SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm

The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms.     

Color image segmentation using adaptive unsupervised clustering approach

This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches.     

Automatic grayscale image segmentation based on Affinity Propagation clustering

Pattern Analysis and Applications - Image segmentation is an important research subject in the field of image analysis and pattern recognition. Based on the affinity propagation (AP) clustering...     

A multiobjective spatial fuzzy clustering algorithm for image segmentation

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.     

Automatic MR image segmentation using maximization of mutual information

Microsystem Technologies - Magnetic resonance (MR) brain image segmentation is an important task for the early detection of any deformation followed by the quantitative analysis for the prediction...     

Rough Possibilistic Type-2 Fuzzy C-Means clustering for MR brain image segmentation

Pixel clustering in spectral domain is an important approach for the soft-tissue categorization of magnetic resonance (MR) brain images. In this regard, clustering algorithms based on type-1 fuzzy set theory are suitable for the overlapping partitions while the rough set based clustering algorithms deal with uncertainty and vagueness. However, additional degree of fuzziness makes the clustering more challenging for various subtle uncertainties and noisy data in the overlapping areas. Hence, this fact motivates us to propose a hybrid technique, called Rough Possibilistic Type-2 Fuzzy C-Means clustering with the integration of Random Forest. In the proposed method, possibilistic approach handles the noisy data better, whereas the other various uncertainties and inherent vagueness are taken care by type-2 fuzzy set and rough set theories. After clustering, it produces rough and crisp points. Thereafter, such crisp points are used to train the Random Forest classifier in order to classify the rough points for yielding better clustering solution. The performance of the proposed method has been demonstrated in comparison with several other recently proposed methods for MR brain image segmentation. Finally, superiority of the results produced by the proposed hybrid method has also been validated through statistical significance test.     

Color image segmentation using tensor voting based color clustering

A novel color image segmentation method using tensor voting based color clustering is proposed. By using tensor voting, the number of dominant colors in a color image can be estimated efficiently. Furthermore, the centroids and structures of the color clusters in the color feature space can be extracted. In this method, the color feature vectors are first encoded by second order, symmetric, non-negative definite tensors. These tensors then communicate with each other by a voting process. The resulting tensors are used to determine the number of clusters, locations of the centroids, and structures of the clusters used for performing color clustering. Our method is based on tensor voting, a non-iterative method, and requires only the voting range as its input parameter. The experimental results show that the proposed method can estimate the dominant colors and generate good segmented images in which those regions having the same color are not split up into small parts and the objects are separated well. Therefore, the proposed method is suitable for many applications, such as dominant colors estimation and multi-color text image segmentation.     

Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation

A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. However, determination of degree of attraction is a challenging task which can considerably affect the segmentation results.This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction. The GAs are best at reaching a near optimal solution but have trouble finding an exact solution, while PSO’s-group interactions enhances the search for an optimal solution. Therefore, significant improvements are expected using a hybrid method combining the strengths of PSO with GAs, simultaneously. In this context, a hybrid GAs/PSO (breeding swarms) method is employed for determination of optimum degree of attraction. The quantitative and qualitative comparisons performed on simulated and real brain MR images with different noise levels demonstrate unprecedented improvements in segmentation results compared to other FCM-based methods.     

Automated brain tumor segmentation on multi-modal MR image using SegNet

Computational Visual Media - The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of...     

