Fast Image Segmentation Research Articles

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>

Multilevel Thresholding for Image Segmentation based on Similarity Filtering

Objective: In this paper, an effective and fast multi-threshold image segmentation method is proposed based on similarity filtering. Method: The image histogram peaks and the valley can be used to locate the clusters in the image. The idea of the proposed research is to fit the Gaussian distribution to the histogram of the image. Dominant peaks are selected from the input image histogram near to its Gaussian distribution. Then for each element of the peaks, peak’s valleys are obtained in the left (low) and right (high) side. Findings: Experiments on a variety of images from Berkeley Segmentation Dataset (BSD) show that the new algorithm effectively segments the image in a computationally efficient manner. Comparison/ Performance evaluation: On comparison, proposed approach is found to be better than other existing methods. Peak Signal to Noise Ratio (PSNR) and time are used to evaluate the performance. The proposed algorithm tries to fit Gaussian curves on the dominant peaks and thus find the valleys which are used as thresholds. Novelty: This is always a quicker process as there is a predefined model which only needs to be fit for the given data set.

An Efficient Image Segmentation and Classification of Tumors in Mammography Image using K-Means Clustering with ROI and ANN Classifier

Breast cancer is commonly occurred in women that causes of death. An early detection of breast cancer can be used to long survival of patients. The tumor is detected in breast using mammography X-ray image. This mammography is one of the special cases of CT scan that is utilized to discover the breast cancer. It is screening method to find the malignant tumor cells in breast among women at early stage to avoid the deaths of patient. However, the radiologists not forever provide accurate results and detection of malignant tumor in breast is a one of the challenging issue due to the tumor cells structure. Therefore, this paper presents K-means clustering with Probabilistic neural networks for improvement of mammogram image quality to provide accurate results. K- Means clustering is applied to segment the mammogram image efficiently and presents an automatic support system for image classification using trained probabilistic neural networks and Artificial Neural networks (ANN) classification will be employed to classify the stage of breast cancer tumor that is caring, malignant or standard. Describing the ROI (Region of Interest), utilizing Region growing segmentation technique, evaluating the area of assumed cancer and establish the image classification of the area on the mammogram image using Sample K-Means Clustering and PNN techniques. Gray Level Co-occurrence Matrix (GLCM) is to extract the features from the texture that features may be in the form of structure, location and outside etc. In this process, the preprocessing method is applied to reduce the noise from the mammogram image for the image enhancement. For the preprocessing, the filter methods are used to give image quality. The performance of k-means clustering and PNN techniques provides fast and accurate image segmentation and classification than the other techniques to detect tumor cells in breast at early stage efficiently.

Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.

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.

Fast Threshold Image Segmentation Based on 2D Fuzzy Fisher and Random Local Optimized QPSO.

In the paper, a real-time segmentation method that separates the target signal from the navigation image is proposed. In the approaching docking stage, the navigation image is composed of target and non-target signal, which are separately bright spot and space vehicle itself. Since the non-target signals is the main part of the navigation image, the traditional entropy-related criterions and Ostu-related criterions will bring inadequate segmentation, while the mere 2D Fisher criterion will causes over-segmentation, all the methods show their shortages in dealing with this kind of case. To guarantee a precise image segmentation, a revised 2D fuzzy Fisher is proposed in the paper to make a trade-off between positioning target regions and retaining target fuzzy boundaries. First, to reduce redundant computations in finding the threshold pair, a 2D fuzzy Fisher criterion-based integral image is established by way of simplifying the corresponding fuzzy domains. Then, to quicken the convergence, a random orthogonal component is added in its quasi-optimum particle to enhance its local searching capacity in each iteration. Experimental results show its competence of quick segmentation.

