Statistical Methods in the Fourier Domain to Enhance and Classify Images

A mathematical model, for which rigorous methods of statistical inference are available, is described and techniques for image enhancement and linear discriminant analysis of groups are developed. Since the gray values of neighboring pixels in tomographically produced medical images are spatially correlated, the calculations are carried out in the Fourier domain to insure statistical independence of the variables. Furthermore, to increase the power of statistical tests the known spatial covariance was used to specify constraints in the spectral domain. These methods were compared to statistical procedures carried out in the spatial domain. Positron emission tomography (PET) images of alcoholics with organic brain disorders were compared by these techniques to age-matched normal volunteers. Although these techniques are employed to analyze group characteristics of functional images, they provide a comprehensive set of mathematical and statistical procedures in the spectral domain that can also be applied to images of other modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI).     

The role of image registration in brain mapping

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain.     

Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified Differential Evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no-reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend.     

卷积神经网络在生物医学图像上的应用进展

生物医学成像领域的迅速发展引起相关图像信息的爆炸式增长,对其图像进行人工智能辅助分析日益成为科学研究、临床应用、即时诊断等领域的迫切需求。近年来深度学习,尤其是卷积神经网络在生物医学图像分析领域取得广泛应用,在生物医学图像的信息提取,包括细胞分类、检测,生理及病理图像的分割、检测等领域发挥日益重要的作用。介绍了深度学习及卷积神经网络相关技术的发展;重点针对近几年卷积神经网络在细胞生物学图像、医学图像领域的应用进展进行了梳理;对卷积神经网络在生物医学图像分析领域研究目前存在的问题及可能的发展方向进行了展望。     

An ear biometric system based on artificial bees and the scale invariant feature transform

Ear recognition is a new biometric technology that competes with well-known biometric modalities such as fingerprint, face and iris. However, this modality suffers from common image acquisition problems, such as change in illumination, poor contrast, noise and pose variation. Using a 3D ear models reduce rotation, scale variation and translation-related problems, but they are computationally expensive. This paper presents a new architecture of ear biometrics that aims at solving the acquisition problems of 2D ear images. The proposed system uses a new ear image contrast enhancement approach based on the gray-level mapping technique, and uses an artificial bee colony (ABC) algorithm as an optimizer. This technique permits getting better-contrasted 2D ear images. In the feature extraction stage, the scale invariant feature transform (SIFT) is used. For the matching phase, the Euclidean distance is adopted. The proposed approach was tested on three reference ear image databases: IIT Delhi, USTB 1 and USTB 2, and compared with traditional ear image contrast enhancement approaches, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). The obtained results show that the proposed approach outperforms traditional ear image contrast enhancement techniques, and increases the amount of detail in the ear image, and consequently improves the recognition rate.     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

Phase-based optimal image thresholding

The automatic binarization of gray-level images or the automatic determination of an optimum threshold value that separates objects from their background is still a difficult and challenging problem in many image processing applications. The difficulty may arise due to a number of factors, including, poor contrast, high noise to signal ratio, complex patterns, and/or variable modalities in the gray-scale histograms. In this paper an algorithm for determining an optimum image thresholding value is proposed. Phase correlation between the gray-level image and its binary counterpart is defined as a function of the thresholding parameter. The optimum thresholding problem is then constructed as a problem of optimization where the objective is to find a threshold value that maximizes the phase correlation between the two images. Experimental results to compare the proposed algorithm to the various thresholding techniques are also presented.     

血管内超声图像后处理综述

血管内超声显像是目前临床常用的诊断血管病变的介入影像手段,可在活体中观 察血管壁和管腔的形态,以及斑块的形态和成分。采用数字图像处理技术,对血管内超声图像 序列进行自动或半自动地处理和分析,对于血管病变的计算机辅助诊断和制定最佳诊疗方案具 有重要意义。本文就近年来血管内超声图像计算机后处理的研究现状进行综述,包括图像分割 和组织标定、运动伪影的抑制、血管的三维重建、血管形态和血流动力学参数的测量、组织定 征显像及与其他影像的融合等,评价了目前的研究情况,并对未来的研究提出了展望。     

Super-resolution: a comprehensive survey

Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.     

