Level Set Segmentation Algorithm Research Articles

Vegetation segmentation based on variational level set using multi-channel local wavelet texture and color

The existing spectrum index-based methods for detecting vegetation coverage suffer from an over-dependence on spectrum. To address these issues, this paper proposes a graph cut-based variational level set segmentation algorithm that combines multi-channel local wavelet texture (MCLWT) and color. First, the prior color is generated by automatic estimation based on the mathematical morphology with a color histogram. Then, local wavelet texture features are extracted using a multi-scale and orientation Gabor wavelet transformation followed by local median and entropy filtering. Next, in addition to the energy of color, that of MCLWT is integrated into the variational level set model based on kernel density estimation. Consequently, all energies are integrated into the graph cut-based variational level set model. Finally, the proposed energy functional is made convex to obtain a global optimal solution, and a primal-dual algorithm with global relabeling is adopted to accelerate the evolution of the level sets. A comparison of the segmentation results from our proposed algorithm and other state-of-the-art algorithms showed that our algorithm effectively reduces the over-dependence on color and yields more accurate results in detecting vegetation coverage.

Abnormal Region Extraction from MR Brain Images using Hybrid Approach

Automatic brain abnormality segmentation from magnetic resonance images is a key task that is performed by computer aided algorithm or manual extraction by a medical expert. The regions are often partitioned based on the similarities of intensities that persist in a particular region. MR brain image segmentation is a critical step that helps to identify the abnormal region. Accurate identification of this abnormal region helps the radiologist and surgeons in surgical process and research. Through this paper we present a hybrid approach of algorithms based on clustering approach like region and edge based algorithm involved in segmenting abnormal region from MR brain images. The method is an integration of region based (pillar K-means) and edge based (level set) segmentation algorithm that aims to segment the abnormal region precisely. Experimental results show that the proposed approach could attain segmentation efficiency of 89.2%, mitigating the segmentation errors that were prevalent with region or edge based algorithms.

3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images

Detailed and accurate characteristics of preimplantation embryos are fundamental for a deep understanding of their development. Recent studies indicate that various geometric features of cells, such as size, shape, volume, and position play a significant role in embryo growth. However, a quantitative assessment of these characteristics first needs a segmentation of the individual cells. The manual separation and labeling of cells is extremely inefficient, and an automated approach is highly desirable. This paper presents an automatic method for early stage embryo segmentation into its constituent cells and membranes using three-dimensional (3D) data. The input data consist of two Z-stacks of fluorescence microscope images containing nuclei and membranes. The method uses a 3D level set segmentation algorithm. Its evaluation is based on a dataset composed of 20 mouse embryos, each with 4---32 blastomeres. Segmentation accuracy was evaluated by calculating F-scores with ground truth obtained by manually labeling desired regions. We also compared output of our method with the one acquired with a watershed algorithm. The proposed approach was able to achieve more than $$90\%$$90% accuracy for embryos with 4 and 8 cells, while for embryos with higher number of cells it was lower, reaching $$75\%$$75% for 32-cell embryo.

Level Set Segmentation Algorithm for High-Resolution Polarimetric SAR Images Based on a Heterogeneous Clutter Model

In this paper, a heterogeneous clutter model named ${\mathcal L}$ distribution is introduced into the level set method for segmentation of high-resolution polarimetric synthetic aperture radar (PolSAR) images. Level set methods are robust and effective techniques for segmentation. However, traditional level set methods for PolSAR data are based on the complex Wishart distribution, which is not an applicable model to high-resolution PolSAR images and heterogeneous regions such as forest and urban areas. The ${\mathcal L}$ distribution is proved to be a highly flexible model for multilook PolSAR data, which is based on the product model with a generalized-gamma-distributed texture component. The ${\mathcal L}$ -model-based level set segmentation method is assessed using C-band, X-band, and L-band PolSAR data acquired by RADARSAT-2, TerraSAR-X, and ESAR sensors, respectively. Experimental results show that the ${\mathcal L}$ -distribution model has a better capacity for characterizing high-resolution PolSAR data, especially for extremely heterogeneous regions. Compared with the Wishart-model-based and Kummer-U-model-based level set methods and Markov random field based methods, it is observed that the proposed level set algorithm can obtain more precise segmentation results.

