Feature based fuzzy inference system for segmentation of low-contrast infrared ship images

Segmentation of infrared ship target is important for sea surveillance system. However, as a result of the deficiencies of infrared images, the segmentation of infrared ship image becomes a challenge. For the purpose of addressing this problem, a feature based infrared ship image segmentation method utilizing the fuzzy inference system is proposed. Firstly, the intensity feature is extracted by applying unimodal threshold, which could preserve the low-contrast pixels in the infrared images. Secondly, the local spatial feature is extracted by employing saliency detection, region growing and morphology processing, which could express the shape of the target. Thirdly, the global spatial feature is extracted by utilizing partial region growing and weighted distance transformation, which could suppress the background. Then these features are fuzzified using accommodative ways and prior knowledge. And in light of the fuzzy rules based upon expert knowledge, these fuzzified features are integrated in fuzzy inference system. Finally, the complete target could be directly segmented from the output of the fuzzy inference system. Experimental results illustrate that the proposed method could effectively extract more intact targets from the low-contrast infrared ship images. Additionally, the proposed method outperforms some existed segmentation methods.     

A new segmentation system for brain MR images based on fuzzy techniques

This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsupervised clustering methods including the FCM by Bezdek, suffer many problems that can be partially treated with a proper rule to construct the initial membership matrix to clusters. This work develops a specific method to construct the initial membership matrix to clusters in order to improve the strength of the clusters. The new FMCM is tested on a set of benchmarks and then the application to the segmentation of MR images is presented and compared with the results obtained using FCM.     

基于模糊马尔可夫场的脑部MR图像分割算法

在传统马尔可夫场模型的基础上,建立了模糊马尔可夫场模型。通过对模型的分析得出图像像素对不同类的隶属度计算公式,提出了一种高效、无监督的图像分割算法,从而实现了对脑部MR图像的精确分割。通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明新算法比传统的基于马尔可夫场的图像分割算法和模糊C-均值等图像分割算法有更精确的图像分割能力。     

Automated segmentation of brain MR images

A simple, robust and efficient image segmentation algorithm for classifying brain tissues from dual echo Magnetic Resonance (MR) images is presented. The algorithm consists of a sequence of adaptive histogram analysis, morphological operations and knowledge based rules to accurately classify various regions such as the brain matter and the cerebrospinal fluid, and detect if there are any abnormal regions. It can be completely automated and has been tested on over hundred images from several patient studies. Experimental results are provided.     

Superpixel-based brain tumor segmentation in MR images using an extended local fuzzy active contour model

Multimedia Tools and Applications - In this paper, to deal with poor boundaries in the presence of noise and heterogeneity of magnetic resonance (MR) images, a new region-based fuzzy active contour...     

改进退化的半监督模糊聚类应用于MR图像分割

半监督聚类利用少量标记样本的辅助信息来引导对大量无标记数据的分割。Pedrycz提出的半监督FCM(sFCM)算法应用标记样本的类别归属信息来辅助聚类,其在标记点过于稀少时会退化为无监督FCM算法且收敛较慢,难以应用于多数实际问题。在半监督FCM的基础上提出一种改进退化的半监督FCM算法(dsFCM),通过在sFCM迭代过程中设置监督成分的比重,来加大标记样本点对聚类中心的影响力,在聚类精度、速度和鲁棒性上均比半监督FCM有所提高,解决了标记点稀疏时的退化问题,在医学图像分割上取得了良好应用。     

多阈值优化交互式分割算法及其在医学图像中的应用

针对交互式图像分割方法对边界模糊的医学图像进行分割时通常需要用户标记较多的初始种子或进行二次交互的不足,提出了一种简化标记的多阈值优化交互式分割算法。该算法在GrowCut交互式算法基础上通过引入图像灰度直方图的多个阈值自动生成初始种子模板,并利用改进的细胞自动机迭代算法实现图像分割。算法简化了用户操作,提高了分割精度。应用该算法分别对临床100张肝脏图像和牙菌斑图像进行分割,结果显示了该算法的良好性能。     

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.     

A framework with modified fast FCM for brain MR images segmentation

Intensity inhomogeneity, noise and partial volume (PV) effect render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the effects listed above. In this paper, a framework with modified fast fuzzy c-means for brain MR images segmentation is proposed to take all these effects into account simultaneously and improve the accuracy of image segmentations. Firstly, we propose a new automated method to determine the initial values of the centroids. Secondly, an adaptive method to incorporate the local spatial continuity is proposed to overcome the noise effectively and prevent the edge from blurring. The intensity inhomogeneity is estimated by a linear combination of a set of basis functions. Meanwhile, a regularization term is added to reduce the iteration steps and accelerate the algorithm. The weights of the regularization terms are all automatically computed to avoid the manually tuned parameter. Synthetic and real MR images are used to test the proposed framework. Improved performance of the proposed algorithm is observed where the intensity inhomogeneity, noise and PV effect are commonly encountered. The experimental results show that the proposed method has stronger anti-noise property and higher segmentation precision than other reported FCM-based techniques.     

