Fast Image Segmentation Algorithm Research Articles

Model-based hybrid variational level set method applied to lung cancer detection

<p>The precise segmentation of lung lesions in computed tomography (CT) scans holds paramount importance for lung cancer research, offering invaluable information for clinical diagnosis and treatment. Nevertheless, achieving efficient detection and segmentation with acceptable accuracy proves to be challenging due to the heterogeneity of lung nodules. This paper presents a novel model-based hybrid variational level set method (VLSM) tailored for lung cancer detection. Initially, the VLSM introduces a scale-adaptive fast level-set image segmentation algorithm to address the inefficiency of low gray scale image segmentation. This algorithm simplifies the (Local Intensity Clustering) LIC model and devises a new energy functional based on the region-based pressure function. The improved multi-scale mean filter approximates the image’s offset field, effectively reducing gray-scale inhomogeneity and eliminating the influence of scale parameter selection on segmentation. Experimental results demonstrate that the proposed VLSM algorithm accurately segments images with both gray-scale inhomogeneity and noise, showcasing robustness against various noise types. This enhanced algorithm proves advantageous for addressing real-world image segmentation problems and nodules detection challenges.</p>

The fast image segmentation algorithms using dynamic programming for modals of image histograms

A machine learning method is now considered capable of accurately segmenting images. However, one significant disadvantage of this strategy is that it requires a lengthy training phase and an extensive training dataset. This article uses an image segmentation by histogram thresholding approach that does not require training to overcome this difficulty. This article proposes straightforward and time-optimal algorithms, which are guaranteed by mathematical proofs. Furthermore, we experiment with the proposed algorithms using 100 images from a standard database. The results show that, while their performances are not significantly different, the two proposed methods are roughly 10 and 20 times faster than the most simple and optimal method, Brute Force. They also show that the proposed algorithms can deal with bimodal images and images with various shapes of the image histogram. Because our proposed algorithms are the most efficient and effective. As a result, they can be used for real-time segmentations and as a pre-processing approach for multiple object segmentation.

Impact crack quantification analyses in 3-D angle-interlock woven composite using image segmentation method

Crack distribution quantifications are key to predict residual strength of composite materials after impact. Here we report impact crack distribution quantifications of 3-D angle-interlock woven composites (3-DAWCs) which were observed with micro-computed tomography (micro-CT), and also characterized with finite element analyses (FEA). A fast and accurate image segmentation algorithm has been employed to identify the inside crack distributions. A damage variable has been adopted to quantify the crack propagation. We found that the weft yarn with straight arrangement is conducive to stress propagation, and is the main load carrier. The large deflection and buckling structure of warp yarns inhibit the stress propagation on the back face. It is shown that the cohesive damages locate at the interlacing points and the interface between resin and surface warp yarns after the first three successive impacts. The FEA model has been verified with the test data for predicting the damage initiation, evolution and failure mode of materials under impact, which is of great significance for the 3-D woven composite material design.

A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

Due to more or less deviations in the imaging system, there will be noise in the image, which makes the image segmentation inaccurate. To divide a natural image into a more accurate binary image, the target and background of the image are effectively separated to achieve a more effective segmentation result. Therefore, this paper proposes an image segmentation algorithm combining a saliency map and neutrosophic set (NS) theory. First, to overcome the problem of weak edges in the image, we highlight the details and use the guided filter to filter the various channels of the natural image. Then, the initial saliency map is generated. After the weighted superposition of the initial saliency map, the local entropy map and the gray scale map, the final saliency map can be generated using the nonlinear function, and it can effectively highlight the foreground information of the image. Second, the saliency map is transformed to the NS domain and interpreted by three subsets: true (T), indeterminate (I), and false (F). According to NS theory, the indeterminacy is reduced, and the segmentation results are finally obtained by using the method of threshold. Various experiments were done and compared with other state-of-the-art approaches. These experiments demonstrate the effect of the proposed work, which is fast and effective for de-noising.

Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms.

Research on Image Segmentation Algorithm Based On Area Array Camera PCB Automatic Optic Inspection

In this paper, a fast and accurate image segmentation algorithm is studied in this paper, which is based on the automatic optic inspection (AOI) equipment device of the experimental prototype surface array camera printed circuit board (PCB), which is built in the laboratory. In the search for “ridge lines” in the “topography” watershed, three thinning algorithms, skeleton algorithm, watershed algorithm and so on are used for image morphological processing. The experimental comparison shows that the improved watershed algorithm, which combines the related morphological processing, has a remarkable effect on image segmentation, and it can lay a good foundation for the detection and detection of the following PCB board defect recognition.

