises montre une très bonne fiabilité avec une déviation standard de 0.46% pour la MG, 1.02% pour la MB et 3.59% pour le LCR, soit 0.19% pour le volume intracrânien total (VICT). L'étude morphométrique corrobore les résultats des études anatomiques et radiologiques existantes. Conclusion: la segmentation du cerveau par un algorithme génétique permet une mesure 100% automatique, fiable et rapide des volumes cérébraux in vivo chez l'individu normal.

Multimodal evaluation for medical image segmentation

This paper is a joint effort between five institutionsthat introduces several novel similarity measures andcombines them to carry out a multimodal segmentationevaluation. The new similarity measures proposed arebased on the location and the intensity values of themisclassified voxels as well as on the connectivity andthe boundaries of the segmented data. We showexperimentally that the combination of these measuresimprove the quality of the evaluation. The study that weshow here has been carried out using four differentsegmentation methods from four different labs applied toa MRI simulated dataset of the brain. We claim that ournew measures improve the robustness of the evaluation andprovides better understanding about the differencebetween segmentation methods.

Trabecular bone score (TBS) the new parameter of 2D texture analysis for the evaluation of 3D bone micro architecture status

X-ray is a technology that is used for numerous applications in the medical field. The process of X-ray projection gives a 2-dimension (2D) grey-level texture from a 3- dimension (3D) object. Until now no clear demonstration or correlation has positioned the 2D texture analysis as a valid indirect evaluation of the 3D microarchitecture. TBS is a new texture parameter based on the measure of the experimental variogram. TBS evaluates the variation between 2D image grey-levels. The aim of this study was to evaluate existing correlations between 3D bone microarchitecture parameters - evaluated from μCT reconstructions - and the TBS value, calculated on 2D projected images. 30 dried human cadaveric vertebrae were acquired on a micro-scanner (eXplorer Locus, GE) at isotropic resolution of 93 μm. 3D vertebral body models were used. The following 3D microarchitecture parameters were used: Bone volume fraction (BV/TV), Trabecular thickness (TbTh), trabecular space (TbSp), trabecular number (TbN) and connectivity density (ConnD). 3D/2D projections has been done by taking into account the Beer-Lambert Law at X-ray energy of 50, 100, 150 KeV. TBS was assessed on 2D projected images. Correlations between TBS and the 3D microarchitecture parameters were evaluated using a linear regression analysis. Paired T-test is used to assess the X-ray energy effects on TBS. Multiple linear regressions (backward) were used to evaluate relationships between TBS and 3D microarchitecture parameters using a bootstrap process. BV/TV of the sample ranged from 18.5 to 37.6% with an average value at 28.8%. Correlations' analysis showedthat TBSwere strongly correlatedwith ConnD(0.856≤r≤0.862; p<0.001),with TbN (0.805≤r≤0.810; p<0.001) and negatively with TbSp (−0.714≤r≤−0.726; p<0.001), regardless X-ray energy. Results show that lower TBS values are related to "degraded" microarchitecture, with low ConnD, low TbN and a high TbSp. The opposite is also true. X-ray energy has no effect onTBS neither on the correlations betweenTBS and the 3Dmicroarchitecture parameters. In this study, we demonstrated that TBS was significantly correlated with 3D microarchitecture parameters ConnD and TbN, and negatively with TbSp, no matter what X-ray energy has been used. This article is part of a Special Issue entitled ECTS 2011. Disclosure of interest: None declared.

3D Automated Lung Nodule Segmentation in HRCT

A fully-automated 3D image analysis method is proposed to segment lung nodules in HRCT. A specific gray-level mathematical morphology operator, the SMDC-connection cost, acting in the 3D space of the thorax volume is defined in order to discriminate lung nodules from other dense (vascular) structures. Applied to clinical data concerning patients with pulmonary carcinoma, the proposed method detects isolated, juxtavascular and peripheral nodules with sizes ranging from 2 to 20 mm diameter. The segmentation accuracy was objectively evaluated on real and simulated nodules. The method showed a sensitivity and a specificity ranging from 85% to 97% and from 90% to 98%, respectively.