改进的遗传模糊聚类算法及其在MR脑组织分割中的应用

为提高MR图像中脑组织分割的精度,针对目前遗传模糊聚类算法存在的问题,提出了改进的遗传模糊聚类算法。首先,通过完全改变遗传算法的编码方式、变异方式和交叉方式,对现有遗传算法进行改进,从而使遗传算法能获得最小的适应度函数值;然后,结合模糊聚类算法,最终得到改进的遗传模糊聚类算法。将改进的遗传模糊聚类算法应用于MR脑图像的分割,结果表明,改进的遗传模糊聚类算法的分割质量高于现有的遗传模糊聚类算法和快速模糊聚类算法。改进的遗传模糊聚类算法可以做为一种快速、全自动的MR脑图像分割工具。     

基于模糊Gibbs场和模糊C均值聚类的脑部磁共振图像的分割

提出了一种利用模糊Gibbs场和模糊C均值聚类的新算法,用来分割脑部磁共振(Magnetic resonance, MR)图像.本算法引入了像素的空间约束,提出了势团均匀分布的概念,并使用模糊信息定义了势团的Gibbs能量,并在传统的基于灰度的模糊C均值聚类(Fuzzy C-means, FCM)算法中引入Gibbs能量的补偿项,建立包含像素灰度信息和空间约束的新的目标函数,并得到模糊矩阵和聚类中心的迭代公式,克服了基于灰度信息的模糊C均值聚类算法的缺陷,从而改善了原有的分割模型.对合成图像和脑部MR图像的实验表明了本算法的有效性,可以有效地分割被噪声污染的低信噪比的MR图像.     

A fuzzy clustering based segmentation system as support to diagnosis in medical imaging.

In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularly suitable for handling a decision making process concerning segmentation of multimodal medical images. By using the possibilistic c-means algorithm as a refinement of a neural network based clustering algorithm named capture effect neural network, we developed the possibilistic neuro fuzzy c-means algorithm (PNFCM). In this paper the PNFCM has been applied to two different multimodal data sets and the results have been compared to those obtained by using the classical fuzzy c-means algorithm. Furthermore, a discussion is presented about the role of fuzzy clustering as a support to diagnosis in medical imaging.     

Semi-automatic Segmentation of Brain Tumors Using Population and Individual Information

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.     

Hybrid multiresolution Slantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation

The paper presents a new approach for automated segregation of brain MR images, using an improved orthogonal discrete wavelet transform (DWT), known as the Slantlet transform (ST), and a fuzzy c-means (FCM) clustering approach. ST has excellent time-frequency resolution characteristics and these can be achieved with shorter supports for the filter, compared to DWT employed for identical situations. FCM clustering, on the other hand, can provide efficient classification results, if it is implemented for well-processed input feature vectors. Thus, by combining both the ST and the FCM clustering approaches, a hybrid scheme has been developed that can segregate brain MR images. This automated tool when developed can infer whether the input image is that of a normal brain or a pathological brain. The proposed technique has been applied to several benchmark brain MR images and the results reveal excellent accuracy in characterizing human brain MR imaging.     

基于改进空间模糊聚类的DTI图像分割算法

针对模糊C均值(FCM)聚类算法初始聚类中心选择的随机性和噪声的敏感性等问题,提出一种基于改进空间模糊聚类的图像分割算法来分割人脑DTI图像。使用局部密度核函数和中心距离函数精确选取初始聚类中心,不仅可以解决因聚类中心随机选取造成的聚类效果不稳定的问题,而且还可以使目标函数迅速收敛,提高分割效率;通过将正态分布空间信息融入模糊隶属度函数,能减小图像噪声以及人为因素对分割结果的影响。用该方法与FCM、SFCM方法对人脑DTI数据进行分割,以评价算法的聚类效果。实验对美国明尼苏达大学生物医学功能成像与神经工程实验室提供的58例DTI数据、3例FA参数图像以及6例迭加过噪声的人脑DTI图像进行分割,结果表明:该算法分割系数最高,可达到0.984 1;在同一图像中,该算法在划分系数上比FCM最高提升20.2%,并且在划分熵上比SFCM最高下降19.8%;该算法目标函数平均迭代次数为32,较FCM的52次与空间FCM的76次有明显降低。实验证明,该算法能够准确、快速地分割出重要目标,且对图像噪声不敏感。     

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.     