Image Segmentation Research Based on GA and Improved Otsu Algorithm

<p>In the face of the problem of high complexity of two-dimensional Otsu adaptive threshold algorithm, a new fast and effective Otsu image segmentation algorithm is proposed based on genetic algorithm. This algorithm replaces the segmentation threshold of the traditional two - dimensional Otsu method by finding the threshold of two one-dimensional Otsu method, it reduces the computational complexity of the partition from O (L4) to O (L). In order to ensure the integrity of the segmented object, the algorithm introduces the concept of small dispersion in class, and the automatic optimization of parameters are achieved by genetic algorithm. Theoretical analysis and experimental results show that the algorithm is not only better than the original two-dimensional Otsu algorithm, but also it has better segmentation effect.</p>

Improved fuzzy clustering and Legendre level sets for segmentation of multiple tumours in low contrast liver CTA images

Accurate and fast image segmentation algorithms are of great importance in medical image processing. In this paper, a new, rapid and efficient region-based segmentation method for Liver tumour segmentation initialised using spatial FCM clustering technique is proposed. In Legendre level sets, the area of interest illumination is represented in lower dimensional subspace. A set of predefined basis functions such as Legendre basis function is used to represent the lower dimensional subspace. This kind of representation enables the robust segmentation of heterogeneous objects even in the presence of noise. The proposed algorithm has been compared with other existing algorithms and its performance evaluation is carried out in CTA abdomen images of various patients. The obtained results prove its effectiveness in low contrast inhomogeneous tumour segmentation.

A fast image segmentation algorithm for detection of pseudo-foreign fibers in lint cotton

Detection of pseudo-foreign fibers based on Automatic Vision Inspection system is crucial to improve the classification accuracy of foreign fibers in cotton, and justify the quality and grade of lint. Pseudo-foreign fibers are dried cotton leaves, seed-coats, barks, grass and dust which mixed into cotton during production, they usually have relative small sizes and large quantities compared to the foreign fibers, so it is hard to detect and segment them from images. In this paper a novel algorithm is proposed to solve the problem. 110 images with pseudo-foreign fibers are tested, and the result showed that both spots and slender shaped pseudo-foreign fibers can be segmented clearly with average speed of 0.67s, which can meet the requirement of online cotton trash detection.

High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

Abstract. Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

A novel fast image segmentation algorithm for large topographic maps

Image segmentation for topographic maps is challenging due to their low quality, high degrees of mixed and false coloring. Besides, many pixels cannot be explicitly separated from each other because of the fuzziness of their colors. Therefore the algorithms based on fuzzy theory are suitable to process such images utilizing their ability to deal with the blurring effect. However, there are still some problems of large-scale data, high time complexity and inaccurate classification. In order to overcome these problems, we propose a novel algorithm for segmenting large topographic maps based on the ideas of fuzzy theory, randomized sampling and multilevel image fusion. In this algorithm, the large topographic map is randomly sampled first. Then the optimal clustering centers are acquired by fuzzy C-means (FCM) clustering. Further, the map is segmented with the fuzzy classification method. Finally, multilevel image fusion is used to fuse the segmented images into the final segmentation maps. Randomized sampling is used to reduce the amount of data, and improve the efficiency of image segmentation. Multilevel fusion can make the final segmentation maps more accurate. The experiments show that our method has higher efficiency and accuracy than the traditional ones. It provides a reliable image segmentation method for large topographic maps.

On-line conveyor belts inspection based on machine vision

Under the background that mining conveyor belts are prone to failure in operation, the on-line fault detection technique based on machine vision for conveyor belts is investigated. High-brightness linear light sources arranged to a vaulted shape provide light for a line-array CCD camera to capture high-quality belt images. A fast image segmentation algorithm is proposed to deal belt images on-line. The algorithm for detecting longitudinal rip and belt deviation which are serious threat to the mine safety production from binary belt images is presented. Then, an on-line visual belt inspection system is developed. The laboratory testing results testify the validity of the visual inspection system.

Research and Implementation of a Fast Image Segmentation Algorithm Based on SRG

In order to improve the real-time performance and accuracy of the traditional SRG(Seeded Region Growing) algorithm in image processing, this paper proposes a intellective and rapid image segmentation by imitating the process of the virus infection in nature, and then implement it on vc++6 platform. On one hand , the algorithm can detecting automatically detect the seeds in image region and can be adapt for uneven-light image by adjusting the parameters based on the brightness of the background; On the other hand, only by one of the image scanning, it can segment and mark the objects from the background. The experimental results show that compared with the traditional SRG algorithm, this algorithm can improve the segmentation speed in different background with higher accuracy.

Selection and Tuning of a Fast and Simple Phase-Contrast Microscopy Image Segmentation Algorithm for Measuring Myoblast Growth Kinetics in an Automated Manner

Acquiring and processing phase-contrast microscopy images in wide-field long-term live-cell imaging and high-throughput screening applications is still a challenge as the methodology and algorithms used must be fast, simple to use and tune, and as minimally intrusive as possible. In this paper, we developed a simple and fast algorithm to compute the cell-covered surface (degree of confluence) in phase-contrast microscopy images. This segmentation algorithm is based on a range filter of a specified size, a minimum range threshold, and a minimum object size threshold. These parameters were adjusted in order to maximize the F-measure function on a calibration set of 200 hand-segmented images, and its performance was compared with other algorithms proposed in the literature. A set of one million images from 37 myoblast cell cultures under different conditions were processed to obtain their cell-covered surface against time. The data were used to fit exponential and logistic models, and the analysis showed a linear relationship between the kinetic parameters and passage number and highlighted the effect of culture medium quality on cell growth kinetics. This algorithm could be used for real-time monitoring of cell cultures and for high-throughput screening experiments upon adequate tuning.