Fast and robust image segmentation with active contours and Student's-t mixture model

In this paper, a novel active contours method, which combines with the Student's-t mixture model via Expectaton-Maximizaton (EM) algorithm, is proposed to segment complicated two-phase images. Firstly, we rewrite the cost function and derive a novel updating of level set function based on probabilistic principles. Secondly, we put forward two novel geometric priors from the level-set-based curve evolution; and both of them have advantages, the suitable one is selected by personalized need to obtain level set function in EM framework with the aim of reducing the computational cost. Therefore, the level set function is derived from latent variables and served as a feedback to the estimation of the latent variables in next iteration. Finally, in order to enhance the robustness to the outliers, Student's-t mixture model with heavy tail has been applied in our algorithm. Experimental results obtained by employing the proposed method on many synthetic, medical and real-world images to demonstrate its robustness, accuracy and effectiveness.

Robust active contours for fast image segmentation

A robust active contour model is proposed for fast image segmentation. By introducing the intensity fitting energy in a local region, the proposed model can segment the images with intensity inhomogeneity efficiently. Since the local fitting functions are computed before curve evolution, the proposed model is insensitive to initialisation and has a high segmentation efficiency. Experiments on several synthetic and real images have proved the effectiveness of the proposed model.

Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram

The K-means is a popular clustering algorithm known for its simplicity and efficiency. However the elapsed computation time is one of its main weaknesses. In this paper, the authors use the K-means algorithm to segment grayscale images. Their aim is to reduce the computation time elapsed in the K-means algorithm by using a grayscale histogram without loss of accuracy in calculating the clusters centers. The main idea consists of calculating the histogram of the original image, applying the K-means on the histogram until the equilibrium state is reached, and computing the clusters centers then the authors use the clusters centers to run the K-means for a single iteration. Tests of accuracy and computational time are presented to show the advantages and inconveniences of the proposed method.

A fast medical image segmentation method of based on initial contour forecast segmentation model

A fast segmentation algorithm of single medical image and sequence images based on active contour model are proposed in this paper. We give out the initial contour forecast segmentation model of 3D medical image first, and then, numerical solution of the image segmentation model algorithm is presented, send data blocks to different processors to do numerical calculations, finally combine the segmentation results. At the same time, parallel designing methods of node allocation, task allocation and load balancing methods are researched. The experiment results show that the parallel method can significantly improve the segmentation speed of 3D medical image.

A learning-based colour image segmentation with extended and compact structural tensor feature representation

In this paper a novel Tensor-Based Image Segmentation Algorithm (TBISA) is presented, which is dedicated for segmentation of colour images. A purpose of TBISA is to distinguish specific objects based on their characteristics, i.e. shape, colour, texture, or a mixture of these features. All of those information are available in colour channel data. Nonetheless, performing image analysis on the pixel level using RGB values, does not allow to access information on texture which is hidden in relation between neighbouring pixels. Therefore, to take full advantage of all available information, we propose to incorporate the Structural Tensors as a feature extraction method. It forms enriched feature set which, apart from colour and intensity, conveys also information of texture. This set is next processed by different classification algorithms for image segmentation. Quality of TBISA is evaluated in a series of experiments carried on benchmark images. Obtained results prove that the proposed method allows accurate and fast image 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.

TU-AB-303-08: GPU-Based Software Platform for Efficient Image-Guided Adaptive Radiation Therapy

