基于神经网络中的NNTool对小鼠皮肤的识别方法

研究皮肤纹理经过UVB光照射后的变化情况并对其进行识别。具体地,采取图像纹理分析方法,对经过光照射后不同时期的小鼠皮肤图像提取纹理特征,进而建立一种新的皮肤纹理识别模型。采用空间灰度共生矩阵法提取图像纹理的4个主要特征,即:能量,熵,惯性矩,相关度,然后利用神经网络中的NNtool对皮肤纹理图像进行训练和分类识别。实验结果很好地证明了这种纹理分析和识别方法的可行性和有效性。     

人体皮肤的双光子图像纹理特征提取方法

近年来通过计算机技术对瘢痕实现无损诊断的研究进展迅速,其对瘢痕图像纹理特征的量化分析得到了很好的诊断效果。在这个过程中出现了很多纹理描述方法,这些方法的提出也促进了纹理研究的发展。本文在对灰度共生矩阵(GLCM)、局部三值模式(LTP)等统计纹理分析方法进行介绍的情况下,在瘢痕图像上利用梯度迭代回归树算法给出了不同方法的实验结果,得到了不同方法的回归模型。这些模型的性能体现在对不同年龄瘢痕的预测能力,其中局部差异局部二值模式(LD-LBP)和局部方向三值模式(LOTP)得到的模型预测能力最好,说明它们是目前对瘢痕图像纹理描述比较准确的方法之一,同时表明统计纹理分析方法适合用于瘢痕图像的纹理研究。     

基于计算机视觉的棉铃虫成虫雌雄自动判别研究

棉铃虫Helicoverpa armigera(Hübner)是棉花作物的主要寄主害虫,其性别的自动准确判别对区域性比、种群数量等方面的预测预报具有重要意义。本文通过CCD设备获取雌雄成虫的原始彩色图像,运用数学形态学和自适应图像增强法进行滤波分析,提高害虫RGB彩色图像及分通道图像的质量。针对RGB彩色图像提取害虫的颜色矩特征,针对B通道灰度图像提取基于灰度共生和差分统计矩阵的纹理、形态不变矩等特征,对提取的36个特征数据进行归一化处理。将惩罚因子和RBF核函数参数作为SVM分类器识别率的重要判断标准,利用K折交叉验证选取最优参数组合并建立模型,当C=4,g=0.0825,识别率达到最佳98.33%。证实了将计算机视觉应用于昆虫性别自动判别的可行性。     

使用图像特征构建快速有效的蛋白质折叠识别方法

蛋白质结构自动分类是探索蛋白质结构- 功能关系的一种重要研究手段。首先将蛋白质折叠子三维空间结构映射成为二维距离矩阵,并将距离矩阵视作灰度图像。然后基于灰度直方图和灰度共生矩阵提出了一种计算简单的折叠子结构特征提取方法,得到了低维且能够反映折叠结构特点的特征,并进一步阐明了直方图中零灰度孤峰形成原因,深入分析了共生矩阵特征中灰度分布、不同角度和像素距离对应的结构意义。最后应用于27类折叠子分类,对独立集测试的精度达到了71.95 %,对所有数据进行10 交叉验证的精度为78.94 %。与多个基于序列和结构的折叠识别方法的对比结果表明,此方法不仅具有低维和简洁的特征,而且无需复杂的分类系统,能够有效和高效地实现多类折叠子识别。     

老化皮肤的纹理分析

本文利用多光子显微镜从亚微米尺度下研究皮肤老化过程的纹理特征。通过多光子显微镜在体获得年轻皮肤及自然老化皮肤的表皮纹理及形态特征。对表皮纹理和形态的图片分别进行纹理网格分析及分形分析,结果显示两种分析法具有高度的相似性及相关性。本文研究显示以皮肤纹理网格数和皮肤分形维数为特征参量可有效评价皮肤老化的纹理特征。     

