Boundary Detection In Images Research Articles

A Mixture Model for Image Boundary Detection Fusion

A Mixture Model for Image Boundary Detection Fusion

Contour detection refined by a sparse reconstruction-based discrimination method

Sparse representations have been widely used for many image processing tasks. In this paper, a sparse reconstruction-based discrimination (SRBD) method, which was previously proposed for the classification of image patches, is utilized to improve boundary detection in colour images. This method is applied to refining the results generated by three different algorithms: a biologically inspired method, and two state-of-the-art algorithms for contour detection. All of the contour detection results are evaluated by the BSDS300 and BSDS500 benchmarks using the quantitative measures: F-score, ODS, OIS and AP. Evaluation results shows that the performance of each algorithm is improved using the proposed method of refinement with at least one of the quantitative measures increased by 0.01. In particularly, even two state-of-the-art algorithms are slightly improved by applying the SRBD method to refine their contour detection results.

Extreme learning machine based microscopic red blood cells classification

The digitalization of blood slides introduced pathology to a new era. Despite being the most powerful prognostic tool; automated analysis of microscopic blood smear images is still not used in routine clinical practices as manual pathological image analysis methods are still in use that is tedious, time consuming and subjective to technician dependent variation, furthermore it also needs training and skills. In this work, we present novel method based on extreme machine learning approach for the classification of red blood cells (RBC) images. Segmentation of RBC is initiated with statistical based thresholding to retrieve those pixels which are most relevant to RBC followed by Fuzzy C-means for the image segmentation and boundary detection. Different texture and geometrical features are extracted for the classification of normal and abnormal cells. The classification technique is rigorously evaluated against the dataset to evaluate the accuracy of classifier. We have compared the results with state of the art techniques. So far the proposed technique has produced more promising results as compared to the existing techniques.

Mathematical Morphology on the Spherical CIELab Quantale with an Application in Color Image Boundary Detection

Mathematical morphology is a theory with applications in image processing and analysis. This paper presents a quantale-based approach to color morphology based on the CIELab color space in spherica...

Directional SUSAN image boundary detection of breast thermogram

Thermography of the breast has been shown to be well suited to detect early signs of breast cancer. This study proposes a novel method for boundary detection of breast thermography images using directional SUSAN method. Among breast thermography image processing steps, breast isolation from background and from each other is an essential stage for proper detection of breast cancer. For this purpose, in this study, breast boundary is grouped into three regions depending on the region property. The algorithm of boundary detection is different for each region. Specially, for bottom breast boundary, directional SUSAN edge detector is presented that uses two rectangle masks to create a directional SUSAN gradient image with emphasis on oblique useful edges and omitting undesirable ones. Then cubic parabolic interpolation is implemented to determine a set of edge points on the boundaries. At last, an effective search algorithm is executed to correct some false points in order to extract breast boundaries accurately. The performance of the proposed approach illustrated by applying on the images of three databases. Experimental results show that this method acts effectively and confirm the accurate boundary detection. Moreover, statistical measures are calculated to indicate the remarkable capabilities of the proposed approach.

Efficient leukocyte segmentation and recognition in peripheral blood image.

Blood cell count, also known as differential count of various types of blood cells, provides valuable information in order to assess variety of diseases like AIDS, leukemia and blood cancer. Manual techniques are still used in diseases diagnosis that is very lingering and tedious process. However, machine based automatic analysis of leukocyte is a powerful tool that could reduce the human errors, improve the accuracy, and minimize the required time for blood cell analysis. However, leukocyte segmentation is a challenging process due to the complexity of the blood cell image; therefore, this task remains unresolved issue in the blood cell segmentation. The aim of this work is to develop an efficient leukocyte cell segmentation and classification system. This paper presents an efficient strategy to segment cell images. This has been achieved by using Wiener filter along with Curvelet transform for image enhancement and noise elimination in order to elude false edges. We have also used combination of entropy filter, thresholding and mathematical morphology for obtaining image segmentation and boundary detection, whereas we have used back-propagation neural network for leukocyte classification into its sub classes. As a result, the generated segmentation results are fruitful in a sense that we have overcome the problem of overlapping cells. We have obtained 100%, 96.15%, 92.30%, 92.30% and 96.15% accuracy for basophil, eosinophil, monocyte, lymphocyte and neutrophil respectively.