Purpose: In this study, we develop an integrated software platform for adaptive radiation therapy (ART) that combines fast and accurate image registration, segmentation, and dose computation/accumulation methods. Methods: The proposed system consists of three key components; 1) deformable image registration (DIR), 2) automatic segmentation, and 3) dose computation/accumulation. The computationally intensive modules including DIR and dose computation have been implemented on a graphics processing unit (GPU). All required patient-specific data including the planning CT (pCT) with contours, daily cone-beam CTs, and treatment plan are automatically queried and retrieved from their own databases. To improve the accuracy of DIR between pCT and CBCTs, we use the double force demons DIR algorithm in combination with iterative CBCT intensity correction by local intensity histogram matching. Segmentation of daily CBCT is then obtained by propagating contours from the pCT. Daily dose delivered to the patient is computed on the registered pCT by a GPU-accelerated superposition/convolution algorithm. Finally, computed daily doses are accumulated to show the total delivered dose to date. Results: Since the accuracy of DIR critically affects the quality of the other processes, we first evaluated our DIR method on eight head-and-neck cancer cases and compared its performance. Normalized mutual-information (NMI) and normalized cross-correlation (NCC) computed as similarity measures, and our method produced overall NMI of 0.663 and NCC of 0.987, outperforming conventional methods by 3.8% and 1.9%, respectively. Experimental results show that our registration method is more consistent and roust than existing algorithms, and also computationally efficient. Computation time at each fraction took around one minute (30–50 seconds for registration and 15–25 seconds for dose computation). Conclusion: We developed an integrated GPU-accelerated software platform that enables accurate and efficient DIR, auto-segmentation, and dose computation, thus supporting an efficient ART workflow. This work was supported by NIH/NCI under grant R42CA137886.

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.

An approach toward fast gradient-based image segmentation.

In this paper, we present and investigate an approach to fast multilabel color image segmentation using convex optimization techniques. The presented model is in some ways related to the well-known Mumford-Shah model, but deviates in certain important aspects. The optimization problem has been designed with two goals in mind. The objective function should represent fundamental concepts of image segmentation, such as incorporation of weighted curve length and variation of intensity in the segmented regions, while allowing transformation into a convex concave saddle point problem that is computationally inexpensive to solve. This paper introduces such a model, the nontrivial transformation of this model into a convex-concave saddle point problem, and the numerical treatment of the problem. We evaluate our approach by applying our algorithm to various images and show that our results are competitive in terms of quality at unprecedentedly low computation times. Our algorithm allows high-quality segmentation of megapixel images in a few seconds and achieves interactive performance for low resolution images.

Image Segmentation Based on Fast Normalized Cut

In this paper, we propose a fast image segmentation method based on normalized cut. This method apply sim- ple linear iterative clustering super-pixel algorithm to obtain super-pixel regions, and then use affinity propagation cluster- ing to extract the representative pixels in each super-pixel regions, Finally, we apply normalized cut to obtain segmenta- tion results. At the end of the paper, Numerical experiments are presented to demonstrate the effectiveness of our pro- posed method.

Fast SAR Image Segmentation via Merging Cost With Relative Common Boundary Length Penalty

In this paper, a region-merging-based method is proposed for fast segmentation of amplitude-format synthetic aperture radar (SAR) images. It combines the existing fast initial partition by applying the watershed transform to the thresholded ratio edge strength map with fast region merging by using a new merging cost with relative common boundary length penalty (RCBLP) and the nearest neighbor graph (NNG) for fast search minimal edge on a region adjacency graph (RAG). A new statistical similarity measure, which is a scale-invariant and approximately constant false alarm rate with respect to region sizes, is proposed and combined with an RCBLP term to form a new merging cost. The region-merging process starting from the initial partition is fast implemented by means of the RAG and NNG. Several quantitative indexes in optical image segmentation assessment are borrowed for quantitative assessment of segmentation quality. Experiments to synthetic and real SAR images are reported. The results show that the proposed method is fast and attains higher quality segmentation results than the two recent state-of-the-art methods.

Vein Image Segmentation Based on One-Dimentional Gray and Filter Erosion Method

For the low contrast and serious noises, a fast image segmentation method based on one-dimensional gray segmentation, binary morphology erosion and area elimination is proposed. Since veins are thin and long, the vein image can be easily distinguished from background by judging the gray difference from nearby pixels when they are vertically or horizontally scanned. Then the processed image is diposed with erosion and area elimination to filter the noise. According to test results on the hand vein images which got from the equipment constructed by ourselves, it is proved that the method is more suitable for hand vein image segmentation than others and clear vein images can be botained quickly.

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.