Study on the methods of image enhancement for liver CT images

Image enhancement methods for liver CT images are studied in this paper. The liver region is first segmented from the whole CT image by simply using the characteristics of the gray distribution of the liver. The segmented liver CT image is processed by direct gray stretching, logarithmic transformation and linear stretching with histogram fitting. In addition, the method of selective histogram equalization is proposed to enhance the segmented liver CT image. It is proven from the experimental results that by this two-step method, the visual effect of the segmented image can be effectively improved and the focus is obviously highlighted.     

图像配准中几种特征点提取方法的分析与实验

研究了图像配准中常用的几种特征点提取方法。基于图像配准对特征提取方法的特殊要求,提出了一组量化的特征提取方法评价因子,能用于合理评价各种方法的性能、优点与缺点,为这些方法的改进提供指导性意见和比较依据。通过大量的实验,利用评价因子比较和分析了上述的几种常用特征提取方法的优缺点、适用环境,并提出改进建议。     

Objective quantification of intervertebral disc volume properties using MRI in idiopathic scoliosis surgery

The aim of this study was to quantify from magnetic resonance imaging (MRI) the volume and hydration variation of the intervertebral disc in the lumbar spine before and after surgery in severe idiopathic scoliosis cases. MRI data were posttreated using a custom-made image processing software to semiautomatically derive volume properties of disc, annulus fibrosus and nucleus pulposus. The nucleus-disc volume ratio was also an indicator of the hydration level. The MRI that was performed in the clinical protocol concerned 28 patients with severe idiopathic scoliosis; four types of surgical instrumentations were used. The reliability of the three-dimensional reconstruction process was initially verified using an intraoperator reproducibility test. Original preoperative data on disc volume properties were then derived. Postoperative volume variations were quantified in discs below spine fusion, taking into account the level of arthrodesis and the disc location. It showed that the postoperative volume criteria increased significantly for nucleus, disc and nucleus-disc volume ratio and that some magnitude modulation could be conditioned by the location of surgical instrumentation. It tended to prove that the recovery of balanced physiological positioning and inherent biomechanical loads could induce a restored hydration of disc, which should favor the remodeling of free segments. This work was the first report to deal with the consequences of scoliosis surgery on subjacent disc in terms of volume and hydration properties. The clinical outcome will follow based on the patient cohort follow-up at 1 year after surgery.     

Mean shift based clustering of neutrosophic domain for unsupervised constructions detection

Automation has been a hot issue in constructions extraction, but there has not yet been a universally accepted algorithm. Commonly, constructions are extracted by user-defined thresholds, and they have to be adjusted with the variation of images and types of constructions. To overcome the shortages, an unsupervised algorithm to extract constructions is proposed in this paper. It adopts mean shift clustering in neutrosophic set domain to segment images, which makes it possible to detect constructions with a stable threshold. The algorithm is compared with three welcomed and recently developed supervised techniques by six study images with two sorts of resolutions. Experiments show that among the four algorithms, the method proposed in this paper performs best in constructions detection. It not only maintains the original shape of buildings, but also generates extracted constructions as a neat whole. Furthermore, the new method has stronger robustness when faced with images with different resolutions and imaging qualities. As tests show that the new algorithm can reach a kappa coefficient of 0.7704 and an accuracy of 89.8054%, which are relatively high in constructions extraction, it can be a robust unsupervised technique to extract constructions.     

Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove “salt-and-pepper” noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small.     

A quantitative comparison of two methods to correct eddy current-induced distortions in DT-MRI