Auroral oval segmentation using dual level set based on local information

ABSTRACTThe extraction of auroral ovals from images acquired by the Ultraviolet Imager is notoriously difficult due to low contrast, inhomogeneity and the presence of dayglow. In this paper, to address these issues, we propose an improved level set segmentation algorithm by incorporating the shape feature and intensity distribution of the auroral oval into the variational framework. The shape term is proposed to keep the annular ring appearance of the auroral oval and avoid the boundary leak by imposing a distance constraint on the inner and outer boundaries. The local information term tackles the difficulty of intensity inhomogeneity by utilizing the statistical distribution in the local window. Experimental results demonstrate that the proposed method obtains more accurate inner and outer boundaries of the auroral oval, comparing to the existing pixel based and level set based methods.

Community delivery of semiautomated fractal analysis tool in cardiac mr for trabecular phenotyping.

To report the development of easy-to-use magnetic resonance imaging (MRI) fractal tools deployed on platforms accessible to all. The trabeculae of the left ventricle vary in health and disease but their measurement is difficult. Fractal analysis of cardiac MR images can measure trabecular complexity as a fractal dimension (FD). This Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the local Institutional Review Board. Participants provided written informed consent. The original MatLab implementation (region-based level set segmentation and box-counting algorithm) was recoded for two platforms (OsiriX and a clinical MR reporting platform [cvi42 , Circle Cardiovascular Imaging, Calgary, Canada]). For validation, 100 subjects were scanned at 1.5T and 20 imaged twice for interstudy reproducibility. Cines were analyzed by the three tools and FD variability determined. Manual trabecular delineation by an expert reader (R1) provided ground truth contours for validation of segmentation accuracy by point-to-curve (P2C) distance estimates. Manual delineation was repeated by R1 and a second reader (R2) on 15 cases for intra/interobserver variability. FD by OsiriX and the clinical MR reporting platform showed high correlation with MatLab values (correlation coefficients: 0.96 [95% CI: 0.95-0.97] and 0.96 [0.95-0.96]) and high interstudy and intraplatform reproducibility. Semiautomated contours in OsiriX and the clinical MR reporting platform were highly correlated with ground truth contours evidenced by low P2C errors: 0.882 ± 0.76 mm and 0.709 ± 0.617 mm. Validity of ground truth contours was inferred from low P2C errors between readers (R1-R1: 0.798 ± 0.718 mm; R1-R2: 0.804 ± 0.649 mm). This set of accessible fractal tools that measure trabeculation in the heart have been validated and released to the cardiac MR community (http://j.mp/29xOw3B) to encourage novel clinical applications of fractals in the cardiac imaging domain. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1082-1088.

Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms

Background and objectiveAccurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation. MethodsThis paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework. For this purpose, a realistic reference model of an intracranial arterial tree was developed based on a real Time-of-Flight (TOF) MRA data set. With this specific geometry blood flow was simulated and a series of TOF images was synthesized using various acquisition protocol parameters and signal-to-noise ratios. The synthesized arterial tree was then reconstructed using a level-set segmentation algorithm available in the Vascular Modeling Toolkit (VMTK). Moreover, to present versatile application of the proposed methodology, validation was also performed for two alternative techniques: a multi-scale vessel enhancement filter and the Chan–Vese variant of the level-set-based approach, as implemented in the Insight Segmentation and Registration Toolkit (ITK). The segmentation results were compared against the reference model. ResultsThe accuracy in determining the vessels centerline courses was very high for each tested segmentation algorithm (mean error rate = 5.6% if using VMTK). However, the estimated radii exhibited deviations from ground truth values with mean error rates ranging from 7% up to 79%, depending on the vessel size, image acquisition and segmentation method. ConclusionsWe demonstrated the practical application of the designed MRA simulator as a reliable tool for quantitative validation of MRA image processing algorithms that provides objective, reproducible results and is observer independent.

Harbor Detection in Polarimetric SAR Images Based on the Characteristics of Parallel Curves

This letter proposes a harbor detection method by extracting specific paralleled contours of harbors in polarimetric synthetic aperture radar (SAR) images. First, land and sea are partitioned by the region-based level set segmentation algorithm which is applied to the extraction of the volume scattering component. Then, parallel curves are modeled by a series of parallel line segments. They are detected by finding out and connecting paralleled line segments given certain criteria. The detected parallel curves are merged according to a distance measurement so that the harbor contour is finally detected. RADARSAT-2 polarimetric SAR data covering an area in Singapore are used to test the harbor detection method. Experimental results show that the method improves sea–land segmentation accuracy and extracts all parallel curves along the coast correctly. The method enables an accurate detection of harbors.