Fast three-dimensional Otsu image segmentation algorithm based on decomposition

Concerning the high the computational complexity and huge calculation of the three-dimensional Otsu,a fast three-dimensional Otsu image segmentation algorithm based on decomposition was presented in this paper.Firstly,the original three-dimensional Otsu algorithm was decomposed into three one-dimensional Otsu algorithms.Then,based on the one-dimensional Otsu algorithm,a new algorithm with a new threshold recognition function was proposed,which combined between-class distance with within-class distance,and the fast realization method was also presented.The experimental results show that the proposed algorithm does not only get satisfactory segmentation result,but also improves the calculation speed,about 1 400 times the recursive algorithm for the three-dimensional Otsu method.

Morphological segmentation of 2-D barcode gray scale image

Segmentation is a key process of 2-D barcode identification. In this paper we propose two 2-D barcode image segmentation algorithms under complex background. The first algorithm is an expansion of multi-scale morphology reconstruction and it can acquire a good segmentation result. However, this algorithm is not suitable for fast real-time image processing due to large computation. To fix the shortcoming of the first algorithm we put forward a fast real-time image segmentation algorithm. The proposed approach is applied in experiments on 2D barcodes with complicated background. In experiments, the second proposed method can maintain image segmentation accuracy while significantly reducing the time consumption.

A new fast multiphase image segmentation algorithm based on nonconvex regularizer

We present a new variational model for the soft multiphase image segmentation. In the model, we introduce a nonconvex regularizer on the membership functions which are used as indicators of different homogeneous regions. The nonconvex regularizer performs better than the usual convex ones in that (i) it well preserves geometric shapes of the homogeneous regions, and (ii) it protects edges from oversmoothing which is a common drawback of the convex regularizer. To solve the nonconvex minimization problem, we design a new fast alternative iteration algorithm, which is robust to the setting of the parameters in the model. We conduct comprehensive experiments to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for image segmentation. The algorithm achieves more accurate results compared to other well-known convex variational methods for image segmentation.

A fast underwater optical image segmentation algorithm based on a histogram weighted fuzzy c-means improved by PSO

The S/N of an underwater image is low and has a fuzzy edge. If using traditional methods to process it directly, the result is not satisfying. Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background, its time-consuming computation is often an obstacle. The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task. So, by using the statistical characteristics of the gray image histogram, a fast and effective fuzzy C-means underwater image segmentation algorithm was presented. With the weighted histogram modifying the fuzzy membership, the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm, so as to speed up the efficiency of the segmentation, but also improve the quality of underwater image segmentation. Finally, particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above. It made up for the shortcomings that the FCM algorithm can not get the global optimal solution. Thus, on the one hand, it considers the global impact and achieves the local optimal solution, and on the other hand, further greatly increases the computing speed. Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced. They enhance efficiency and satisfy the requirements of a highly effective, real-time AUV.

Fast Multi-Object Image Segmentation Algorithm Based on C-V Model

Multi-objective image segmentation is a frequently encountered problem. The classical C-V algorithm has the shortage about multi-iterative operations and the computational time is too long to segment the large size image. On the base of analysis upon the relationship between the image size and the number of iterations and time to get the right result, the article proposes a fast image segmentation algorithm based on local C-V active contour model which are based on threshold segmentation and the connected component labeling to segment the large size image with multiple objectives. In the first step, a coarse segmentation is obtained by using the OTSU method, then label and cut the image with the fast non-recursion pixel marking algorithm of connected domains. The segmentation is used as an initial solution in the C-V model. The analysis and experimental results indicate that the improved C-V algorithm can get the right result quickly compared with classical C-V algorithm. It is fast and effective to segment the large size image with multiple objectives.

Fast image segmentation algorithm based on region competition with level set

An adaptive variational image segmentation model based on Bayesian and region competition was presented. Level set method was used to describe the plane curves and partitioned regions, and the energy function was obtained based on Bayesian region statistical information. Then, a new fast partial different equation for curve evolution was deduced to implement unambiguous image segmentation by region competition. The model can extract multi-class objects simultaneously with fast evolving speed and high segmentation precision. Also, it is easy to integrate other image information such as texture and shape into this model. Besides, the energy function and curve evolution equations are independent so that we can choose different probability functions to describe various types of images. The experimental results show that it is a fast, effective and novel image segmentation algorithm.