Brain image segmentation using semi-supervised clustering

The objective of brain image segmentation is to partition the brain images into different non-overlapping homogeneous regions representing the different anatomical structures. Magnetic resonance brain image segmentation has large number of applications in diagnosis of neurological disorders like Alzheimer diseases, Parkinson related syndrome etc. But automatically segmenting the MR brain image is not an easy task. To solve this problem, several unsupervised and supervised based classification techniques have been developed in the literature. But supervised classification techniques are more time consuming and cost-sensitive due to the requirement of sufficient labeled data. In contrast, unsupervised classification techniques work without using any prior information but it suffers from the local trap problems. So, to overcome the problems associated with unsupervised and supervised classification techniques, we have proposed a new semi-supervised clustering technique using the concepts of multiobjective optimization and applied this technique for automatic segmentation of MR brain images in the intensity space. Multiple centers are used to encode a cluster in the form of a string. The proposed clustering technique utilizes intensity values of the brain pixels as the features. Additionally it also assumes that the actual class label information of 10% points of a particular image data set is also known. Three cluster validity indices are utilized as the objective functions, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. First two cluster validity indices are symmetry distance based Sym-index and Euclidean distance based I-index, which are based on unsupervised properties. Last one is a supervised information based cluster validity index, Minkowski Index. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on several simulated MR normal brain images and MR brain images having some multiple sclerosis lesions. The performance of the proposed semi-supervised clustering technique is compared with some other popular image segmentation techniques like Fuzzy C-means, Expectation Maximization and some recent image clustering techniques like multi-objective based MCMOClust technique, and Fuzzy-VGAPS clustering techniques.     

用于图像分割的粗糙集改进模糊聚类方法

采用一种新的基于粗糙集理论的图像分割算法。通过提取直方图的外层,以及计算像素点周围的局部模糊程度来更新粗糙度。使用局部模糊粗糙度和待定算子来更新FCM算法中的隶属度函数。从粗糙集理论意义上来说,直方图的外层与上近似有关,而直方图取值与下近似有关。该方法通过对比传统的聚类分割算法和刘华军的改进算法,大大降低了时间复杂度,聚类效果显著。实验证明,该方法收敛性较强,运行时间较短,且具有良好的分割效果。     

结合马尔可夫随机场与模糊C-均值聚类的脑MRI图像分割

脑磁共振成像(MRI)在临床上得到了大量的应用,准确分割脑组织结构可以提高脑疾病诊断的可靠性和治疗方案的有效性。模糊C-均值聚类(FCM)算法擅长解决图像中存在的模糊性和不确定性问题,是最常用的脑MRI分割方法。但因FCM仅利用图像灰度信息,没有考虑区域信息,导致其抗噪性能很差,常与区域信息结合进行改进。马尔可夫随机场(MRF)算法充分利用了图像区域信息,但容易出现过分割现象,因此FCM常与MRF进行结合改进。针对现有的FCM和MRF结合方式上存在的问题,提出了一种新型的自适应权值的FCM与MRF结合算法,用于脑MR图像分割。该算法利用了图像邻域像素的区域相关性,自适应的更新联合场的权值,改进了现有的权值固定的结合方式,充分发挥了FCM和MRF各自的优势,使二者结合更加合理。实验结果表明,本文算法较FCM和现存的一些FCM改进算法有更强的抗噪声能力和更高的分割精度。     

Automatic multi-thresholds selection for image segmentation based on evolutionary approach

Automatic thresholding has been widely used in machine vision for automatic image segmentation. Otsu’s method selects an optimum threshold by maximizing the between-class variance in a grayscale image. However, the method becomes time-consuming when extended to multi-level threshold problems, because excessive iterations are required in order to compute the cumulative probability and the mean of class. In this paper, we focus on the issue of automatic selection for multi-level thresholding, and we greatly improve the efficiency of Otsu’s method for image segmentation based on evolutionary approaches. We have investigated and evaluated the performance of the Otsu and Valleyemphasis thresholding methods. Based on our evaluation results, we have developed many different algorithms for automatic threshold selection based on the evolutionary method using the Modified Adaptive Genetic Algorithm and the Hill Climbing Algorithm. The experimental results show that the evolutionary approach achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.     

Adaptive k-means clustering algorithm for MR breast image segmentation

Image segmentation is vital for meaningful analysis and interpretation of the medical images. The most popular method for clustering is k-means clustering. This article presents a new approach intended to provide more reliable magnetic resonance (MR) breast image segmentation that is based on adaptation to identify target objects through an optimization methodology that maintains the optimum result during iterations. The proposed approach improves and enhances the effectiveness and efficiency of the traditional k-means clustering algorithm. The performance of the presented approach was evaluated using various tests and different MR breast images. The experimental results demonstrate that the overall accuracy provided by the proposed adaptive k-means approach is superior to the standard k-means clustering technique.     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.     

Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies

Image segmentation consists in partitioning an image into different regions. MRI image segmentation is especially interesting, since an accurate segmentation of the different brain tissues provides a way to identify many brain disorders such as dementia, schizophrenia or even the Alzheimer's disease. A large variety of image segmentation approaches have been implemented before. Nevertheless, most of them use a priori knowledge about the voxel classification, which prevents figuring out other tissue classes different from the classes the system was trained for. This paper presents two unsupervised approaches for brain image segmentation. The first one is based on the use of relevant information extracted from the whole volume histogram which is processed by using self-organizing maps (SOM). This approach is faster and computationally more efficient than previously reported methods. The second method proposed consists of four stages including MRI brain image acquisition, first and second order feature extraction using overlapping windows, evolutionary computing-based feature selection and finally, map units are grouped by means of a novel SOM clustering algorithm. While the first method is a fast procedure for the segmentation of the whole volume and provides a way to model tissue classes, the second approach is a more robust scheme under noisy or bad intensity normalization conditions that provides better results using high resolution images, outperforming the results provided by other algorithms in the state-of-the-art, in terms of the average overlap metric. The proposed algorithms have been successfully evaluated using the IBSR and IBSR 2.0 databases, as well as high-resolution MR images from the Nuclear Medicine Department of the “Virgen de las Nieves” Hospital, Granada, Spain (VNH), providing in any case good segmentation results.     

基于U-net模型的全自动鼻咽肿瘤MR图像分割

鼻咽肿瘤生长方向不确定，解剖结构复杂，当前主要依靠医生手动分割，该方法耗时久同时严重依赖于医生的经验。针对这一问题，基于深度学习理论，提出一种基于U-net模型的全自动鼻咽肿瘤MR图像分割算法，利用卷积操作替换原始U-net模型中的最大池化操作以减少特征信息的损失。首先，从所有患者的肿瘤切片中提取大小为128×128的区域作为数据样本；然后，将患者样本分为训练样本集和测试样本集，并对训练样本集进行数据扩充；最后，选择训练样本集中所有数据用于训练网络模型。为了验证所提模型的有效性，选取测试样本集中患者的所有肿瘤切片进行分割，最终平均分割精度可达到：DSC（Dice Similarity Coefficient）为80.05%，PM系数为85.7%，CR系数为71.26%，ASSD（Average Symmetric Surface Distance）指标为1.1568。与基于图像块的卷积神经网络（CNN）相比，所提算法DSC，PM（Prevent Match）、CR（Correspondence Ratio）系数分别提高了9.86个百分点、19.61个百分点、16.02个百分点，ASSD指标下降了0.4364；与全卷积神经网络（FCN）模型及基于最大池化的U-net网络相比，所提算法的DSC、CR系数均取得了最优结果，PM系数较两种对比模型中的最大值低2.55个百分点，ASSD指标较两种对比模型中的最小值略高出0.0046。实验结果表明，所提算法针对鼻咽肿瘤图像可以实现较好的自动化分割效果以辅助医生进行诊断。     

A new hybrid image segmentation approach using clustering and black hole algorithm

Clustering technique is used in image segmentation because of its simple and easy approach. However, the existing clustering techniques required prior information as input and the performance are entirely dependent on this prior information, which is the main drawback of the clustering approaches. Therefore, many researchers are trying to introduce a novel method with user free parameter. We proposed a clustering method, that is, independent of user parameters and later we used a region merging technique to improve the performance of the clustering output. In this article, we proposed a hybrid image segmentation method which is based on a clustering algorithm and black hole algorithm. In the clustering technique, we have used recursive density estimation technique of surrounding pixels. After clustering technique, presence of small segments may be present and it would give lower a performance of segmentation output. Therefore, a segment is merged with another segment by finding best matched segment. Black hole algorithm concept has been used to define the fitness of each segment and to find the best matching segment. We have compared the proposed method with the other clustering-based segmentation methods and different evaluation indices are used to calculate the performance, and the result proved the effectiveness of the proposed algorithm.