Norms for size and closure time of anterior fontanelle: A study on babies in Nagpur region

Intraventricular hemorrhage (IVH) or bleed within the cerebral ventricles is a common condition among very low birth weight pre-term neonates. The prognosis for these patients is worsened should they develop progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilatation (PHVD), which occurs in 10–30% of IVH patients. Accurate measurement of ventricular volume would be valuable information and could be used to predict PHVD and determine whether that specific patient with ventricular dilatation requires treatment. While the monitoring of PHVD in infants is typically done by repeated transfontanell 2D ultrasound (US) and not MRI, once the patient's fontanels have closed around 12–18 months of life, the follow-up patient scans are done by MRI. Manual segmentation of ventricles from MR images is still seen as a gold standard. However, it is extremely time- and labor-consuming, and it also has observer variability. This paper proposes an accurate multiphase geodesic level-set segmentation algorithm for the extraction of the cerebral ventricle system of pre-term PHVD neonates from 3D T1 weighted MR images. The proposed segmentation algorithm makes use of multi-region segmentation technique associated with spatial priors built from a multi-atlas registration scheme. The leave-one-out cross validation with 19 patients with mild enlargement of ventricles and 7 hydrocephalus patients shows that the proposed method is accurate, suggesting that the proposed approach could be potentially used for volumetric and morphological analysis of the ventricle system of IVH neonatal brains in clinical practice.

Crack detection algorithm for oil rock core industrial CT image

In order to meet the requirement of crack region segmentation, based on the characteristics of oil sedimentary core industrial CT image, the level set segmentation algorithm is improved. Firstly, a median filter is used for denoising, and a C-V model is adopted to get the coarse image segmentation result. The background area and the core area are separated, and the contour of the core is obtained. Secondly, the gray level of the background region is adjusted, so that it is equal to the average gray level of the core area. Finally, the Region-Scalable Fitting (RSF) model is adopted to precisely segment the crack region. For the core image of Gauss noise pollution, the method of non-local mean with the neighborhood window is used, and then the improved level set algorithm is used. The experimental results show that the improved level set algorithm for image segmentation is effective for oil rock core CT image.

3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets

Intraventricular hemorrhage (IVH) or bleed within the cerebral ventricles is a common condition among very low birth weight pre-term neonates. The prognosis for these patients is worsened should they develop progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilatation (PHVD), which occurs in 10–30% of IVH patients. Accurate measurement of ventricular volume would be valuable information and could be used to predict PHVD and determine whether that specific patient with ventricular dilatation requires treatment. While the monitoring of PHVD in infants is typically done by repeated transfontanell 2D ultrasound (US) and not MRI, once the patient's fontanels have closed around 12–18months of life, the follow-up patient scans are done by MRI. Manual segmentation of ventricles from MR images is still seen as a gold standard. However, it is extremely time- and labor-consuming, and it also has observer variability. This paper proposes an accurate multiphase geodesic level-set segmentation algorithm for the extraction of the cerebral ventricle system of pre-term PHVD neonates from 3D T1 weighted MR images. The proposed segmentation algorithm makes use of multi-region segmentation technique associated with spatial priors built from a multi-atlas registration scheme. The leave-one-out cross validation with 19 patients with mild enlargement of ventricles and 7 hydrocephalus patients shows that the proposed method is accurate, suggesting that the proposed approach could be potentially used for volumetric and morphological analysis of the ventricle system of IVH neonatal brains in clinical practice.

Mass segmentation in mammograms using a kernel-based fuzzy level set method

In this paper, a Kernel-Based Fuzzy Level Set (KFLS) method is proposed to facilitate the automatic segmentation of lesions in mammogram image. Initially, the mammogram image is pre-processed to remove the noises and to extract the breast profile. Kernel-based Fuzzy C-Means (KFCM) segmentation is applied to segment the pre-processed image into a number of clusters based on the breast structure. The cluster with Region of Interest (ROI) is automatically identified to remove background and normal tissues. Finally, the mass region extracted from the KFCM is used as an initial contour for the level set segmentation to refine the mass boundary. The result of KFCM is also used to automatically initialise the control parameters of level set segmentation algorithm. This methodology is validated on 300 mammogram images of DDSM database with diagnosed mass. The experimental results demonstrate that the proposed method achieves a sensitivity of 93.32% and accuracy of 94.31%.

