Forty-Sixth Annual Exhibition of the Institute of Physics and the Physical Society

Abstract

The frequency histogram of connected elements (FHCE) is a recently proposed algorithm that has successfully been applied in various medical image segmentation tasks. The FHCE is based on the idea that most pixels belong to the same class as their neighbouring pixels. However, the FHCE performance relies to a great extent on the optimal selection of a threshold parameter. Since evaluating segmentation results is a highly subjective process, a collection of threshold values must typically be examined. No algorithm has been proposed to automate the determination of the threshold parameter value of the FHCE. This study presents a method based on the fuzzy C-means clustering algorithm, designed to automatically generate optimal threshold values for the FHCE. This new approach was applied as a part of a structured sequence of image processing steps in order to facilitate segmentation of microcalcifications in digitized mammograms. A unique threshold value was generated for each mammogram, taking into account the different grey-level patterns based on different compositions of various breast tissues in it. The segmentation algorithm was tested on 100 mammograms (50 collected from the Mammographic Image Analysis Society and 50 normal mammograms onto which a number of simulated microcalcifications were generated). The algorithm was able to detect subtle microcalcifications with sensitivity ranging from 93 to 98%, False alarm ratio from 3 to 5% and false negatives variability from 2 to 3%.     

Contrast enhancement via texture region based histogram equalization

Abstract

This paper presents two novel contrast enhancement approaches using texture regions-based histogram equalization (HE). In HE-based contrast enhancement methods, the enhanced image often contains undesirable artefacts because an excessive number of pixels in the non-textured areas heavily bias the histogram. The novel idea presented in this paper is to suppress the impact of pixels in non-textured areas and to exploit texture features for the computation of histogram in the process of HE. The first algorithm named as Dominant Orientation-based Texture Histogram Equalization (DOTHE), constructs the histogram of the image using only those image patches having dominant orientation. DOTHE categories image patches into smooth, dominant or non-dominant orientation patches by using the image variance and singular value decomposition algorithm and utilizes only dominant orientation patches in the process of HE. The second method termed as Edge-based Texture Histogram Equalization, calculates significant edges in the image and constructs the histogram using the grey levels present in the neighbourhood of edges. The cumulative density function of the histogram formed from texture features is mapped on the entire dynamic range of the input image to produce the contrast-enhanced image. Subjective as well as objective performance assessment of proposed methods is conducted and compared with other existing HE methods. The performance assessment in terms of visual quality, contrast improvement index, entropy and measure of enhancement reveals that the proposed methods outperform the existing HE methods.     

基于最小类内差和最大类间差的图像分割算法研究

针对现有二维Otsu图像分割算法未考虑到目标和背景这二类像素自身的内聚性,提出一种新的自适应二维Otsu算法。该算法通过待分割图像的二维直方图,分别统计类内的绝对差、类间总体离差以反映类内、类间的离散度,从而构造出新阈值判别函数。通过一种改进的遗传算法优化二维阈值判别函数,自动得到较理想的分割阈值。实验结果表明,与其它阈值判别函数相比,通过优化新的阈值判别函数得到的二维阈值,具有了较好的分割效果,能够更好地保留了目标物的轮廓,而且计算量小。     

Contrast enhancement of computed tomography images by adaptive histogram equalization‐application for improved ischemic stroke detection

The visualization of computed tomography brain images is basically done by performing the window setting, which stretches an image from the Digital Imaging and Communications in Medicine format into the standard grayscale format. However, the standard window setting does not provide a good contrast to highlight the hypodense area for the detection of ischemic stroke. While the conventional histogram equalization and other proposed enhanced schemes insufficiently enhance the image contrast, they also may introduce unwanted artifacts on the so‐called “enhanced image.” In this article, a new adaptive method is proposed to excellently improve the image contrast without causing any unwanted defects. The method first decomposed an image into equal‐sized nonoverlapped sub‐blocks. After that, the distribution of the extreme levels in the histogram for a sub‐block is eliminated. The eliminated distribution pixels are then equally redistributed to the other grey levels with threshold limitation. Finally, the grey level reallocation function is defined. The bilinear interpolation is used to estimate the best value for each pixel in the images to remove the potential blocking effect. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 153–160, 2012     

Content-based image retrieval using color vector angle difference histogram

Abstract

This paper presents an innovative method to represent an image for image retrieval in Content-Based Image Retrieval (CBIR), using a color vector angle differences histogram approach (CVADH), which is different from the existing histograms, such as color histograms, gray level co-occurrences (GLCM), color distribution entropy (CDE), or just counting the frequency number of pixels or neighbors. In contrast to other methods, the CVADH takes account of the color difference and spatial information of two pixels in RGB color space. Furthermore, it directly leverages color clues to extract shape features to show differences given different backgrounds. Experimental results show that the scheme is more efficient than traditional ones that have been developed for CBIR.     

