K-均值聚类中心分析法实现红外人体目标分割

针对由于不同红外成像设备参数差异以及目标周围环境影响而造成的红外目标分割阈值自动选取算法的鲁棒性差这一问题,本文从红外成像的机理出发,提出了一个新的解决方案并加以实现.首先对图像的直方图采用K-均值聚类,然后通过对聚类中心分布特点的分析,确定图像分割的阈值.该方法不需要事先对图像进行均衡和对背景分布进行假设.实验结果表明,算法具有良好的鲁棒性.     

Tissue Segmentation in Nasopharyngeal CT Images Using TwoStage Learning

Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of computing resources, the structure of the second module is simpler and the number of network layers is less. In addition, our segmentation module is based on U-Net and incorporates a skip structure, which can make full use of the original features of the data and avoid feature loss. We evaluated our proposed method on the nasopharyngeal dataset provided by West China Hospital of Sichuan University. The experimental results show that the proposed method is superior to many standard segmentation structures and the recently proposed nasopharyngeal tissue segmentation method, and can be easily generalized across different tissue types in various organs.     

Automatic Segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature

Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from the segmented liver, extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features. In this work, the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering (FCM) which is one of the algorithms to segment the images. The dataset used in this method has been collected from various repositories and scan centers. The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency. It gives better results when compared with other existing algorithms. Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.     

结合遗传算法与方向图法的指纹图像分割

指纹作为人体的重要特征，由于其唯一性和终生不变性，越来越成为生物测定技术的重要手段，在自动指纹识别中，指纹图像分割是图像预处理中关键的一环，不仅能够对图像信息进行压缩，保留脊谷线的主要信息，同时能够去除大量的粘连，加速后续处理的速度和提高指纹特征提取和匹配的精度，将基于遗传算法的阈值分割方法和方向图法相结合对指纹图像进行分割。     

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T₂-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate. In contrast to other state-of-the-art methods, namely Adaptive Wind Driven Optimization (AWDO), Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO), the proposed algorithm has been found to be better at producing the best objective function, Peak Signal-to-Noise Ratio (PSNR), Standard Deviation (STD) and lower computational time values. Further, it was observed thatthe segmented image gives greater detail when the threshold level increases. Moreover, the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus, these images will lead to better segments of gray, white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

基于随机权重粒子群和K-均值聚类的图像分割

K-均值聚类具有简单、快速的特点,因此被广泛应用于图像分割领域.但K-均值聚类容易陷入局部最优,影响图像分割效果.针对K-均值的缺点,提出一种基于随机权重粒子群优化（RWPSO）和K-均值聚类的图像分割算法RWPSOK.在算法运行初期,利用随机权重粒子群优化的全局搜索能力,避免算法陷入局部最优;在算法运行后期,利用K-均值聚类的局部搜索能力,实现算法快速收敛.实验表明：RWPSOK算法能有效地克服K-均值聚类易陷入局部最优的缺点,图像分割效果得到了明显改善;与传统粒子群与K-均值聚类混合算法（PSOK）相比,RWPSOK算法具有更好的分割效果和更高的分割效率.     

基于流体静力学的区域分割活动轮廓模型

提出一种新的活动轮廓模型,应用于灰度图像的区域分割。对于定义在灰度图像上的闭合连续简单曲线,该模型应用流体静力学理论直接驱动,使其不断地缓慢收拢,直至收敛于区域边界。在这个过程中,闭合连续简单曲线所经历的像素都被该模型根据像素性质判定其区域归属。重新初始化有关变量,激活已收敛于区域边界的闭合连续简单曲线,继续驱动闭合连续简单曲线收拢,直至该曲线收敛于内嵌的新区域边界或者收敛于一个点。在该模型运行过程中,一条闭合连续简单曲线可能会分裂成多条闭合连续曲线,以适应多区域分割。当一条闭合连续的简单曲线经过模型持续驱动之后收敛于一个点时,被其包围的区域分割才告结束。本文提出的模型能够分割多区域和嵌套区域。     

区域生长和C-均值聚类结合的图像分割方法

结合边缘检测的模糊C-均值聚类图像分割方法,本文提出一种基于区域生长和模糊C-均值聚类相结合的图像分割方法.采用与区域生长类似的方法,寻找图像中封闭边缘围成的相互独立的区域,根据物理就近原则对边缘点进行归类,完成图像的分割.经实验验证:目标区的分割相对完整.     

基于多特征的模糊c均值聚类的图像分割

图像分割是图像处理中最为基础和重要的领域之一，是对图像进行视觉分析和模式识别的基本前提。本文提出采用像索点在θ方向上的非覆盖均值差分作为像素点的一个重要特征，联合其他常用特征，对图像进行c均值模糊聚类分析。实验结果表明本文提出的方法可以很好的从图像中分割出有意义的区域。     

医学图像的交互分割及三维表面重建

提出一种基于灰度信息的三维医学图像交互分割方法,并对分割提取的组织或区域进行了三维表面重建,在分割过程中,首先对图像进行滤波,确定阈值范围进行二值化,然后根据待提取组织或区域的特征,选取合适的数学形态学操作,最后对区域进行种子填充,这一过程是一个交互的过程,对提取出的区域用MT算法构造等值面,用灰度梯度法计算表面法微量,并实现高质量三维显示。     