Eddy current-induced geometric distortions of single-shot, diffusion-weighted, echo-planar (DW-EP) images are a major confounding factor to the accurate determination of water diffusion parameters in diffusion tensor MRI (DT-MRI). Previously, it has been suggested that these geometric distortions can be removed from brain DW-EP images using affine transformations determined from phantom calibration experiments using iterative cross-correlation (ICC). Since this approach was first described, a number of image-based registration methods have become available that can also correct eddy current-induced distortions in DW-EP images. However, as yet no study has investigated whether separate eddy current calibration or image-based registration provides the most accurate way of removing these artefacts from DT-MRI data. Here we compare how ICC phantom calibration and affine FLIRT (http://www.fmrib.ox.ac.uk), a popular image-based multi-modal registration method that can correct both eddy current-induced distortions and bulk subject motion, perform when registering DW-EP images acquired with different slice thicknesses (2.8 and 5 mm) and b-values (1000 and 3000 s/mm(2)). With the use of consistency testing, it was found that ICC was a more robust algorithm for correcting eddy current-induced distortions than affine FLIRT, especially at high b-value and small slice thickness. In addition, principal component analysis demonstrated that the combination of ICC phantom calibration (to remove eddy current-induced distortions) with rigid body FLIRT (to remove bulk subject motion) provided a more accurate registration of DT-MRI data than that achieved by affine FLIRT.     

基于分时积分亚像元融合的地球静止轨道平台消颤振技术

相较于低轨卫星线阵扫描成像模式,地球静止轨道面阵成像的曝光时间相对更长,更容易受到平台颤振的影响而造成图像模糊.为了消除由平台颤振引起的像质退化,本文提出了基于分时积分亚像元融合的方法.由于地球静止轨道的凝视成像特性,相机观察区域在长时间内保持不变,因此分时短曝光可以获得多帧目标内容相同,但模糊尺度更低的短曝光图像.然后对多帧短曝光图像采用基于能量区域质心法的相位相关算法进行亚像元图像配准,计算相对偏移量并进行补偿,位移探测准确度可达0.1像元以内,满足卫星平台应用需求.再按亚像元偏移量对多帧图像进行融合,融合的过程可以提升由于曝光时间缩短而降低的单帧图像信噪比,最终可以获得图像清晰度更高、信噪比与原长曝光图像相当、信息辨识度更好的遥感图像.     

基于分时积分亚像元融合的地球静止轨道平台消颤振技术

相较于低轨卫星线阵扫描成像模式,地球静止轨道面阵成像的曝光时间相对更长,更容易受到平台颤振的影响而造成图像模糊.为了消除由平台颤振引起的像质退化,本文提出了基于分时积分亚像元融合的方法.由于地球静止轨道的凝视成像特性,相机观察区域在长时间内保持不变,因此分时短曝光可以获得多帧目标内容相同,但模糊尺度更低的短曝光图像.然后对多帧短曝光图像采用基于能量区域质心法的相位相关算法进行亚像元图像配准,计算相对偏移量并进行补偿,位移探测准确度可达0.1像元以内,满足卫星平台应用需求.再按亚像元偏移量对多帧图像进行融合,融合的过程可以提升由于曝光时间缩短而降低的单帧图像信噪比,最终可以获得图像清晰度更高、信噪比与原长曝光图像相当、信息辨识度更好的遥感图像.     

Accuracy of image registration between MRI and light microscopy in the ex vivo brain

A multistep procedure was developed to register magnetic resonance imaging (MRI) and histological data from the same sample in the light microscopy image space, with the ultimate goal of allowing quantitative comparisons of the two datasets. The fixed brain of an owl monkey was used to develop and test the procedure. In addition to the MRI and histological data, photographic images of the brain tissue block acquired during sectioning were assembled into a blockface volume to provide an intermediate step for the overall registration process. The MR volume was first registered to the blockface volume using a combination of linear and nonlinear registration, and two dimensional (2D) blockface sections were registered to corresponding myelin-stained sections using a combination of linear and nonlinear registration. Before this 2D registration, two major types of tissue distortions were corrected: tissue tearing and independent movement of different parts of the brain, both introduced during histological processing of the sections. The correction procedure utilized a 2D method to close tissue tears and a multiple iterative closest point (ICP) algorithm to reposition separate pieces of tissue in the image. The accuracy of the overall MR to micrograph registration procedure was assessed by measuring the distance between registered landmarks chosen in the MR image space and the corresponding landmarks chosen in the micrograph space. The average error distance of the MR data registered to micrograph data was 0.324±0.277 mm, only 8% larger than the width of the MRI voxel (0.3 mm).     