Segmentation of liver in ultrasonic images applying local optimal threshold method

Abstract

Low brightness contrast and grey level discontinuities of the ultrasonic liver image make it difficult to segment the object and the background and to extract the edges of the object using the global optimal threshold method. In this paper, we investigate a local optimal threshold method for the segmentation of ultrasound liver image. First of all, the distributed energy of the ultrasound liver image is estimated in the proposed liver segmentation. Then, the polynomials are fitted from the distributed energy data and a peak zone is defined from the minimum of the fitted polynomials. Finally, a few blocked images are divided from the number of the peak zones. Furthermore, multiple local optimal thresholds are obtained from the blocked images using Otsu’s method, and the ultrasonic liver image is segmented according to all local optimal thresholds. Experimental results validate the segmentation and edge detection of liver in the ultrasound images.     

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.     

一种基于FCM的图像分割方法

提出一种新的图像分割方法 FWFCM(fast walvet fuzzy C-means method),该方法对图像像素点的灰度进行模糊隶属度的分析,将需要聚类的像素空间投影到灰度直方图空间,从而减少了经典FCM算法的迭代计算量,提高了算法的收敛速度;并且利用小波变换的多分辨率的分析,抑制噪声点对图像分割的影响,提高了图像分割的精度.     

改进的自适应阈值Canny边缘检测

针对传统Canny边缘检测算法的阈值需要人为设定的缺陷,本文提出了一种新的自适应改进方法.该方法-根据梯度直方图信息,提出梯度差分直方图的概念,同时,对图像进行自适应分类处理,使得算法不仅不需人工设定阈值参数,而且还能有效地避免Canny算法在边缘寻找中的断边和虚假边缘现象.对边缘信息丰富程度不同的灰度图和彩色图像运用该方法寻找边缘的实验结果表明,对于在目标与背景交界处的多数像素梯度幅值较大的图片,该算法具有边缘检测能力强、自适应能力强的优点.     

A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters, thunderstorm cloud clusters are quickly and accurately identified. The experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.     

A chaotic image cryptosystem based on Toeplitz and Hankel matrices

Abstract

Because of properties in chaos system such as the sensitive dependence on initial conditions, system parameters, pseudorandom property and ergodicity, chaotic image encryption algorithm can suggest a new and efficient way of encryption scheme, which has been studied more and more in recent years. A novel chaotic image encryption algorithm based on Toeplitz matrix and Hankel matrix is proposed in this paper. We shuffle totally the positions of image pixels to confuse the relationship between the plain image and cipher image combined with Toeplitz matrix, Hankel matrix and logistic chaotic system. Another hyper-chaos system of Chen's chaotic system is taken to change the grey values of image pixels to enhance the security further. Experimental results in Sections 3 and 4 demonstrate that the key space is large enough and the key is sensitive to initial conditions to resist the brute force attack in the proposed algorithm. Additionally, the distribution of grey values in encrypted image has a random-like behaviour to resist statistical analysis.     

基于太赫兹图像的航天复合材料粘接缺陷检测方法研究

采用频率范围为0.23~0.32THz的反射式连续太赫兹波检测系统,对航天复合材料与基板的粘接缺陷进行了无损检测。采用环氧树脂作为粘接剂,制作了隔热复合材料与基板的粘接样件。使太赫兹波聚焦在粘接层处,获得层析图像,根据焦平面附近的图像,可直观判断出粘接面有无缺陷。结合中值滤波,采用模糊c均值(FCM)聚类算法对缺陷图像进行分割得到含有目标缺陷信息的二值图像,计算像素点得到缺陷面积。通过与已知缺陷面积样件对比,对粘接缺陷面积的预测误差在±9%以内。     

印刷网点微观图像阈值分割算法研究

目的 通过阈值处理方法, 准确获取网点微观图像的特征参数, 将其与仪器测量值相结合, 综合评价印刷品复制质量。方法 提出一种基于高斯函数模型拟合网点图像灰度直方图数据的阈值分割算法, 寻找网点类图像最佳分割阈值, 对图像进行二值化处理, 得到准确的网点参数。结果 得到的印刷品网点面积率在全阶调范围内更接近于测量值, 分割效果明显优于传统的阈值分割算法。结论 提出的高斯拟合阈值分割算法更有利于提取网点类图像的微观参数, 精度高, 稳定性好,为获取准确的网点图像微观参数提供了理论与实践参考。     

Automatic threshold technique for holographic particle field characterization

The 3D distribution of a particle field by digital holography is obtained by 3D numerical reconstruction of a 2D hologram. The proper identification of particles from the background during numerical reconstruction influences the overall effectiveness of the technique. The selection of a suitable threshold value to segment particles from the background of reconstructed images during 3D holographic reconstruction process is a critical issue, which influences the accuracy of particle size and number density of reconstructed particles. The object particle field parameters, such as depth of sample volume and density of object particles, influence the optimal threshold value. The present study proposes a novel technique for the determination of the optimal threshold value of a reconstructed image. The effectiveness of the proposed technique is demonstrated using both simulated and experimental data. The proposed technique is robust to variation in optical properties of particle and background, depth of sample volume, and number density of object particle field. The particle diameter obtained from the proposed threshold technique is within 5% of that obtained from the particle size analyzer. There is a maximum ten times increase in reconstruction effectiveness by using the proposed automatic threshold technique in comparison with the fixed manual threshold technique.     