基于自适应前景分割及粒子滤波的人体运动跟踪

提出了在图像序列中用自适应前景分割及粒子滤波对人体的3-D运动轨迹进行跟踪的方法．首先建立了像素点的高斯模型,并结合图像帧间的差分信息以及灰度分布的先验概率等因素完成了图像中人体的自适应分割．根据所得到的分割结果建立了透视投影下的运动平面跟踪模型．根据投影过程的非线性以及图像中噪声分布的未知性,提出了粒子滤波的跟踪方法,并最终得到了人体运动平面的3-D轨迹．实际人体运动图像序列的实验证明,本文方法能有效地跟踪人体运动的3-D轨迹,并反映出在此跟踪问题上粒子滤波比传统的扩展卡尔曼滤波更具优势．     

水平集在盐水白带细胞显微图像分割中的应用

盐水白带细胞种类繁多成分复杂,细胞存在弱边缘、相互交织和面积小不易区分等特点,加之受杂质的影响,都给细胞分割带来困难.为此本文采用一种新的水平集方法,解决了传统水平集方法的演化曲线难以探测到细胞弱边缘的问题,而且这种水平集方法避免了重新初始化符号距离函数减少了计算量.实验结果表明,这种方法的演化曲线可以探测到细胞弱边缘,能够对盐水白带细胞显微图像进行分割.     

实时监控系统中运动人体图像分割

在运用差图像算法进行运动人体检测、噪声消除的基础上,提出了将统计直方图与梯度边缘检测相结合的算法以消除运动人体的影子;同时运用图像扩展模板并配合图像边界处平滑的算法对被分割人体的断裂处进行连接填充。实验结果验证了上述算法对噪声抑制、人影消除以及人体图像断裂处填充的有效性。     

基于形态学和Otsu的固体火箭发动机CT缺陷三维分割

为了能够准确判别固体火箭发动机内部缺陷的性质和可能对发动机造成的危害,需要从三维空间的角度来观察分析.在传统的缺陷分析中,主要是通过观察CT的二维切片序列图像以及对二维图像的主观分析去发现缺陷体.为了对缺陷体进行更为准确、立体地分析,提出对固体火箭发动机工业CT三维体数据进行处理.首先,通过结合形态学和Otsu阈值分割方法对缺陷进行分割和提取;然后,重构出缺陷体三维体数据;最后,对体数据进行三维可视化显示.实验结果表明,该方法能有效、准确地分割和提取固体火箭发动机三维CT图像缺陷,具有较强的鲁棒性.     

基于CT图像的金属点阵结构内部缺陷检测方法

基于计算机断层扫描(CT)图像的灰度值分布特征,提出了一种金属三维多层点阵结构内部缺陷的检测方法。通过3个横向相邻固定尺寸像素点集合灰度值总和之间的差值分布来判别缺陷的存在,理论分析给出了相应的判别标准和依据。实验验证结果表明:与人工标记的方法进行对比,该算法对金属三维多层点阵结构样件的内部典型断层缺陷的识别率达到98.5%。     

基于模拟退火并行遗传算法的Otsu双阈值医学图像分割

模拟退火和并行遗传算法是两种较好的改进进化算法性能的方法。将这两种思想有机地结合起来,利用遗传算法能全局寻优的优势和模拟退火算法的爬山性能,提出了一种基于模拟退火并行遗传算法的Otsu双阈值医学图像分割算法。在该算法中,进化在多个不同的子群中并行进行,利用模拟退火算法的爬山性能,避免单种群进化过程中出现的过早收敛现象,提高整个算法的收敛速度。实验证明,这种新的图像分割算法与并行遗传算法相比,不仅能够对图像进行准确的分割,而且具有更强的精确性和稳定性。其收敛速度明显比并行遗传算法的Otsu双阈值医学图像分割快。     

基于最佳方向性梯度通量血管增强的冠脉分割

针对多层螺旋CT（MSCT）冠状动脉分割时受周围静脉血管等组织的影响而容易发生泄漏的问题,提出了一种基于最佳方向性梯度通量（OOF）血管增强的分割方法．首先,得到原始图像的梯度向量场,选择合适的半径,计算球面特定方向上投影梯度的通量,寻找使得流向球体内部的投影通量最小的最佳方向．求解最佳方向上梯度通量矩阵的特征值,利用特征值构造血管相似度响应函数,对冠状动脉进行增强,之后采用自适应阈值的区域生长方法将冠脉血管分割出来．实验结果表明,该算法受冠脉周围组织的影响较小,避免了泄漏的发生,而且能提取到较多的细小分支．     

混合颗粒系重叠图像分割与分类方法研究

针对传统图像处理算法对重叠颗粒的分割困难,引入Mask R-CNN深度学习算法并做针对性改进,通过调整残差网络ResNet-101加速训练,提出双FPN结构实现全局特征融合,使用Soft-NMS避免重叠颗粒漏检。设计了颗粒重叠图像实验系统,采集单一球形、球形与不规则混合多分散颗粒重叠图像用于分割研究。实验结果表明:该方法对混合颗粒分类准确率为91%,召回率为92%,均优于传统算法;其应用于含气泡的一水柠檬酸结晶过程中结晶的在线测量,所得结晶颗粒中位径误差为3.8%,数目误差为-1.3%。所提方法为混合颗粒的重叠图像分析提供了思路,后续有望解决图像法结晶过程后期在线监测乏力与气泡干扰的问题。     

成组技术和分类编码技术在自动化仓库系统设计中的应用

分类编码技术主要应用于生产制造业,对零件分类成族,应用工艺原则对零件族进行加工。本文将其应用到自动化仓库系统的设计中,对入库货物分类编码,同时基于成组技术思想规划仓库并进行编码。