基于模糊连接度的多发性硬化症MR图像自动分割算法

多发性硬化症(MS)是一种严重威胁中枢神经功能的疾病,利用磁共振成像技术能够无损伤地检出其病灶。为了自动地对多发性硬化症病灶进行分割,提出了基于模糊连接度的分割算法,实现了种子点的自动选取。作为多发性硬化症分割的预处理,针对脑部MR FLAIR图像的特征,基于区域增长方法,还提出了脑部组织提取算法。通过对临床患者MR图像的分割实验,表明该分割算法能够比较准确地分割多发性硬化症病灶,其分割效果明显好于模糊C-均值聚类算法和基于马尔可夫场模型的分割算法。该算法还具有无监督、运算速度快、稳健性好等优点,能够应用于多发性硬化症的临床辅助诊断。     

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.     

A novel kernelized fuzzy C-means algorithm with application in medical image segmentation

Image segmentation plays a crucial role in many medical imaging applications. In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data. The algorithm is realized by modifying the objective function in the conventional fuzzy C-means (FCM) algorithm using a kernel-induced distance metric and a spatial penalty on the membership functions. Firstly, the original Euclidean distance in the FCM is replaced by a kernel-induced distance, and thus the corresponding algorithm is derived and called as the kernelized fuzzy C-means (KFCM) algorithm, which is shown to be more robust than FCM. Then a spatial penalty is added to the objective function in KFCM to compensate for the intensity inhomogeneities of MR image and to allow the labeling of a pixel to be influenced by its neighbors in the image. The penalty term acts as a regularizer and has a coefficient ranging from zero to one. Experimental results on both synthetic and real MR images show that the proposed algorithms have better performance when noise and other artifacts are present than the standard algorithms.     

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.     

基于塔分割和多中心模糊聚类的医学图像分割

本文提出一种基于塔分割和多中心模糊C均值算法结合的无监督MR图像分割方法。文中采用根标记方法对塔图像进行过分割；在塔的最底层模糊刖像上应用HSC（hierarchical subtractive clustering）计算初始的聚类中心及聚类数。进而应用FCM算法合并过分割的结果。由于塔分割有效地降低了聚类样本数和HSC自动获得有效的初始聚类中心和聚类数，实验结果表明，在聚类性能不变情况下显著地减少FCM算法的运算时间，从而实现医学图像的快速分割。     

Information bottleneck based incremental fuzzy clustering for large biomedical data

Incremental fuzzy clustering combines advantages of fuzzy clustering and incremental clustering, and therefore is important in classifying large biomedical literature. Conventional algorithms, suffering from data sparsity and high-dimensionality, often fail to produce reasonable results and may even assign all the objects to a single cluster. In this paper, we propose two incremental algorithms based on information bottleneck, Single-Pass fuzzy c-means (spFCM-IB) and Online fuzzy c-means (oFCM-IB). These two algorithms modify conventional algorithms by considering different weights for each centroid and object and scoring mutual information loss to measure the distance between centroids and objects. spFCM-IB and oFCM-IB are used to group a collection of biomedical text abstracts from Medline database. Experimental results show that clustering performances of our approaches are better than such prominent counterparts as spFCM, spHFCM, oFCM and oHFCM, in terms of accuracy.     

Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification

Skull-stripping in magnetic resonance (MR) images is one of the most important preprocessing steps in medical image analysis. We propose a hybrid skull-stripping algorithm based on an adaptive balloon snake (ABS) model. The proposed framework consists of two phases: first, the fuzzy possibilistic c-means (FPCM) is used for pixel clustering, which provides a labeled image associated with a clean and clear brain boundary. At the second stage, a contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of an adaptive balloon snake model. The model is designed to drive the contour in the inward normal direction to capture the brain boundary. The entire volume is segmented from the center slice toward both ends slice by slice. Our ABS algorithm was applied to numerous brain MR image data sets and compared with several state-of-the-art methods. Four similarity metrics were used to evaluate the performance of the proposed technique. Experimental results indicated that our method produced accurate segmentation results with higher conformity scores. The effectiveness of the ABS algorithm makes it a promising and potential tool in a wide variety of skull-stripping applications and studies.     