Detection of multi-class lung diseases based on customized neural network

In the medical image processing domain, deep learning methodologies have outstanding performance for disease classification using digital images such as X-rays, magnetic resonance imaging (MRI), and computerized tomography (CT). However, accurate diagnosis of disease by medical personnel can be challenging in certain cases, such as the complexity of interpretation and non-availability of expert personnel, difficulty at pixel-level analysis, etc. Computer-aided diagnostic (CAD) systems with proper training have shown the potential to enhance diagnostic accuracy and efficiency. With the exponential growth of medical data, CAD systems can analyze and extract valuable information by assisting medical personnel during the disease diagnostic process. To overcome these challenges, this research introduces CX-RaysNet, a novel deep-learning framework designed for the automatic identification of various lung disease classes in digital chest X-ray images. The core novelty of the CX-RaysNet framework lies in the integration of both convolutional and group convolutional layers, along with the usage of small filter sizes and the incorporation of dropout regularization. This phenomenon helps us optimize the model's ability to distinguish minute features that reveal different lung diseases. Additionally, data augmentation techniques are implemented to augment the training and testing datasets, which enhances the model's robustness and generalizability. The performance evaluation of CX-RaysNet reveals promising results, with the proposed model achieving a multi-class classification accuracy of 97.25%. Particularly, this study represents the first attempt to optimize a model specifically for low-power embedded devices, aiming to improve the accuracy of disease detection while minimizing computational resources.     

Detection of abnormal traffic video images based on high-dimensional fuzzy geometry

Detection of abnormal video images of transportation is to find out video images that contain abnormities among all images of transportation using video and image processing and analyzing techniques. It is an important component of intelligent transportation system, which can not only reduce the workload of traffic managers, but also effectively improve the efficiency of traffic management. However, video images of transportation in practice usually have complex backgrounds, and current detecting algorithms of traffic abnormity sometimes become ineffective due to interference factors such as noises and affine transformation (illumination variation, target occlusion, scale changes and view changes, etc.). In order to overcome these interference factors and fuzzy uncertainties in image representation, as well as improve the accuracy of traffic images representation, this study explored the representation methods of traffic images using fuzzy geometry theory on the basis of fuzzy uncertainties occurring during the process of imaging, transmission and processing of images; moreover, this study also put forward two kinds of representation algorithms of traffic images, and analyzed and verified effectiveness of representation algorithms based on theories and experiments.     

Imaging cerebral function

The Scan Analysis and Visualization Processor (Scan/VP), a flexible, portable, Unix-based software package for visualizing and analyzing positron emission tomography (PET) images in a clinical-research setting, is described. PET systems are compared to computerized tomography (CT) and magnetic resonance imaging (MRI) systems. The imaging and software aspects of Scan/VP, and procedures devoted specifically to functional PET imaging, including mathematical modeling, image registration, regional thresholding, and derivation of regional covariation patterns, are discussed. Basic surface display, animation, and stereo techniques for visualizing variations in metabolic topology and underlying disease patterns are also discussed     

Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

In the last two decades, we have seen an amazing development of image processing techniques targeted for medical applications. We propose multi-GPU-based parallel real-time algorithms for segmentation and shape-based object detection, aiming at accelerating two medical image processing methods: automated blood detection in wireless capsule endoscopy (WCE) images and automated bright lesion detection in retinal fundus images. In the former method we identified segmentation and object detection as being responsible for consuming most of the global processing time. While in the latter, as segmentation was not used, shape-based object detection was the compute-intensive task identified. Experimental results show that the accelerated method running on multi-GPU systems for blood detection in WCE images is on average 265 times faster than the original CPU version and is able to process 344 frames per second. By applying the multi-GPU framework for bright lesion detection in fundus images we are able to process 62 frames per second with a speedup average 667 times faster than the equivalent CPU version.     

基于阈值及像素聚类的分割算法性能比较

图像分割是图像处理到分析的关键步骤,阈值分割方法因其计算简单而被广泛应用,聚类算法也因其准确性成为图像分割领域中一类极其重要的算法。选取几种经典阈值分割算法和几种聚类算法对几幅毫米波图像进行分割实验,并引入错分类误差、均匀测度、区域间灰度对比度作为算法测评标准,比较了各种算法对毫米波图像的分割性能。     

