Automatic Boundary Detection Research Articles

A Novel Approach for Segmentation of Brain Image using a Multiscale Transform and a Region Based Active Contour

Over the Past decade Medical Image segmentation is one of the most challenging and focused topic for intensive research in interdisciplinary areas of Image processing and computer vision. Segmentation is the process of automatic or semi-automatic detection of boundaries [5]. In this paper, we implement a novel unsupervised method for segmenting MRI brain Images based on multiresolution transforms and region based active contour. Application of multiscale, multiresolution methods with active contour is most interesting research topic in image segmentation [6].This new application makes segmentation algorithms more economical for computation.

Reservoir Rock Characterization Using Wavelet Transform and Fractal Dimension

The aim of this study is to characterize and find the location of geological boundaries in different wells across a reservoir. Automatic detection of the geological boundaries can facilitate the matching of the stratigraphic layers in a reservoir and finally can lead to a correct reservoir rock characterization. Nowadays, the well-to-well correlation with the aim of finding the geological layers in different wells is usually done manually. For a rather moderate-size field with a large number of wells (e.g., 150 wells), the construction of such a correlation by hand is a quite complex, labor-intensive, and time-consuming. In this research, the wavelet transform as well as the fractal analysis, with the aid of the pattern recognition techniques, are used to find the geological boundaries automatically. In this study, we manage to use the wavelet transforms approach to calculate the fractal dimension of different geological layers. In this process, two main features, the statistical characteristics as well as the fractal dimensions of a moving window, are calculated to find a specific geological boundary from a witness well through different observation wells. To validate the proposed technique, it is implemented in seven wells of one of the Iranian onshore fields in the south-west of Iran. The results show the capability of the introduced automatic method in detection of the geological boundaries in well-to-well correlations.

DeepDendro – A tree rings detector based on a deep convolutional neural network

Tree-ring analysis is widely used in many different fields of science. Among others, information carried by annual tree rings allows determining the rates of environmental changes and the timing of events. The analysis of the tree rings requires prior detection of tree-ring boundaries which is traditionally performed manually with the use of stereoscope, a moving table, and a data recorder. This is, however, time-consuming and very cumbersome, especially in the case of long tree-ring series. Several approaches to an automatic detection of tree-ring boundaries exist; however, they use basic image processing techniques. As a result, their accuracy is limited, and their application is restricted mainly to conifer wood where the tree-ring boundaries are clearly defined. There also exists some commercial software, however, none of them is perfect as they fail when applied to the ring-porous wood type. Therefore this paper proposes a DeepDendro approach i.e., an automatic tree-ring boundary detector built upon the U-Net convolutional network. To the authors’ best knowledge this is the first study which applies ConvNets for an automatic detection of tree rings. The performance of the existing approach was tested on the dataset of images of wood cores of three species that represent the ring-porous type of the anatomical structure (Quercus sp., Fraxinus excelsior L., and Ulmus sp.). The testing dataset contained over 2500 of tree-ring boundaries, 96% of which were determined correctly by the proposed method. The corresponding precision is at the level of 0.97 which confirms that only a few false boundaries were introduced by the DeepDendro approach. The results were obtained automatically without any user interaction.

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.

Identifying the nature of lithogeochemical boundaries in drill holes

There is a need for rapid and reliable techniques for extracting geological information from geochemical data derived from exploration drill hole samples because geochemical data bases are becoming too large to interpret manually. Automated boundary detection techniques which use the continuous wavelet transform are a popular method for extracting multi-scale geological boundaries from drill hole signals. However, these boundary detection techniques do not distinguish between sharp and gradational boundaries, which may be an important factor when interpreting the geology of the subsurface or calculating resource estimates. This paper demonstrates how a scale-dependant measure of relative sharpness of the boundary can be extracted from the results of the continuous wavelet transform. The results of the automated method are compared against the interpretations of 15 geologists. The comparison demonstrates the need for alternative boundary selection methods when boundaries are not sharp. The results also demonstrate how the multi-scale nature of the sharpness measure allows for reliable gradient analysis in noisy signals.

