Fast wavelet-based multiresolution image registration on a multiprocessing digital signal processor

Image registration is a fundamental task in image processing. It is used in matching two or more images taken at different times, from different imaging modalities, or from different viewpoints. One of the obstacles in achieving practical acceptance of image registration techniques is their computational complexity, which results in a long response time. In this article we present a fast multiresolution image registration algorithm using wavelet transform for the translational and rotational alignment of two-dimensional images. In particular, a novel approach to determine the algorithm parameters to balance the registration accuracy and computational requirement is also described. We implemented this algorithm on a PC-based multimedia and imaging system using a multiprocessing digital signal processor. The algorithm is capable of achieving a subpixel registration accuracy reliably under various noise levels. The multiresolution algorithm implemented on this desktop system was able to register two 256 × 256 images in 466 ms, which is 40 times faster than the uniresolution exhaustive search approach. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 29–37, 1998     

A blockwise relaxation labeling scheme and its application to edge detection in cardiac MR image sequences

We present a segmentation scheme for magnetic resonance (MR) image sequences based on vector quantization of a block-partitioned image followed by a relaxation labeling procedure. By first searching a coarse segmentation, the algorithm yields very fast and effective performance on images that are inherently noisy, and can effectively use the correlation in a sequence of images for robust performance and efficient implementation. The algorithm defines feature vectors by the local histogram on a block-partioned image and approximates the local histograms by normal distributions. The relative entropy is chosen as the meaningful distance measure between the feature vectors and the templates. After initial computation of the normal distribution parameters, a blockwise classification maximization algorithm classifies blocks in the block-partitioned image by minimizing their relative entropy distance for a coarse-resolution segmentation; and finally, finer resolution is obtained by contextual Bayesian relaxation labeling in which label update is performed pixelwise by incorporating neighborhood information. Sequence processing is then performed to segment all images in the sequence. The scheme is applied to left ventricular boundary detection in short-axis MR image sequences, and results are presented to show that the algorithm successfully extracts the endocardial contours and that sequence processing significantly improves edge detection performance and can avoid local minima problems. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 340–350, 1998     

Hierarchical fuzzy segmentation of brain MR images

In brain magnetic resonance (MR) images, image segmentation and 3D visualization are very useful tools for the diagnosis of abnormalities. Segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is the basic process for 3D visualization of brain MR images. Of the many algorithms, the fuzzy c‐means (FCM) technique has been widely used for segmentation of brain MR images. However, the FCM technique does not yield sufficient results under radio frequency (RF) nonuniformity. We propose a hierarchical FCM (HFCM), which provides good segmentation results under RF nonuniformity and does not require any parameter setting. We also generate Talairach templates of the brain that are deformed to 3D brain MR images. Using the deformed templates, only the cerebrum region is extracted from the 3D brain MR images. Then, the proposed HFCM partitions the cerebrum region into WM, GM, and CSF. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 115–125, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10035     

多级FFD配准视频人脸超分辨率重建

超分辨率重建是解决视频人脸识别中人脸分辨率低的有效方法,但由于人脸畸变、表情变化等非刚性变化导致无法精确配准和重建.针对此问题,提出基于B样条的多级模型自由形式形变(FFD)弹性配准算法.先用低分辨率FFD网格全局配准,再对全局配准后的图像分块并计算对应子图块的相关性系数,对相关性系数小的子图块用高分辨率FFD网格局部细配准.在配准的寻优过程中采用基于混沌因子的自适应步长最速下降法提高寻优效率.配准后,采用POCS算法对多帧图像重建高分辨率图像来识别.在标准视频库和自建视频库上实验仿真,结果表明在人脸畸变和表情变化很大的情况下,也能够精确的配准和很好的重建,得到较高识别率.     

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.     