PSO-based Optimized Canny Technique for Efficient Boundary Detection in Tamil Sign Language Digital Images

For the hearing impaired, sign language is the most prevailing means of communication for their day-to-day life. It is always a challenge to develop an optimized automated system to recognize and interpret the implication of signs expressed by the hearing impaired. There are a wide range of algorithms developed for SLR, in which only few considerable approaches are carried out in Tamil Sign Language Recognition. This paper has proposed a significant contribution in segmentation process which is the most predominant component of image analysis in constructing the SLR system. Segmentation is handled using edge detection procedure for finding the borders of hand sign within the captured images, by detecting the split in the image illumination. The objective of the edge function, to find the boundary intensity, is done by a particle swarm optimization technique which chooses the optimal threshold values and implemented in the canny hysteresis thresholding method. The analysis primarily uses common edge recognition algorithms which contain Sobel, Robert, Canny and Prewitt from which the scope of the work is extended by introducing an optimization technique in Canny method. The performance of the proposed algorithm is tested with real time Tamil sign language dataset and comparison is inevitably carried out with standard segmentation metrics

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.

Paper Currency CIS Image Fuzzy Enhancement and Boundary Detection

According to the features of paper currency CIS image, the fuzzy set theory is initial used to improve the contrast of banknote target and background, paper money’s edge pixels are obtained by scanning, and then the sub-pixel edges are fitted by least square method; in the fitting process, the error threshold method is applied for eliminating noisy edge points, then slope correction of paper money is carried out according to the angle of sub-pixel edge line and the note area is extracted, finally, fuzzy enhancement is used for the extracted image again. Experiments show that, this method has good versatility, high accuracy and strong anti-interference ability, it can even extract the paper currency area effectively in the edge deterioration and unfilled corner condition, and the enhanced image is in favor of subsequent paper currency recognition.

Accelerating image boundary detection by hardware parallelism

Image boundary can provide useful information for high-level tasks in computer vision applications. However, high-quality image boundary detection algorithms are computationally intensive, which limits their applicability in real-world applications. In this paper, a study on accelerating algorithms of image boundary detection by hardware parallelism is presented. The Pb (Probability boundary) algorithm, as one representative high-quality algorithm of gradient-based boundary detection, is selected. Firstly, different types of parallelisms existing in Pb are analyzed. Then, suitable hardware structures to accelerate Pb based on those parallelisms are discussed. Finally, time performance, accuracy and scalability of the parallel Pb detector accelerated by hardware are presented. After being implemented in a Xilinx Virtex-7 FPGA, XC7VX485T-2FFG1761C, the parallel Pb detector with the working frequency of 200MHz takes 6.3ms to process an 321×481 image. It is more competitive than Pb implemented on CPUs when larger images are processed. This paper demonstrates a promising way to improve the real-time performance of high-quality image boundary detection systems, especially when embedded and real-time systems are taken into account.

Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images.

Segmenting the prostate from CT images is a critical step in the radio-therapy planning for prostate cancer. The segmentation accuracy could largely affect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians. In this paper, we propose a learning-based approach for boundary detection and deformable segmentation of the prostate. Our proposed method aims to learn a boundary distance transform, which maps an intensity image into a boundary distance map. To enforce the spatial consistency on the learned distance transform, we combine our approach with the auto-context model for iteratively refining the estimated distance map. After the refinement, the prostate boundaries can be readily detected by finding the valley in the distance map. In addition, the estimated distance map can also be used as a new external force for guiding the deformable segmentation. Specifically, to automatically segment the prostate, we integrate the estimated boundary distance map into a level set formulation. Experimental results on 73 CT planning images show that the proposed distance transform is more effective than the traditional classification-based method for driving the deformable segmentation. Also, our method can achieve more consistent segmentations than human raters, and more accurate results than the existing methods under comparison.