Fractal parameters and well-logs investigation using automated well-to-well correlation

The aim of well-to-well correlation is to detect similar geological boundaries in two or more wells across a formation, which is usually done manually. The construction of such a correlation by hand for a field with several wells is quite complex and also time-consuming as well. The aim of this study is to speed up the well-to-well correlation process by providing an automated approach. The input data for our algorithm is the depths of all geological boundaries in a reference well. The algorithm automatically searches for similar depths associated with those geological boundaries in other wells (i.e., observation wells). The fractal parameters of well-logs, such as wavelet exponent (Hw), wavelet standard deviation exponent (Hws), and Hausdorff dimension (Ha), which are calculated by wavelet transform, are considered as pattern recognition dimensions during the well-to-well correlation. Finding the proper fractal dimensions in the automatic well-to-well correlation approach that provide the closest geological depth estimation to the results of the manual interpretation is one of the prime aims of this research. To validate the proposed technique, it is implemented on the well-log data from one of the Iranian onshore oil fields. Moreover, the capability of gamma ray, density, and sonic log in automatic detection of geological boundaries by this novel approach is also analyzed in detail. The outcome of this approach shows promising results.

Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm

Radar sounders are unique instruments for subsurface investigation in both terrestrial and space applications. They are widely employed for monitoring changes to the polar ice sheets and for the study of planetary bodies (e.g., Mars). The analysis of the very large amount of data produced by such systems requires the development of automatic techniques for an objective, accurate, and fast extraction of relevant information from radargrams. In this paper, we propose a novel technique for the automatic detection of layer boundaries based on a local scale hidden Markov model (HMM), which models the radar response in the presence of a layer boundary, and the Viterbi algorithm (VA, which performs the inference step). The proposed technique is based on a divide and conquer strategy that executes the VA using the observation data and the HMM to infer the most likely layer boundary location within a small radargram portion. Finally, a detection strategy is defined to chain together the inferred local layer locations. Furthermore, a novel radargram enhancement and denoising technique tailored to support the detection step is presented. The effectiveness of the proposed technique has been confirmed by testing it on different radargrams acquired by shallow radar over the north pole of Mars. The results obtained point out the superiority of the proposed method in retrieving the position of each layer boundary (and thus of the related intensity and geometric properties) with respect to the state-of-the-art techniques.

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch's membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm).

Dynamic Programming Based Segmentation in Biomedical Imaging

Many applications in biomedical imaging have a demand on automatic detection of lines, contours, or boundaries of bones, organs, vessels, and cells. Aim is to support expert decisions in interactive applications or to include it as part of a processing pipeline for automatic image analysis. Biomedical images often suffer from noisy data and fuzzy edges. Therefore, there is a need for robust methods for contour and line detection. Dynamic programming is a popular technique that satisfies these requirements in many ways. This work gives a brief overview over approaches and applications that utilize dynamic programming to solve problems in the challenging field of biomedical imaging.

Automatic boundary detection based on entropy measures for text-independent syllable segmentation

In this paper, we study the boundary detection in syllable segmentation field. We describe an algorithm proposed for text-independent syllable segmentation. This algorithm provides a performance comparison between the entropies of Shannon, Tsallis and Renyi in an effective detection of beginning-ending points of syllable in a speech signal. The Shannon generalizations (Tsallis and Renyi) quantify the degree of signal organization and offer the relevant information such as the voicing degree on the first syllable segment that we obtained from the temporal dynamics of singularity exponents. The method we propose is focused on an aggregation measure based on entropies to enhance the syllable boundaries detection. It has been also demonstrated in this paper that the best suited entropy for efficient boundary detection is Renyi entropy. Once evaluated, our algorithm produced better performance with efficient results on two languages, i.e., the Fongbe (an African tonal language spoken especially in Benin, Togo, and Nigeria) and an American English. The overall accuracy of syllable boundaries was obtained on Fongbe dataset and validated subsequently on TIMIT dataset with a margin of error < 5ms.

Image analyzing method to detect vague boundaries by using reaction–diffusion system

Boundary detection is a very important procedure in the image analysis of structures. Because vague boundaries are often traced by hand in a time-consuming process, an automated boundary detection system is needed. Here, we introduce a method to detect vague boundaries by solving a reaction–diffusion system. This method makes blurry boundaries clear, even if there are irregularities in image brightness. Combining our method with ordinary image analysis methods, we are able to derive useful information from images with vague boundaries.Because the algorithm of this method depends on mathematical theory, we can apply it to different kinds of images. To confirm the effectiveness of our method, two different kinds of images were tested, those of capillaries and prior austenite grain structures. Good numerical results were achieved for both types of images.