多发性硬化症MR图像分割新算法研究

提出了一种针对多发性硬化症病灶T2加权脑部磁共振（MR）图像的分割算法。根据多发性硬化症病灶和脑脊液在T2加权像上同表现为高亮度信号的特点,把模糊C均值分割算法与形态学方法相结合,提出了基于核模糊C均值的多发性硬化症病灶分割算法。该算法首先用改进的核模糊C均值算法做基础分割,再用形态学方法提取出多发性硬化症病灶得到最终分割结果。通过对多发性硬化症模拟脑部MR图像的分割结果表明,算法能够比较准确地分割多发性硬化症病灶。     

3D unsupervised modified spatial fuzzy c-means method for segmentation of 3D brain MR image

Pattern Analysis and Applications - This paper proposed a novel 3D unsupervised spatial fuzzy-based brain MRI volume segmentation technique in the presence of intensity inhomogeneity and noise....     

基于空间约束的模糊C均值聚类肝脏CT图像分割

传统模糊C均值（FCM）聚类算法应用于肝脏CT图像分割时仅考虑像素本身特征,无法解决灰度不均匀造成的影响以及肝脏边界模糊造成的边界泄露的问题。为解决上述问题,提出一种结合空间约束的模糊C均值（SFCM）聚类分割算法。首先,使用二维高斯分布函数构建卷积核,利用该卷积核对源图像进行空间信息提取得到特征矩阵;然后,引入空间约束惩罚项,更新并优化目标函数得到新的迭代方程;最后,通过多次迭代,完成对肝脏CT图像的分割。实验结果表明,SFCM算法分割具有灰度不均匀和边界粘连的肝脏CT图像时得到的肝脏轮廓形状更加规则,准确率达到92.8%,比FCM和直觉模糊C均值（IFCM）算法的分割准确率分别提升了2.3和4.3个百分点,过分割率分别降低了4.9和5.3个百分点。     

改进的模糊C-均值算法在医学图像分割中的应用

针对随机选取聚类中心易使得迭代过程陷入局部最优解的缺点,提出了一种混合优化蚁群和动态模糊C-均值的图像分割方法,该方法利用蚁群算法较强处理局部极值的能力,并能动态确定聚类中心和数目.针对传统的分阶段结合遗传算法和蚁群算法的策略存在收敛速度慢,聚类精度差的问题,提出在整个优化过程综合遗传算法和蚁群算法,并在蚁群算法中引入拥挤度函数,利用遗传算法的快速性、全局收敛性提高了蚁群算法的收敛速度,同时利用蚁群算法的并行性和正反馈性提高了聚类的精确度.最后将该算法应用到医学图像分割,对比实验表明,混合算法具有很强的模糊边缘和微细边缘分割能力.     

Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, tumor segmentation work is difficult. This paper introduces a discriminative classification algorithm for semi-automated segmentation of brain tumorous tissues. The classifier uses interactive hints to obtain models to classify normal and tumor tissues. A non-parametric Bayesian Gaussian random field in the semi-supervised mode is implemented. Our approach uses both labeled data and a subset of unlabeled data sampling from 2D/3D images for training the model. Fast algorithm is also developed. Experiments show that our approach produces satisfactory segmentation results comparing to the manually labeled results by experts.

Interactive 3D medical image segmentation with energy-minimizing implicit functions

We present an interactive segmentation method for 3D medical images that reconstructs the surface of an object using energy-minimizing, smooth, implicit functions. This reconstruction problem is called variational interpolation. For an intuitive segmentation of medical images, variational interpolation can be based on a set of user-drawn, planar contours that can be arbitrarily oriented in 3D space. This also allows an easy integration of the algorithm into the common manual segmentation workflow, where objects are segmented by drawing contours around them on each slice of a 3D image.Because variational interpolation is computationally expensive, we show how to speed up the algorithm to achieve almost real-time calculation times while preserving the overall segmentation quality. Moreover, we show how to improve the robustness of the algorithm by transforming it from an interpolation to an approximation problem and we discuss a local interpolation scheme.A first evaluation of our algorithm by two experienced radiology technicians on 15 liver metastases and 1 liver has shown that the segmentation times can be reduced by a factor of about 2 compared to a slice-wise manual segmentation and only about one fourth of the contours are necessary compared to the number of contours necessary for a manual segmentation.     