Enhancement of histological volumes through averaging and their use for the analysis of magnetic resonance images

Magnetic resonance imaging (MRI) of small animals is routinely performed in research centers. But despite its many advantages, MR still suffers from limited spatial resolution which makes the interpretation and quantitative analysis of the images difficult, particularly for small structures of interest within areas of significant heterogeneity. One possibility to address this issue is to complement the MR images with histological data, which requires reconstructing 3D volumes from a series of 2D images. A number of methods have been proposed recently in the literature to address this issue, but deformation or tearing during the slicing process often produces reconstructed volumes with visible artifacts and imperfections. In this paper, we show that a possible solution to this problem is to work with several histological volumes, reconstruct each of these separately and then compute an average. The resulting histological atlas shows structures and substructures more clearly than any individual volume. We also propose an original approach to normalize intensity values across slices, a required preprocessing step when reconstructing histological volumes. We show that the histological atlas we have created can be used to localize structures and substructures, which cannot be seen easily in MR images. We also create an MR atlas that is associated with the histological atlas. We show that using the histological volumes to create the MR atlas is better than using the MR volumes only. Finally, we validate our approach quantitatively on MR image volumes by comparing volumetric measurements obtained manually and obtained automatically with our atlases.     

A semi-automated hybrid algorithm for accurate detection and counting of chemically etched tracks

In this paper, we present an accurate and comprehensive semi-automated algorithm for detection and counting the chemically etched tracks on polycarbonate nuclear Track detectors. The proposed algorithm consists of two main phases: a preprocessing phase for image preparation and quality improvement. It uses a format conversion into gray-scale image and contrast enhancement procedure. The contrast enhancement procedure employs the first-order fuzzy moment of the image and the Sugeno class of fuzzy complements to maximize the parametric indices of fuzziness of the image. In one feature extraction phase, two intrinsic characteristics of tracks are defined and formulated as topological feature (TF) and chromatic feature (CF). TF refers to tracks shape and geometry which are characterized and quantized by calculating the local Gray Level Spatial Correlation histogram of the tracks and CF refers to the color intensity difference of the tracks borders and tracks centric areas. The CF characterizes this difference specified to the tracks. After the feature extraction phase, the algorithm enters the detection and counting phase in which the fuzzy sets of background, noise (including small objects, notches, etc.) and track are constructed. Detected objects are then characterized according to their membership values to the fuzzy sets. The algorithm is designed in order to be applicable for different forms of tracks and track counting systems. Accuracy, linearity, sensitivity to the input parameters and comprehensibility of the method are validated in the simulation section.     

On the structure and volume growth of submanifolds in Riemannian manifolds

The aim of this note is two-fold. First, we investigate the relations between the volume growth of a submanifold and its second fundamental form. In the second part, we discuss the relations between the index of some Schrödinger operators and the structure of a submanifold, and prove some one-end theorems.     

Relationship between the changes in cellular volume of fish spermatozoa and their cryoresistance

We have investigated the hypothesis that the spermatozoa of marine fish are more resistant than freshwater species to the dynamic changes in osmotic pressure that occur during the process of cryopreservation. We show that while the spermatozoa of marine fish can be successfully activated across a wide range of osmotic pressures (0-2000 mOsmol/l), those of the freshwater species only survive activation within a more restricted range (0-300 mOsm/l). After freeze-thawing, up to 30 percent of motile cells were found in silver carp samples, while up to 90 percent of motile cells were observed in samples from the haarder (Mugil soiuy B). Haarder spermatozoa showed no change of cell volume after dilution in activating or cryoprotective media, while the silver carp spermatozoa responded by swelling and eventual cell disruption. We propose that the differences in cryoresistance of silver carp (Hypophthalmichthys molitrix V.) and haarder spermatozoa may be determined by the ability to preserve cellular volume under non-isotonic conditions.     