基于影像纹理特征的川西南山地常绿阔叶林有效叶面积指数的空间分析

遥感是获取叶面积指数（LAI）信息的最有吸引力的选择之一,但目前基于遥感数据的叶面积指数估测精度有限.本文以川西南山地常绿阔叶林为研究对象,基于地面调查的83个20 m×20 m样地和SPOT5数据,运用灰度共生矩阵法提取影像单波段、简单波段比图和主成分图的纹理信息,以不同图像处理方式的纹理参数作为辅助变量进行地统计分析估算有效LAI（LAIe）.结果表明： LAIe与不同方式处理图像的纹理参数存在不同程度的相关性,其中,与B₁波段、B₁/B₄和PC₁的均质性呈极显著相关关系.与以归一化植被指数（NDVI）为辅助变量相比,以纹理参数B₁波段、B₁/B₄和PC₁的均质性作为辅助变量估测LAIe的精度均有所提高,分别提高5.3%、11.0%、14.5%,还能在一定程度上降低统计误差.以NDVI、PC₁均质性作为辅助变量的LAIe空间地统计估测模型最优（R²＝0.840,RMSE=0.212）.本研究结果为合理地选择除植被指数外的其他辅助变量估测区域LAI的空间分布提供了一种新的思路和方法.     

基于频谱图像灰度共生矩阵的无损测温方法

本文提出了一种基于哈达玛变换的频谱图像灰度共生矩阵(Hadamard-GLCM)的高强度聚焦超声治疗无损测温方法。利用高强度聚焦超声辐照新鲜离体猪肉组织,获取辐照前后的B超图像的减影图像,采用Hadamard变换对其进行处理,获取频谱图像,将频谱图像的灰度共生矩阵惯性矩作为反应温度变化的信息参数。实验表明:不仅单组数据的Hadamard-GLCM惯性矩(HGMI)和温度能很好的线性拟合,而且多组数据的Hadamard-GLCM惯性矩与温度也成近似的线性关系,而且斜率非常接近,拟合度更接近1,误差小,对温度的分辨能力高,容错能力强,与传统的测温方法相比有着明显的优势,能为HIFU治疗过程中的无损测温提供有效的实时依据。     

基于启发式A^＊算法的超声图像颈动脉内膜提取

从超声图像准确提取颈动脉内膜,为基于颈动脉超声图像判断动脉粥样硬化服务。方法提出一种基于启发式A*算法从超声图像中提取颈动脉内膜边缘的方法。先使用图像分割法区分血管腔和血管壁,再采用结合图像灰度值特点的A*算法准确地提取颈动脉内膜边缘。结果通过对临床采集的32幅颈动脉超声图像的分析研究,表明本方法自动提取的结果与医生手工描绘的结果基本吻合。结论本方法有望应用于超声图像颈动脉内膜的自动提取。     

细胞核内特征值的改变可引起DNA倍体的变化

目的通过测定不同DNA倍体细胞,研究细胞核内特征值的改变。方法用宫颈刷刷出宫颈细胞,经固定后,用涂片离心机制成二张玻片,一张行巴氏染色作TBS诊断,另一张行Feulgen染色做DNA定量测定。通过对宫颈细胞核图像内像素的统计,计算出细胞核内多种特征值,比较不同DNA倍体细胞内特征值的不同。结果 161873例妇女行宫颈细胞学检查,常规细胞学检查发现2454例低级别鳞状上皮内病变(low-grade squamous intraepithelial lesion,LSIL)和523例高级别鳞状上皮内病变(high-grade squamous intraepithelial lesion,HSIL);而DNA倍体分析发现3412例有3个以上>5c细胞。84%以上的LSIL和HSIL病例均可见倍体异常细胞。与2c细胞相比,4c、5c、7c及9c细胞核面积及核半径明显增大;7c、9c细胞核内平均光学密度和紧实度均值也有明显改变,而光密度方差和灰度熵无变化。结论宫颈细胞DNA倍体改变往往伴有细胞形态和DNA核内分布等特征值的改变。     

Classification of leaf epidermis microphotographs using texture features

We present the results of a Gray Level Co-occurrence Matrix (GLCM) analysis for two sets of leaf epidermis images for the adaxial (20×_H) and abaxial sides (20×_E). The leaves were collected from a dry forest in Mona Island which is located between the Dominican Republic and Puerto Rico. For each set of images (GLCM) texture features were calculated namely the energy, correlation, contrast, absolute value, inverse difference, homogeneity, and entropy. From the calculated statistics a features matrix was obtained for each image and randomly divided into training set and test set using the hold-out method. In this method 70% of the images were considered as a training set and 30% as the test set. For each training and test set a linear discrimination analysis (LDA) was performed resulting in a average correct classification percent of 90% for the abaxial side in comparison with 80% for the adaxial side.     