Segmentation of Inhomogeneous Skin Tissues in High-frequency 3D Ultrasound Images, the Advantage of Non-parametric Log-likelihood Methods

We propose a multi-purpose level-set segmentation algorithm to detect the boundary of tumors and tissues in high-frequency 3D ultrasound images of the skin. Whereas most proposed algorithms assume a specific (e.g. Rayleigh) distribution of the speckle noise, we do not make such assumption and use non-parametric Parzen estimates of the distribution. We discuss the advantage of the method on synthetic and clinical images of the skin and tumors.

A fully automated and reproducible level-set segmentation approach for generation of MR-based attenuation correction map of PET images in the brain employing single STE-MR imaging modality.

Generating MR-based attenuation correction map (μ-map) for quantitative reconstruction of PET images still remains a challenge in hybrid PET/MRI systems, mainly because cortical bone structures are indistinguishable from proximal air cavities in conventional MR images. Recently, development of short echo-time (STE) MR imaging sequences, has shown promise in differentiating cortical bone from air. However, on STE-MR images, the bone appears with discontinuous boundaries. Therefore, segmentation techniques based on intensity classification, such as thresholding or fuzzy C-means, fail to homogeneously delineate bone boundaries, especially in the presence of intrinsic noise and intensity inhomogeneity. Consequently, they cannot be fully automatized, must be fine-tuned on the case-by-case basis, and require additional morphological operations for segmentation refinement. To overcome the mentioned problems, in this study, we introduce a new fully automatic and reproducible STE-MR segmentation approach exploiting level-set in a clustering-based intensity inhomogeneity correction framework to reliably delineate bone from soft tissue and air. MR images were acquired on a clinical 1.5T MRI System, MAGNETOM Avanto, using a FLASH 3D pulse sequence with TE=1.1ms, TR=12ms, flip angle=18°, voxel size=1.2×1.2×2mm3. For segmentation of the STE-MR images into three regions, consisting of bone, air and soft tissue, a region-based level-set segmentation algorithm was applied. In this technique, k-means clustering is applied to estimate the intensity properties of each region for bias field correction simultaneously with the level-set segmentation. This algorithm incorporates both intensity and spatial information to define continuous boundaries. The quantitative assessment outcomes of the segmentation performance yielded an average of 89%, 82%, 91%, and 73% for the accuracy, sensitivity, specificity and dice scores in bone segmentation, respectively. The results suggest that the proposed fully automatic segmentation approach can reliably discriminate bony structures from the neighboring air and soft tissue in STE-MR images, which is suitable for generating accurate μ-maps in clinical PET/MR applications.

A morphological algorithm for measuring angle of airway branches in lung CT images.

Accurate measurement of human airway lumen bifurcation angle in the bronchial tree may be an important parameter for evidence of pulmonary diseases. Here, we describe a new method for recognizing and following airway bifurcation over numerous contiguous CT images. Based on morphological properties of airways and specific changes to airway properties while digitally navigating through the bifurcation, our method is able to track airways through several levels of bifurcation. Then, based on the center of the lumen area, determined by the level set segmentation algorithm, we estimate the centerline of each branch and calculate the angle between two bifurcating branches. By applying this method to an airway imaging phantom, we obtained accurate results in a short computational time. This new approach provides a rapid, automated, and accurate lung airway angle measurement and may prove useful to radiologists who use CT images for pulmonary disease assessment.

Shadow-based Building Detection and Segmentation in High-resolution Remote Sensing Image

This paper proposes an effective method to extract buildings in high-resolution remote sensing images based on shadow detection. Firstly, a superpixel segmentation algorithm called SLIC is introduced to split the input image into homogeneous patches. LDA-based color features of the patches are extracted for detecting shadow regions. According to the positions of the shadows, an adaptive strategy for seed location and regional growth is developed to accomplish the coarse detection of buildings. Finally, buildings are extracted accurately using Level Set segmentation algorithm. The experimental results prove that the proposed method is applicable in various complicated situations and is more robust and precise as compared with other competing algorithms.