基于脉冲耦合神经网络和遗传算法的图像增强

提出了基于脉冲耦合神经网络（PCNN-Pulse Coupled Neural Network）与遗传算法的图像增强算法．对PCNN模型进行了改进使之更适用于图像处理,并利用遗传算法的全局寻优能力自动搜索图像的最佳灰度阈值,再对图像进行处理．该算法既可有效地去除噪声,又能根据图像灰度性质自动选取最佳阈值,并对自适应分割后图像进行不同的灰度变换,从而实现了图像的自适应增强．试验结果表明,该算法显著提高了图像增强效果．     

基于灰阶熵的模糊对比度自适应图像增强算法

一般的数字图像存在噪声大、对比度低、边缘模糊等缺陷.为了有效地增强图像的模糊对比度,以满足后续的识别与检测要求,提出了一种基于灰阶熵的模糊对比度自适应图像增强算法.在模糊域内,根据邻域窗口灰阶熵值的大小,合理选取阈值,对阈值两侧的图像像素点进行不同程度的对比度增强处理,实现局部特征的增强.实验结果表明,该方法不仅增强了图像的整体对比度,而且有效地丰富了目标图像的细节信息,并抑制了噪声的放大.     

基于背景代表色提取的迷彩伪装颜色选取算法

为了达到好的伪装效果,迷彩颜色应与背景色调相融合,以使人眼及光学仪器难以探测和分辨.因此,背景主色的准确提取是确定迷彩颜色的前提.利用灰度直方图可以确定背景图像中的主要灰度,但无法区分不同色调.而基于颜色直方图的背景主色提取方法的运算量太大.本文提出了一种基于HSI模型和量化颜色直方图的迷彩颜色选取算法.利用HSI颜色模型描述背景颜色特性,通过特殊量化方式对背景的颜色直方图进行量化,接着借助阈值方法选取背景主色作为迷彩颜色.结合迷彩伪装图案设计方法对上述迷彩颜色选取算法进行了实验分析,并通过边缘检测和相关跟踪方法对不同背景下的目标迷彩伪装效果进行了验证.     

自适应红外图像直方图均衡增强算法

针对红外图像的特点,本文提出了一种自适应红外图像直方图均衡增强算法.该方法根据原始图像的直方图,自适应地构造出一个加权函数对原始图像的直方图进行加权处理,然后采用加权后的直方图对原始图像进行直方图均衡化.算法不需要人为指定阈值,而且克服了传统直方图均衡提升红外图像背景的缺点.实验结果表明,该算法对红外图像具有较好的增强效果,能够有效地抑制图像的背景,突出目标.     

Modelling of dynamical system and generalised circulant matrix in image encryption

Abstract

In this paper, a modelled image encryption scheme is presented, of which generalised circulant matrix is employed together with dynamical chaotic system. The strong correlations among adjacent pixels in plain-image can be declined greatly by the proposed scheme. Meanwhile, the diffusion transform is considered simultaneously to avoid statistical analysis, known plaintext and chosen plaintext attack. The experimental results show that the proposed scheme can suggest an efficient way to hide the image information, and has the advantages of large key space, sensitive to initial conditions and zero correlation between adjacent pixels in cipher-image. Moreover, the grey values in the cipher-image are distributed symmetrically. The proposed image scheme can be applied to practical image information transmission and protection in the public.     

A CMOS Image Sensor With In-Pixel ADC, Timestamp, and Sparse Readout

Recent developments in CMOS image sensors have focused on the advancement of digital pixel sensors (DPS). A novel DPS aiming to combine multiple functionalities on the imaging plane has been designed and tested: The on pixel intelligent CMOS (OPIC) sensor was manufactured in a 0.25 $mu$m logic process with 5 metal layers, and 8 $mu$m epitaxial layers. The sensor comprises three 2 $,times,$2 mm test arrays of 30 $mu$m pixels. Two of the test arrays are named “advanced” and include two 8-bit DRAM cells, one 8-bit ROM cell, and one 1-bit DRAM cell per pixel. The 8-bit DRAM cells can record both ADC and “time-to-threshold” data, while the ROM hard codes the pixel address within the array. The 1-bit DRAM acts as a “hit flag” enabling automatic sparsification of the image data based on an external threshold.