A new automatic image analysis method for assessing estrogen receptors’ status in breast tissue specimens

Manual assessment of estrogen receptors′ (ER) status from breast tissue microscopy images is a subjective, time consuming and error prone process. Automatic image analysis methods offer the possibility to obtain consistent, objective and rapid diagnoses of histopathology specimens. In breast cancer biopsies immunohistochemically (IHC) stained for ER, cancer cell nuclei present a large variety in their characteristics that bring various difficulties for traditional image analysis methods. In this paper, we propose a new automatic method to perform both segmentation and classification of breast cell nuclei in order to give quantitative assessment and uniform indicators of IHC staining that will help pathologists in their diagnostic. Firstly, a color geometric active contour model incorporating a spatial fuzzy clustering algorithm is proposed to detect the contours of all cell nuclei in the image. Secondly, overlapping and touching nuclei are separated using an improved watershed algorithm based on a concave vertex graph. Finally, to identify positive and negative stained nuclei, all the segmented nuclei are classified into five categories according to their staining intensity and morphological features using a trained multilayer neural network combined with Fisher's linear discriminant preprocessing. The proposed method is tested on a large dataset containing several breast tissue images with different levels of malignancy. The experimental results show high agreement between the results of the method and ground-truth from the pathologist panel. Furthermore, a comparative study versus existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed method.     

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster’s shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.     

一种分割脑磁共振图像的改进FCM聚类算法

噪声和偏移场是影响磁共振(MRI)图像质量的主要因素。以含加性噪声和乘性偏移场的脑MRI图像组织分割为目标,提出一种抗噪局部相干模糊聚类算法,通过在目标函数中加入模糊算子和一致局部信息约束,达到同时抑制噪声和偏移场不利影响的目的,提高分割准确性和稳定性。采用20例合成图像、60例来自BrainWeb的模拟脑MRI图像、100例来自IBSR真实脑MRI图像,对算法的聚类性能进行评价。实验结果表明,在噪声和偏移场干扰并存的情况下,所提出算法与其他几种经典FCM改进算法相比,对合成图像集的平均分类准确度SA达到0.97,高于其他算法,最大可提高0.37;对真实脑MRI图像集的脑脊液分割有明显优势,相似性测度KI平均提高约0.1。分析表明,所提出算法有更好的分类准确性和稳定性。     

新型乳腺磁共振增强图像肿瘤区域的自动分割模型

乳腺磁共振增强图像上,乳腺癌主要有肿块型和非肿块型两种强化方式。由于乳腺肿瘤区域相对较小,肿块型和非肿块型之间形态学差异大,非肿块型自身差异性复杂,因而很难精确分割出乳腺肿瘤区域。针对这些问题,提出一套新颖的粗检测细分割的深度学习模型(YOLOv2+SegNet)。该模型在精准分割之前,首先运用YOLOv2网络在乳腺可能的肿瘤区域进行粗检测,从而得到大致可能的肿瘤区域;接下来在粗检测的基础上,针对检测到可能的肿瘤区域,运用SegNet网络进行精细分割,从而实现算法最优的性能。为了验证YOLOv2+SegNet模型的有效性,从医院采集的数据集中选取560张乳腺MRI增强图像作为训练和测试(其中训练和测试集分别为415张和145张乳腺MRI数据)。在实验的过程中,运用YOLOv2+SegNet模型,分别对乳腺肿块型、非肿块型、肿块和非肿块混合型3类MRI数据进行肿瘤区域自动分割的实验。实验结果表明:YOLOv2+SegNet模型和SegNet网络分割结果的Dice系数相比有约10%的提升,与传统的C-V模型、模糊C均值聚类、光谱映射主动轮廓模型以及深度模型U-net、DeepLab相比有更为明显的提升。     

Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images

Hyperdensity in head CT images has been shown to be a specific feature for diagnosing tuberculous meningitis (TBM) in children. We describe the extraction of hyperdense regions using fuzzy c-means clustering and fuzzy maximum likelihood estimation, thus providing a tool for the enhancement of an often subtle radiological feature. We calculate an asymmetry measure and confirm that normal and TBM images have different patterns of hyperdensity. Our results may be used in computer-assisted diagnosis of TBM.     

Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images

In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.