基础信息保持和细节强化的胸部CT图像增强

目的 清晰的胸部计算机断层扫描（computed tomography,CT）图像有助于医生准确诊断肺部相关疾病,但受成像设备、条件等因素的限制,扫描得到的CT图像质量有时会不尽如人意。因此,本文提出一种简单有效的基于基础信息保持和细节强化的胸部CT图像增强算法。方法 利用多尺度引导滤波器将胸部CT图像分解为一个基础信息层和多个不同尺度的细节层。基于熵的权重将胸部CT图像的多个细节层进行融合,并乘以强化系数进一步增强纹理细节。将强化的细节层和原始的基础信息层重新组合即可生成细节强化的胸部CT图像。通过此种增强方式,本文算法既能显著增强胸部CT图像的纹理细节,又能将大部分原始的基础结构信息保留到增强图像中。结果 为了验证算法的有效性,将本文算法与5种优秀的图像增强算法在由3 209幅胸部CT图像组成的数据集上进行测试评估。定性和定量实验结果表明,本文算法得到的增强图像保持了更多原始胸部CT图像中的基础结构信息,并更显著地强化了其中的纹理细节信息。在定量结果中,本文算法的标准差、结构相似性和峰值信噪比指标值均优于对比的5种方法,相比于性能第2的方法分别提高了4.95、0.16和4.47,即分别提升了5.61%、17.00%和16.17%。此外,本文算法增强一幅CT图像仅消耗0.10 s,有潜力应用于实际的临床诊断中。结论 本文算法可以在保留大量原始结构信息的同时有效强化CT图像的细节信息,有助于医生对患者肺部疾病的精确诊断。本文算法具有较好的泛化能力,可以用于增强其他部位的CT图像和其他模态图像并取得优秀的增强结果。     

多尺度变换像素级医学图像融合：研究进展、应用和挑战

基于多尺度变换的像素级图像融合是计算机视觉领域的研究热点,广泛应用于医学图像处理等领域。本文对多尺度变换的像素级图像融合进行综述,阐述多尺度变换图像融合的基本原理和框架。在多尺度分解方面,以时间为序梳理了塔式分解、小波变换和多尺度几何分析方法的发展历程。在融合规则方面,围绕Piella框架和Zhang框架,讨论通用的像素级图像融合框架;在低频子带融合规则方面,总结基于像素、区域、模糊理论、稀疏表示和聚焦测度的5种融合规则;在高频子带融合规则方面,综述基于像素、边缘、区域、稀疏表示和神经网络的5种融合规则。总结12种跨模态医学图像融合方式,讨论该领域面临的主要挑战,并对未来的发展方向进行展望。本文系统梳理了多尺度变换像素级图像融合过程中的多尺度分解方法和融合规则,以及多尺度变换在医学图像融合中的应用,对多尺度变换像素级医学图像融合方法的研究具有积极的指导意义。     

Reconstruction of 3D medical images: A nonlinear interpolation technique for reconstruction of 3D medical images

Three-dimensional medical images reconstructed from a series of two-dimensional images produced by computerized tomography, magnetic resonance imaging, etc., present a valuable tool for modern medicine. Usually, the interresolution between two cross sections is less than the intraresolution within each cross section. Therefore, interpolations are required to create a 3D visualization. Many techniques, including voxel-based and patch tiling methods, apply linear interpolations between two cross sections. Although those techniques using linear interpolations are economical in computation, they need much cross-sectional data and are unable to enlarge because of aliasing. Hence, the techniques that apply two-dimensional nonlinear interpolation functions among cross sections were proposed. In this paper, we introduce the curvature sampling of the contour of a medical object in a CT (computerized tomography) image. Those sampled contour points are the candidates for the control points of Hermite surfaces between each pair of cross sections. Then, a nearest-neighbor mapping of control points between every two cross sections is used for surface formation. The time complexity of our mapping algorithm is O(m + n), where m and n are the numbers of control points of two cross sections. It is much faster than Kehtarnavaz and De Figueiredo's merge method, whose time complexity is O(n³m²).     

目标成像处理算法验证平台设计与实现

为了验证基于光电图像的目标成像处理算法,并对不同算法之间的优缺点进行比较,设计并实现了操作简单、移植性强的目标成像处理算法验证平台。基于VC＋＋6.0进行模块化设计,方便了经典算法的改进和新算法的植入,并且易于验证、调试与修改。视频源处理模块为待验证的目标跟踪算法提供可自定义亮度、对比度、噪声等的场景。同时,算法中预处理、目标分割等模块可将质量较差的图像进行去噪、增强、边缘提取等变换,易于跟踪算法对有用信息的提取。设计模块之间的接口,使平台具有更强的可植入性,方便目标跟踪算法的开发。     

基于模糊理论的SAR图像海上舰船检测方法研究

舰船检测是合成孔径雷达图像海洋应用的一个重要部分,针对中分辨率近岸海域SAR图像,提出了一种基于模糊理论的海上舰船检测方法。该方法先利用改进的模糊增强算法对图像进行增强处理,以改变图像灰度的分布特性,从而分离图像中海洋区域和陆地区域,并结合最大熵分割法提取海洋背景中包含候选舰船的感兴趣区域,最后,对ROI区域进行分割,提取舰船的特征,并基于模糊推理技术实现对海上舰船目标的检测。