Prognostic classification of early ovarian cancer based on very low dimensionality adaptive texture feature vectors from cell nuclei from monolayers and histological sections.

In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images.The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images.     

Texture of Bananas Submitted to Different Freeze Drying Cycle Applying Scanning Electron Microsocopy with Image Analysis Techniques

Prediction of texture in bananas submitted to different freeze drying cycle was investigated applying scanning electron microscopy combined with image analysis technique. Freeze drying was performed at different cycles. Microstructure was analyzed using a Scanning Electron Microscopy; Texture parameters were analyzed by Gray Level Co-Matrix Analysis and by conventional analysis; colour by image analysis and porosity by conventional technique. Micrographs revealed that a higher porous size structure was obtained when freeze drying cycles was performed at shorter cycles. Significant difference (P < 0.0001) were obtained for texture, senescence and porosity. A linear trend with a linear correlation was applied for instrumental vs. image texture. Results showed that image features (contrast, correlation, entropy, energy and homogeneity) correlated with mechanical texture. When short cycles were applied minimum damage on texture and senescence parameters appeared. Prediction of texture can be performed easily as a quantitative and non invasive technique that could be related in future studies for quality.

     

Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm

Marketability of agricultural products depends heavily on appearance attributes such as color, size, and ripeness. Sorting plays an important role in increasing marketability by separating crop classes according to appearance attributes, thus reducing waste. As an expert technique, image processing and artificial intelligence (AI) techniques have been applied to classify hawthorns based on maturity levels (unripe, ripe, and overripe). A total of 600 hawthorns were categorized by an expert and the images were taken by an imaging box. The geometric properties, color and, texture features were extracted from segmented hawthorns using the Gray Level Co-occurrence Matrix (GLCM) and evaluation of various color spaces. The efficient feature vector was created by QDA feature reduction method and then classified using two classical machine learning algorithms: Artificial Neural Network (ANN) and Support Vector Machine (SVM). The obtained results indicated that the efficient feature-based ANN model with the configuration of 14–10-3 resulted in the accuracy of 99.57, 99.16, and 98.16% and the least means square error (MSE) of 1 × 10⁻³, 8 × 10⁻³, and 3 × 10⁻³ for training, validation and test phases, respectively. The machine vision system combined with the machine learning algorithms can successfully classify hawthorns according to their maturity levels.     

Detection of Mild Cognitive Impairment Using Kernel Density Estimation Based Texture Analysis of the Corpus Callosum in Brain MR Images

ObjectivesMild Cognitive Impairment (MCI) is the prodromal stage of Alzheimer's disease (AD), which is a progressive and fatal neurodegenerative disorder. Detection of MCI condition can enable early diagnosis resulting in timely intervention to delay the disease progression. Onset of MCI causes tissue alterations in Corpus Callosum (CC) of the brain. Texture analysis of brain Magnetic Resonance (MR) images aids in characterising these imperceptible changes. In this study, Kernel Density Estimation (KDE) technique is used to analyse the textural variations in CC to detect MCI condition.Materials and methodThe pre-processed brain MR images are obtained from a public access database. Reaction Diffusion level set is employed to segment CC from sagittal slices of the images. Kernel density estimation method is applied to study the local intensity variations within the segmented CC. Statistical features quantifying these variations are extracted from the KDE values. These features are used to differentiate MCI condition using linear classifiers based on discriminant analysis and support vector machine. The results are compared with conventional Grey Level Co-occurrence Matrix (GLCM) features for validation.ResultsThe KDE-based texture features extracted from CC show significant variation between normal and MCI classes. Results demonstrate that this approach can differentiate MCI condition with high accuracy and specificity of 81.3% and 82.7%, respectively. The KDE-based features perform better when compared with GLCM features for distinguishing MCI.ConclusionsThe KDE-based texture features are able to capture the subtle changes occurring in CC at the MCI stage. This technique achieves comparable performance to other state-of-the-art methods with reduced number of features. Efficiency of the KDE-based texture analysis confirms that the proposed computer assisted technique can be used for mass screening of MCI, which can aid in handling the disease severity.     