An automatic integrated image segmentation, registration and change detection method for water-body extraction using HSR images and GIS data

Abstract. Automatic water-body extraction from remote sense images is a challenging problem. Using GIS data to update and extract waterbody is an old but active topic. However, automatic registration and change detection of the two data sets often presents difficulties. In this paper, a novel automatic water-body extraction method is proposed. The core idea is to integrate image segmentation, image registration and change detection with GIS data as a whole processing. A new iterative segmentation and registration strategy is proposed. Here, the so-called visual attention model and level set segmentation algorithm is introduced to find interesting areas with suspected water-bodies and to obtain initial objects contours. And an improved shape curve similarity (ISCS) is presented to match image segmentation objects and GIS water-bodies with overall spatial constraints. Furthermore buffer based change detection algorithm is designed to obtain unchanged water-bodies and SVM classifier is used to verify changed ones. Experiment results show that proposed method was effective in rapid extraction and change detection of water-bodies.

Identity Concealment of Brain Images by Masking

In this paper, a novel method of concealment of facial information was proposed to protect the patient’s privacy. It consists with two steps: the construction of a standard brain template and substitution of the template to a new brain image data. Fifteen medical brain images are used to build the standard brain template. Registration and normalization procedures are applied to construct the standard brain templates by using B-Spline registration. Segmentation is used to substitute the original image for the template image and brain extraction. Canny-edge Level set segmentation algorithm is applied to segment a scalp-skull part from a whole brain volume data. New medical brain images are substituted for the standard template to construct the anonymous face models. In order to verify the validity of the anonymous face model, volumes of extracted brains from an original test brain and its anonymous brain were compared. The result shows that the actual brain part from the anonymous brain is exactly same as one which comes from an original medical brain image.

Validation study of a fast, accurate, and precise brain tumor volume measurement

Precision and accuracy are sometimes sacrificed to ensure that medical image processing is rapid. To address this, our lab had developed a novel level set segmentation algorithm that is 16× faster and >96% accurate on realistic brain phantoms. MethodsThis study reports speed, precision and estimated accuracy of our algorithm when measuring MRIs of meningioma brain tumors and compares it to manual tracing and modified MacDonald (MM) ellipsoid criteria. A repeated-measures study allowed us to determine measurement precisions (MPs) – clinically relevant thresholds for statistically significant change. ResultsSpeed: the level set, MM, and trace methods required 1:20, 1:35, and 9:35 (mm:ss) respectively on average to complete a volume measurement (p<0.05). Accuracy: the level set was not statistically different to the estimated true lesion volumes (p>0.05). Precision: the MM's within-operator and between-operator MPs were significantly higher (worse) than the other methods (p<0.05). The observed difference in MP between the level set and trace methods did not reach statistical significance (p>0.05). ConclusionOur level set is faster on average than MM, yet has accuracy and precision comparable to manual tracing.

Quantification and visualization of carotid segmentation accuracy and precision using a 2D standardized carotid map

This paper describes a framework for vascular image segmentation evaluation. Since the size of vessel wall and plaque burden is defined by the lumen and wall boundaries in vascular segmentation, these two boundaries should be considered as a pair in statistical evaluation of a segmentation algorithm. This work proposed statistical metrics to evaluate the difference of local vessel wall thickness (VWT) produced by manual and algorithm-based semi-automatic segmentation methods (ΔT) with the local segmentation standard deviation of the wall and lumen boundaries considered. ΔT was further approximately decomposed into the local wall and lumen boundary differences (ΔW and ΔL respectively) in order to provide information regarding which of the wall and lumen segmentation errors contribute more to the VWT difference. In this study, the lumen and wall boundaries in 3D carotid ultrasound images acquired for 21 subjects were each segmented five times manually and by a level-set segmentation algorithm. The (absolute) difference measures (i.e., ΔT, ΔW, ΔL and their absolute values) and the pooled local standard deviation of manually and algorithmically segmented wall and lumen boundaries were computed for each subject and represented in a 2D standardized map. The local accuracy and variability of the segmentation algorithm at each point can be quantified by the average of these metrics for the whole group of subjects and visualized on the 2D standardized map. Based on the results shown on the 2D standardized map, a variety of strategies, such as adding anchor points and adjusting weights of different forces in the algorithm, can be introduced to improve the accuracy and variability of the algorithm.