中阶梯光栅光谱仪谱图背景去除算法

中阶梯光栅光谱仪凭借交叉色散特性实现全谱瞬态直读,面阵探测器接收的二维光谱图像需要还原成一维谱图以提取有效波长。由于二维谱图含有庞大的数据,且有效信息仅占极小比例,因此在谱图还原前进行背景去除能够减小数据量、提高运算速度。详细分析了中阶梯光栅光谱仪二维图像的特点,并针对其特点提出了背景去除算法。将图像边缘检测方法应用于弥散光斑的检测中,选择合适的边缘检测算子与原始图像卷积得到边缘图像,设置边缘图像的全局阈值对其进行二值分割,最终利用二值边缘图像映射原始图像得到去除背景的二维谱图。依据不同元素灯在不同积分时间下所拍摄的谱图,对比不同边缘检测算法的背景去除效果,分析了各算子对算法速度、精度的影响。实验结果表明本文提出的算法运算简单、边缘图像阈值易于计算、目标提取精度高,处理后的图像可以与谱图还原算法有效对接,谱图处理速度显著提升。     

Partially coherent optical processor for enhancement of partial second order derivatives of an image: Analysis and implementation

We present an optical processor capable to perform partial second derivatives of an image working with partially coherent illumination. The proposed system utilizes a liquid crystal display, onto which images to be derived are displayed. The method is based on the capacity of these devices to generate simultaneously a contrast reverted replica of the image displayed on it. A positive replica of original image is obtained when the LCD is between crossed polarizers, while the negative one is obtained with the LCD is between parallel polarizers. Since the LCD is a diffraction element, both polarizer arrangements may be simultaneously implemented, for example, in a 4f optical processor using polarization filtering of the different diffraction orders. When three images (two replicas of original image displaced in opposite direction and the other one with contrast reverted and centered at the origin.) are imagined across a slightly defocused plane, one obtains an image with enhanced second derivatives in a given direction. No other incoherent (or partially coherent) optical system is presently known which can perform partial second-order derivatives of an image. Experimental results obtained are presented.     

中药口服液中可见异物检测系统研究

针对目前生产线上中药口服液中可见异物检测系统存在的速度慢、精度低等问题,设计了一种可同时检测多个药瓶的基于机器视觉的异物快速检测系统。介绍了成像系统和机械系统。提出了一种基于边缘轮廓的图像配准算法,有效地消除了药瓶晃动的影响。提出了一种逐点阈值分割图像的二值化方法,能得到质量较好的二值化图像。实验结果表明,该系统能快速、有效地检测出可见异物,能较好地满足在线检测需求。     

Segment-based region of interest generation for pedestrian detection in far-infrared images

We present a region of interest (ROI) generation method specialized for nighttime pedestrian detection using far-infrared (FIR) images. Because pedestrians typically appear brighter than background in FIR images, previous research efforts primarily attempted to extract ROIs based on the intensity threshold. However this approach has problems resulting from the intensity variances of pedestrians due to their clothing and, especially in urban scenarios, and other heat sources that emit more heat than the pedestrians. In this paper, we propose a novel ROI generation method that is based on combining image segments instead of using the intensity threshold. In order to minimize dependence on brightness, we utilize the low-frequency characteristics of FIR images. As a result, our proposed method generates a small number of ROIs at an acceptable miss rate and the generated ROIs provide advantages for classification because the pedestrians are satisfactorily arranged within a bounding box. Experiments conducted indicate that our proposed method performs reliably in urban scenarios.     

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.     

Proposal, verification and comparison of three computer image analysis methods for detection and evaluation of colour glaucomatous changes within the optic disc of a human eye retina

The typical symptom of the human eye glaucoma is a rise and a progression of the bright area (named pallor area) within the retina blind spot. The image analysis manner proposed by the authors detects and suitably numerically describes the relative size of the representative pallor area in the colour digital image of the retina obtained by a suitable fundus camera connected with the computer. Three new different computer image analysis statistical methods for experimental diagnostic evaluation of the obtained characteristic data are proposed in this article: the quantile curves method, the neural net method and the probability density curves method. The quantile curves method is based on the graphical comparison of a relative representative pallor area size with its determined normal value. The neural net and probability density curves methods can automatically and objectively classify the investigated eyes in exactly defined glaucoma risk classes and diagnosed glaucoma with the rated probabilities of incorrect diagnosis determination. All mentioned methods are verified and mutually compared by their application to the large statistical sets of human retina images of various healthy and glaucomatous subjects.