A Fast Geodesic Active Contour Model for Medical Image Segmentation Using Prior Analysis and Wavelets

The deformable geodesic active contour (GAC) method is one of the most popular techniques used in object boundary detection in images. In this work, we improve the automatic GAC technique by incorporating prior information extracted from the image region of interest. In addition, we propose a new stopping function to speed up convergence and improve accuracy. The proposed technique was applied to both synthetic and real medical images. The results show both an improvement of more than 40 % in convergence speed together with an excellent accuracy when compared with the previous work.

SU‐E‐J‐76: 3D Soft Tissue Boundary Detection for Automatic Verification of Deformable Image Registration

Purpose: The intermediate goal of this work is to develop a novel 3D image boundary detection algorithm in order to reliably detect the soft tissue boundaries in patient CT images. This is the very critical first step towards a potential solution for the currently unsolved problem ‐ automatic verification of deformable image registration (DIR) on patient images. Our hypothetic procedure of DIR verification is 1) tissue boundary detection, 2) boundary matching, and 3) verification of DIR results using the matched tissue boundaries. Methods: A novel 3D image boundary detection algorithm was developed. Multiple steps were used in the tissue boundary procedure, including 3D gradient computation supporting anisotropic voxels, hysteresis thresholding, non‐maxima suppression directly based on the boundary normal directions, boundary thinning, tissue boundary type selection based on the image intensity values on both sides of the boundaries, boundary separation and voxel reconnection based on boundary probability tests. These steps are innovative and critical to enable reliable detection of separated tissue boundaries that would be used in the following boundary matching step. Preprocessing includes automatic couch table removal and non‐local‐mean noise reduction. Post‐processing includes small piece removal and boundary voxels to point coordinate conversion. Results: The algorithm was implemented in MATLAB and tested using patients' head‐neck and pelvis CT images. Results were visually assessed. Compared to the results of the 3D extension of the standard Canny edge detection algorithm, results of the proposed algorithm are significantly more accurate and reliable. Conclusion: The proposed algorithm is a new approach of medical image processing. Combined with image feature matching methods from computer vision fields, it could lead to a potential solution for DIR verification, which is the current roadblock for many clinical applications including treatment adaptation, automatic segmentation and treatment response evaluation.

Image Boundary Detection Using the Modified Level Set Method and a Diffusion Filter

The Level Set Method has been widely used in image processing and computer vision. However, the Level Set Method has problems with stability and convergence. This arrangement often leads to either a complete breakdown or premature termination of the curve evolution process, the consequence of which is unsatisfactory results. Convergence and stability of the Level Set Method are controlled by the speed function and parameters of the Level Set Method. In this paper, we will present a modification of the speed function for image boundary detection. To preserve the boundary of an image and to remove noise, we use a diffusion filter to substitute a Gaussian Filter in the standard Level Set Method. After evaluating the experiments using a specific set of artificial images, we have found that the performance of those methods has been significantly improved.