Preliminary investigation of multiparametric strain Z-score (MPZS) computation using displacement encoding with simulated echoes (DENSE) and radial point interpretation method (RPIM).

To describe and assess an automated normalization method for identifying sentinel (septal) regions of myocardial dysfunction in nonischemic, nonvalvular dilated cardiomyopathy (DCM), using an unprecedented combination of the navigator-gated 3D spiral displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI), radial point interpolation (RPIM) and multiparametric strain z-score (MPZS). Navigator-gated 3D spiral DENSE, in a 1.5T MRI machine, was used for acquiring the displacement encoded complex images, MR Analytical Software System (MASS) for automated boundary detection and automated meshfree RPIM for left-ventricular (LV) myocardial strain computation to analyze MPZS in 36 subjects (with n = 17 DCM patients). Pearson's r correlation established relations between global/sentinel MPZS and ejection fraction (EF). The time taken for combined RPIM-MPZS computations was recorded. Maximum MPZS differences were seen between anteroseptal and posterolateral regions in the base (2.0 ± 0.3 vs. 0.9 ± 0.5) and the mid-wall (2.1 ± 0.4 vs. 1.0 ± 0.4). These regional differences were found to be consistent with historically documented septal injury in nonischemic DCM. Correlations were 0.6 between global MPZS and EF, and 0.7 between sentinel MPZS and EF. The time taken for combined RPIM-MPZS computations per subject was 18.9 ± 5.9 seconds. Heterogeneous contractility found in the sentinel regions with the current automated MPZS computation scheme and the correlation found between MPZS and EF may lead to the creation of a new clinical metric in LV DCM surveillance. J. MAGN. RESON. IMAGING 2016;44:993-1002.

Automatic parsing of lane and road boundaries in challenging traffic scenes

Automatic detection of road boundaries in traffic surveillance imagery can greatly aid subsequent traffic analysis tasks, such as vehicle flow, erratic driving, and stranded vehicles. This paper develops an online technique for identifying the dominant road boundary in video sequences captured by traffic cameras under challenging environmental and lighting conditions, e.g., unlit highways captured at night. The proposed method works in real time of up to 20 frames/s and generates a ranked list of road regions that identify road and lane boundaries. Our method begins by segmenting each frame into a set of superpixels. An adaptive sampling step approximates superpixel contours to a collection of edge segments. Next, we show how online hierarchical clustering can be efficiently used to organize edges into clusters of colinearly similar sets. Promising clusters are paired with each other to form cluster pairs. Then we present and prove a statistical ranking measure that is used along with road-activity and perspective cues to find the dominant road boundaries. We evaluate the proposed approach on two real-world datasets to test our method under camera viewpoint changes and extreme environmental and lighting conditions. Results show that our method outperforms two state-of-the-art techniques in precision, recall, and runtime.

GLOBAL GEOINFORMATION SYSTEM “SUSPENDED SEDIMENT YIELD IN THE RIVER BASINS OF THE EARTH”

General-theoretical and methodical problems of creation and use of the global geoinformation system, which reflects the basic regularity of formation, spatial and temporal variability of suspended sediment yield of the Earth’s rivers, are considered. The role of river basins as geosystem formation in research of sediment yield is shown. Approaches to the automatic detection of river basin boundaries, methods of geoinformation system integration with thematic databases for purpose of geomorphological analysis of river suspended sediment yield are offered and tested.

Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data

Optical coherence tomography (OCT) is useful for materials defect analysis and inspection with the additional possibility of quantitative dimensional metrology. Here, we present an automated image-processing algorithm for OCT analysis of roll-to-roll multilayers in 3D manufacturing of advanced ceramics. It has the advantage of avoiding filtering and preset modeling, and will, thus, introduce a simplification. The algorithm is validated for its capability of measuring the thickness of ceramic layers, extracting the boundaries of embedded features with irregular shapes, and detecting the geometric deformations. The accuracy of the algorithm is very high, and the reliability is better than 1 μm when evaluating with the OCT images using the same gauge block step height reference. The method may be suitable for industrial applications to the rapid inspection of manufactured samples with high accuracy and robustness.