C/C复合材料热解炭消光角测量中图像配准算法

用偏振光显微镜旋转成像测量C/C复合材料热解炭消光角过程中,所采集的序列图像间经常出现目标偏移现象。针对该问题,提出了一种基于结构特征的图像配准算法。该算法采用圆边缘检测算子检测每幅图像中同一纤维圆截面的圆心,以圆心作为图像配准的匹配点,通过坐标变换实现图像配准。在热解炭消光角测量过程中的应用结果表明,该配准算法可以避免因目标偏移而造成的数据采集误差,保证消光角测量的准确实现。     

车载图像配准稳定算法中的关键技术

提出一种有效的车载图像配准稳定算法,这种算法的关键技术包括：一是采用由粗到精、由局部匹配到全局配准的两级配准策略,即在粗配准阶段,利用基于灰度投影均值的SSDA改进算法进行快速模板匹配;精配准时,借鉴联合直方图区域记数法的思路,在灰度信息的统计特性空间上巧妙定义一种全局准则函数,既能保证精度又摆脱了大量的浮点运算。二是在运动滤波时通过自适应选取滑动窗口的长度,降低了图像序列的抖动,同时防止过稳现象的发生。实验结果表明,该算法的平移配准误差( 0．254, 0．083)远远小于1个像素,且在一定硬件平台上稳定单帧图像需要14．7ms,仅为同精度其它算法的1／3,从而满足了车载图像系统的实时性和精度要求。     

Medical image registration‐based retrieval using distance metrics

In this article, registration and retrieval are carried out separately for medical images and then registration‐based retrieval is performed. It is aimed to provide a more thorough insight on the use of registration, retrieval, and registration‐based retrieval algorithm for medical images. The purpose of this work is to deal these techniques with anatomical imaging modalities for clinical diagnosis, treatment, intervention, and surgical planning in a more effective manner. Two steps are implemented. In the first step, the affine transformation‐based registration for medical image is processed. The second step is the retrieval of medical images processed by using seven distance metrics such as euclidean, manhattan, mahalanobis, canberra, bray‐curtis, squared chord, chi‐squared, and also by using the features like mean, standard deviation, skewness, energy, and entropy. Now images registered by affine transformation are applied for retrieval. In this work, both registration and retrieval techniques in medical domain share some common image processing steps and required to be integrated in a larger system to complement each other. Experimental results, it is evident that euclidean and manhattan produces 100% precision and 35% recall found to have higher performance in retrieval. From the four anatomical modalities considered (brain, chest, liver, and limbs) brain image has better registration. It is also found that though the registration of images changes the orientation, for better performance of images in clinical evaluation it does not widely affect the retrieval performance. In the medical domain the ultimate aim of this work is to provide diagnostic support to physicians and radiologists by displaying relevant past cases, along with proven pathologies as ground truth from experimental results. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 360–371, 2013     

一类禁忌算法及其在医学图像配准中的应用

针对脑功能成像中图像配准的优化模型,提出了一种基于记忆的禁忌算法。数值试验表明该算法是可行的、有效的。应用此算法求解医学图像配准的优化模型,可实现时间序列脑功能图像的高精度配准。     

Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed‐based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 ‐ w, T2 – w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C – Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO‐based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO‐based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33–45, 2017     

基于虚拟点的可见光和SAR图像配准研究

本文以机场场景下的可见光和SAR图像为研究对象,提出了一种基于虚拟点特征的可见光和SAR图像配准方法.该方法以虚拟点特征和控制点匹配技术为基础,处理具有全局仿射几何失真的异源图像配准问题.首先根据两类图像的特点,使用Canny算子和一种兴趣算子提取两幅图像中的共有特征一直线特征,然后在直线特征的基础上拟合虚拟点特征,采用基于特征一致的粗配准和基于虚拟点特征的精确配准相结合的方法,对两幅图像实现由粗到精的自动配准,实验结果表明,本文方法可行且能取得较高的配准精度.     

复杂底纹柔性卫生用品表面缺陷检测算法研究

目的 柔性卫生用品由于其材料表面不平整、产品容易发生变形等特性,给机器视觉中图像关键区域的定位和分割带来了很大问题,从而影响检测结果。为了解决复杂底纹柔性卫生用品表面脏点的检测问题,提出一种新型检测算法,以实现对复杂底纹柔性卫生用品快速又精准的检测。方法 在该检测算法中首先对采集的模板图像和样本图像进行预处理,在模板图像中创建一个合适特征模板,根据特征模板进行样本图像的匹配和定位;然后把经过精准定位后的样本图像进行关键区域分割;最后运用差影法把样本关键区域中的缺陷找出。该算法采用德国MVtec公司的halcon算子加以编程实现。结果 实验结果显示,该算法能够识别棉芯表面大于0.04 mm2的脏点,检测1幅图片的平均时间平均在100 ms,检测准确率为100%。结论 文中方法与传统方法相比,鲁棒性强,速度快,能够满足工业高速生产的需求。     