基于t-混合模型的脑MR图像白质分割

核磁共振成像（MRI）作为临床辅助诊断和研究的重要工具,MR图像分割的准确性直接影响着后续处理的正确性和有效性。在目前的图像分割算法中,基于t-混合模型的图像分割方法因其快速和稳健性而受到重视。该方法的一般过程是先估计混合模型的参数,计算图像中每点的后验概率,然后根据贝叶斯最小错误率准则对图像进行分割。根据MR图像的特点,提出了基于t-混合模型的大脑MR图像白质分割的算法,并取得了较好的实验结果。     

Region oriented compression of color images using fuzzy inference and fast merging

A new region based lossy compression scheme for color images is proposed. The segmentation method belongs to the split and merge category. Splitting is carried out using the watershed transform. In the merging stage, a fuzzy color preserving rule-based system and a novel one-dimensional graph structure are introduced to provide accurate results with reduced computational complexity. The compression part is based on the Shape Adaptive DCT with ΔDC correction method. The quantization matrices used have been designed according to the properties of the employed transform. Promising perceptual results for the low bit rate range compared to previously reported compression methods have also been reported.     

Iterative sliced inverse regression for segmentation of ultrasound and MR images

In this study, we propose an integrated approach based on iterative sliced inverse regression (ISIR) for the segmentation of ultrasound and magnetic resonance (MR) images. The approach integrates two stages. The first is the unsupervised clustering which combines multidimensional scaling (MDS) with K-Means. The dimension reduction based on MDS is employed to obtain fewer representative variates as input variables for K-Means. This step intends to generate the initial group labels of the training data for the second stage of supervised segmentation. We then combine the SIR with the nearest mean classifier (NMC) or the support vector machine (SVM) to iteratively update the group labels for supervised segmentation. The method of SIR is introduced by Li [Sliced inverse regression for dimension reduction. J. Am. Stat. Assoc. 86 (1991) 316–342] to explore the effective dimension reduction (e.d.r.) directions from the training data embedded in high-dimensional space. The test data are then projected onto these directions and the classifiers are further applied to classify the test data. The integrated approach based on ISIR is evaluated on simulated and clinical images, which include ultrasound and MR images. The evaluation results indicate that this approach provides an improvement of image segmentation over the methods to be compared without dimension reduction.     

Bone tumor segmentation from MR perfusion images with neural networks using multi-scale pharmacokinetic features

Bone tumor segmentation and the distinction between viable and non-viable tumor tissue is required during the follow-up of chemotherapeutical treatment. Monitoring viable tumor area over time is important in the ongoing assessment of the effect of preoperative chemotherapy. In this paper, features derived from a pharmacokinetic model of tissue perfusion are investigated. A multi-scale analysis of the parametric perfusion images is applied to incorporate contextual information. A feed-forward neural network is proposed to classify pixels into viable, non-viable tumor, and healthy tissue. We elaborate on the design of a cascaded classifier and analyze the contribution of the different features to its performance. Multi-scale blurred versions of the parametric images together with a multi-scale formulation of the local image entropy turned out to be the most relevant features in distinguishing the tissues of interest. We experimented with an architecture consisting of cascaded neural networks to cope with uneven class distributions. The classification of each pixel was obtained by weighting the results of five bagged neural networks with either the mean or median rules. The experiments indicate that both the mean and median rules perform equally well.     

Removal of noise patterns in handwritten images using expectation maximization and fuzzy inference systems

The removal of noise patterns in handwritten images requires careful processing. A noise pattern belongs to a class that we have either seen or not seen before. In the former case, the difficulty lies in the fact that some types of noise patterns look similar to certain characters or parts of characters. In the latter case, we do not know the class of noise in advance which excludes the possibility of using parametric learning methods. In order to address these difficulties, we formulate the noise removal and recognition as a single optimization problem, which can be solved by expectation maximization given that we have a recognition engine that is trained for clean images. We show that the processing time for a noisy input is higher than that of a clean input by a factor of two times the number of connected components of the input image in each iteration of the optimization process. Therefore, in order to speed up the convergence, we propose to use fuzzy inference systems in the initialization step of the optimization process. Fuzzy inference systems are based on linguistic rules that facilitate the definition of some common classes of noise patterns in handwritten images such as impulsive noise and background lines. We analyze the performance of our approach both in terms of recognition rate and speed. Our experimental results on a database of real-world handwritten images corroborate the effectiveness and feasibility of our approach in removing noise patterns and thus improving the recognition performance for noisy images.