Texture Analysis and Modified Level Set Method for Automatic Detection of Bone Boundaries in Hand Radiographs

Rheumatoid Arthritis (RA) is a chronic inflammatory joint disease characterized by a distinctive pattern of bone and joint destruction. To give an RA diagnosis, hand bone radiographs are taken and analyzed. A hand bone radiograph analysis starts with the bone boundary detection. It is however an extremely exhausting and time consuming task for radiologists. An automatic bone boundary detection in hand radiographs is thus strongly required. Garcia et al. have proposed a method for automatic bone boundary detection in hand radiographs by using an adaptive snake method, but it doesn’t work for those affected by RA. The level set method has advantages over the snake method. It however often leads to either a complete breakdown or a premature termination of the curve evolution process, resulting in unsatisfactory results. For those reasons, we propose a modified level set method for detecting bone boundaries in hand radiographs affected by RA. Texture analysis is also applied for distinguishing the hand bones and other areas. Evaluating the experiments using a particular set of hand bone radiographs, the effectiveness of the proposed method has been proved.

Full Automatic Boundary Detection of Left Ventricle Cavity in Both Views of Short-Axis and Four-Chamber by Processing Successive Echograms

Full Automatic Boundary Detection of Left Ventricle Cavity in Both Views of Short-Axis and Four-Chamber by Processing Successive Echograms

Automatic Boundary Detection of Wall Motion in Two-dimensional Echocardiography Images

Problem statement: Medical image analysis is a particularly difficult problem because the inherent characteristics of these images, including low contrast, speckle noise, signal dropouts and complex anatomical structures. An accurate analysis of wall motion in Two-dimensional echocardiography images is important clinical diag nosis parameter for many cardiovascular diseases. A challenge most researchers faced is ho w to speed up the clinical decisions and reduce human error of estimating accurately the true wall movements boundaries if can be done automatically will be a useful tool for assessing these diseases qualitatively and quantitatively. Approach: The proposed method involves three stages: First, the p re-processing stage for image contrast enhancement to reduce speckle-noise and to highlight certain fe atures of interest (i.e., myocardial tissue). In th e second stage, we applied the segmentation process u sing thresholding technique by considering a mean value of pixels intensity as a threshold value to d istinct image features (e.g., Background and object ). After thresholding implementation, the two most common mathematical morphology operators 'erosion' and 'dilation' are applied to improve the efficiency of the wall boundary detection process. Finally, Robert's operator is used as edge detector to identify the wall boundaries. Results: For accuracy measurement, the experimental results of t he proposed method are compared to that obtained from medical QLab system qualitatively and quantitatively. Conclusion: The results showed that our proposed method is reliable and its performance acc uracy percentages are 50% more acceptable and 42% better than QLab system results.

A Fast Region-Based Active Contour Model for Boundary Detection of Echocardiographic Images

This paper presents the boundary detection of atrium and ventricle in echocardiographic images. In case of mitral regurgitation, atrium and ventricle may get dilated. To examine this, doctors draw the boundary manually. Here the aim of this paper is to evolve the automatic boundary detection for carrying out segmentation of echocardiography images. Active contour method is selected for this purpose. There is an enhancement of Chan-Vese paper on active contours without edges. Our algorithm is based on Chan-Vese paper active contours without edges, but it is much faster than Chan-Vese model. Here we have developed a method by which it is possible to detect much faster the echocardiographic boundaries. The method is based on the region information of an image. The region-based force provides a global segmentation with variational flow robust to noise. Implementation is based on level set theory so it easy to deal with topological changes. In this paper, Newton-Raphson method is used which makes possible the fast boundary detection.

Noninvasive Measurement of Transient Change in Viscoelasticity Due to Flow-Mediated Dilation Using Automated Detection of Arterial Wall Boundaries

We measured the stress–strain relationship of the radial arterial wall during a heartbeat noninvasively. In our previous study, the viscoelasticity of the intima–media region was estimated from the stress–strain relationship, and the transient change in viscoelasticity due to flow-mediated dilation (FMD) was estimated. In this estimation, it is necessary to detect the lumen–intima boundary (LIB) and the media–adventitia boundary (MAB). To decrease the operator dependence, in the present study, a method is proposed for automatic and objective boundary detection based on template matching between the measured and adaptive model ultrasonic signals. Using this method, arterial wall boundaries were appropriately detected in in vivo experiments. Furthermore, the transient change in viscoelasticity estimated from the stress–strain relationship was similar to that obtained manually. These results show the feasibility of the proposed method for automatic boundary detection enabling an objective and appropriate analysis of the transient change in viscoelasticity due to FMD.