Position mapping and a uniformity correction method for small-animal SPECT based on connected regional recognition

We describe a novel position mapping and the uniformity correction method to improve the performance of small animal single-photon emission computed tomography (SPECT) imaging. The SPECT system consists of a cerium doped lutetium−yttrium oxyorthosilicate (LYSO) scintillation crystal (22×22 pixel array, 2mm×2mm×3mm pixel size), a position sensitive photomultiplier tube (H8500c, Hamamatsu Photonics Co., Ltd., Shizuoka Prefecture, Japan), and a parallel-hole collimator (Nuclear Fields Pty. Ltd., St. Marys, Australia). The position mapping method was based on a connected regional recognition algorithm. We present the algorithm and step-by-step details of image boundary detection, dynamic binarization, connected regional recognition, center-of-gravity computing, and look-up table establishment. The position mapping and uniformity correction tables were generated and applied to the SPECT projection data. The corrected projection images demonstrated that this correction method improved the uniformity of the raw projection image by ∼16%. The preliminary SPECT reconstruction results (using algebraic reconstruction technology) are also presented. A comparison between the reconstructed images before and after correction further confirms the performance of this correction method.

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.

In vitro three‐dimensional aortic vasculature modeling based on sensor fusion between intravascular ultrasound and magnetic tracker

It is desirable to reduce aortic stent graft installation time and the amount of contrast media used for this process. Guidance with augmented reality can achieve this by facilitating alignment of the stent graft with the renal and mesenteric arteries. For this purpose, a sensor fusion is proposed between intravascular ultrasound (IVUS) and magnetic trackers to construct three-dimensional virtual reality models of the blood vessels, as well as improvements to the gradient vector flow snake for boundary detection in ultrasound images. In vitro vasculature imaging experiments were done with hybrid probe and silicone models of the vasculature. The dispersion of samples for the magnetic tracker in the hybrid probe increased less than 1 mm when the IVUS was activated. Three-dimensional models of the descending thoracic aorta, with cross-section radius average error of 0.94 mm, were built from the data fusion. The development of this technology will enable reduction in the amount of contrast media required for in vivo and real-time three-dimensional blood vessel imaging.

Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images

Intravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for studying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step towards the reliable quantitative diagnosis of atherosclerosis. In this paper, a novel scheme is proposed to automatically detect lumen and media-adventitia borders. This segmentation method is based on the level-set model and the contourlet multiresolution analysis. The contourlet transform decomposes the original image into low-pass components and band-pass directional bands. The circular hough transform (CHT) is adopted in low-pass bands to yield the initial lumen and media-adventitia contours. The anisotropic diffusion filtering is then used in band-pass subbands to suppress noise and preserve arterial edges. Finally, the curve evolution in the level-set functions is used to obtain final contours. The proposed method is experimentally evaluated via 20 simulated images and 30 real images from human coronary arteries. It is demonstrated that the mean distance error and the relative mean distance error have increased by 5.30 pixels and 7.45%, respectively, as compared with those of a recently traditional level-set model. These results reveal that the proposed method can automatically and accurately extract two vascular boundaries.

Knowledge-based image segmentation using swarm intelligence techniques

Intelligent techniques such as swarm intelligence techniques rarely have been used for image segmentation or boundary detection. The limited increasing number of agents in the environment and how to find efficiently the right threshold in an image, develop a flexible design, and fully autonomous system that supports different platforms makes the task challenging. Considering challenges this paper presents a swarm-based intelligent technique for image segmentation that is based on a fully agent-based model system, called swarm intelligence-based image segmentation (SIBIS). SIBIS adopts a cellular automata technique where the swarm of agents navigate through the image and operate on their pixels and local regions. Three features such as swarm intelligence, agent-based modelling and cellular automata are integrated to make SIBIS efficient. SIBIS system can find the image segmentation threshold automatically without changing the background or the texture of the image.

Mold Cavity Surface Subdivision Method Based on Fuzzy Clustering for Edge Detection

This paper presents a mold cavity surface subdivision method by using image boundary detection technology to realize high efficiency NC machining. From the new perspective of image processing, the method constructed an optimal cutting direction angle field image in the u-v system of parameters according to the relationship between maximum cutting bandwidth and cutting direction in the theory of iso-scallop height firstly, and then fuzzy c - means clustering method was applied for subdivision surface boundary detection. Experimental results demonstrate good performance of the proposed method in distinguishing and extracting boundary points of surface subdivision.