Interest points localization for brain image using landmark‐annotated atlas

The localization of clinically important points in brain images is crucial for many neurological studies. Conventional manual landmark annotation requires expertise and is often time‐consuming. In this work, we propose an automatic approach for interest point localization in brain image using landmark‐annotated atlas (LAA). The landmark detection procedure is formulated as a problem of finding corresponding points of the atlas. The LAA is constructed from a set of brain images with clinically relevant landmarks annotated. It provides not only the spatial information of the interest points of the brain but also the optimal features for landmark detection through a learning process. Evaluation was performed on 3D magnetic resonance (MR) data using cross‐validation. Obtained results demonstrate that the proposed method achieves the accuracy of ～ 2 mm, which outperforms the traditional methods such as block matching technique and direct image registration. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 145–152, 2012     

基于子空间近似对称Hausdorff分数图像匹配方法

模板匹配方法,对于如旋转、尺度等变化造成的形变非常敏感,通常需要利用大量的参考模板进行匹配,因此匹配速度较慢.由于不同模板之间的畸变不是很大,故可以利用子空间近似的方法,利用特征分解得到模板集的特征子空间,然后利用特征子空间近似表示模板集.笔者考虑到子空间近似的特点,利用对称Hausdorff分数的表示方法,使得与模板相匹配图像特征向量空间和根据模板得到的特征向量空间基本一致,从而提高了匹配效率.     

基于特征匹配的地图图像自动配准技术研究

本文针对地图中的特征点，提出了一种基于广义特征点的图像自动配准方法，将特征点从单纯的点拓展到特征区域。以Moravec算子结合其他特征约束条件来自动搜索广义特征点。分别对两幅图像提取广义特征点后，利用基于根均方误差和交叉相关的两级匹配算法完成同名控制点的建立。并以局部加权直线拟合方法来校正图像的几何畸变。最后建立两幅图像之间的函数映射关系，完成图像的配准。实验结果证明了该方法的有效性。该方法可用于校正近景面地图影像的几何畸变和遥感图像的局部几何畸变。     

Fuzzy entropy‐based MR brain image segmentation using modified particle swarm optimization

This article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: Z‐function and S‐function. The optimal parameters of these fuzzy MFs are obtained using modified particle swarm optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy‐based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281–288, 2013     

Application of Elastic Image Registration and Refraction Correction for Non‐Contact Underwater Strain Measurement

Abstract: The refraction‐induced image distortion introduces large errors in the deformation measurement of fluid submerged specimens using digital image correlation (DIC). This study provides a review of the nature of the refraction‐induced image distortion, assesses experimental conditions that interact with refraction and proposes an elastic image registration technique to correct the refraction distortion of underwater images. In the elastic image registration technique, control points are selected on reference and refracted images of a template object and locally sensitive transformation functions that overlay the two images are obtained. The transformation functions so obtained are then used to reconstruct undistorted images from underwater images and the former are used as input to a DIC system. The proposed approach has shown to improve the refraction error in the order of 5–8% for typical material test samples undergoing deformation inside a water‐filled glass chamber.     

Integrated segmentation of brain tumor images for radiotherapy and neurosurgery

Segmentation of brain tumor images is an important task in diagnosis and treatment planning for cancer patients. To achieve this goal with standard clinical acquisition protocols, conventionally, either classification algorithms are applied on multimodal MR images or atlas‐based segmentation is used on a high‐resolution monomodal MR image. These two approaches have been commonly regarded separately. We propose to integrate all the available imaging information into one framework to be able to use the information gained from the tissue classification of the multimodal images to perform a more precise segmentation on the high‐resolution monomodal image by atlas‐based segmentation. For this, we combine a state of the art regularized classification method with an enhanced version of an atlas‐registration approach including multiscale tumor‐growth modeling. This contribution offers the possibility to simultaneously segment subcortical structures in the patient by warping the respective atlas labels, which is important for neurosurgical planning and radiotherapy planning. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 59–63, 2013     

Brightness preserving optimized weighted bi-histogram equalization algorithm and its application to MR brain image segmentation

Medical image segmentation is crucial for neuroscience research and computer-aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast-enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub-histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub-histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.