基于Gabor滤波器的皮肤纹理图像增强

在疗效化妆品中,常常需要对护肤品的性能和效果进行分析。皮肤纹理的检测是客观衡量疗效化妆品的有效手段。基于计算机视觉技术的皮肤纹理分析,对拍摄的皮肤图像要进行图像预处理,增强图像,为后续的分析提供有效的数据。采用经过微调的定向的Gabor滤波器进行增强图像,通过实验得出Gabor滤波器不仅抑制噪声的效果好,还保留了皮肤图像的整体和局部特征。     

Citrus orchard mapping in Juybar,Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries

Nowadays crop mapping as an interdisciplinary hot topic attracted both agriculture and remote sensing researchers' interests. This study proposed an automatic method to map citrus orchards in Juybar, Iran, where planting citrus trees is booming there. In this regard, 148 Sentinel-1, Sentinel-2, and ALOS Digital Surface Model (DSM) tiles are processed in Google Earth Engine to provide a hybrid feature set including initial satellite images, Gray Level Co-occurrence Matrix (GLCM) textural features, and spectral features such as vegetation, built-up, bare-soil indices, and the proposed Vegetation Dynamic Index (VDI). A semi-automatic sample selection paradigm is also developed based on a time-series analysis of 12 monthly Normalized Difference Vegetation Indices (NDVIs), Otsu thresholding, multi-level thresholding (MLT), and using two proposed indices called Evergreenness Index (EGI) and Water-covered or No-vegetation (WCNV) index, and finally human post-revision. The output of the Support Vector Machine (SVM) classification using 60,000 samples and the post-classification operation showed that the classified map has an average overall accuracy (OA) and an average kappa coefficient (KC) equal to 99.7% and 0.992, respectively. The results show that multispectral bands lonely extracted orchards with high accuracy (OA: 99.55%, KC: 0.986), and the rest of the features only made a slight improvement to that. For the year 2019, an area of about 4351 ha is estimated as citrus orchards, which is in accordance with the agriculture department's reports (~4700 ha). The results indicate that from 2016 to 2019, despite a “citrus to non-citrus” land-use conversion of about 754 ha, the citrus orchards area was totally expanded by about 17%. Comparing the results with the Google Earth images indicates the precise extraction of orchards with a 10 m spatial resolution. To use the proposed method for extensive cases or areas with other types of evergreen trees, it is recommended to use high-resolution normalized DSMs (nDSMs) and textural features.     

Optimization assisted framework for thyroid detection and classification: A new ensemble technique

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like “(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification”. Initially, the thyroid images as well as its related data are given as input. From the input image, the features such as“ Grey Level Co-occurrence Matrix(GLCM), Grey level Run Length Matrix(GLRM), proposed Local Binary Pattern(LBP), and Local Tetra Patterns (LTrP)” are extracted. Meanwhile, from the input data, the higher-order statistical features like skewness, kurtosis, entropy, as well as moment get retrieved. Consequently, the Linear Discriminant Analysis (LDA) based dimensionality reduction is processed to resolve the problem of “curse of dimensionality”. Finally, the classification is carried out via two phases: Image features are classified using an ensemble classifier that includes Support Vector Machine (SVM)& Neural Network(NN) models. The data features are subjected to Recurrent Neural Network(RNN) based classification, which is optimized by an Adaptive Elephant Herding Algorithm (AEHO) via tuning the optimal weight. At last, the performance of the adopted scheme is compared to the extant models in terms of various measures. Especially, the mean value of the suggested RNN + AEHO model is 4.35%, 3.54%, 6.07%, 3.8%, 1.69%, 2.85%, 2.07%, 2.54%, 0.13%, 0.035%, and 8.53% better than the existing CNN, NB, RF, KNN, Levenberg, RNN + EHO, RNN + FF, RNN + WOA, WF-CS, FU-SLnO and HFBO methods respectively.     

Textural fingerprints: a comprehensive descriptor for biofilm structure development

Automated tools to determine biofilm structure are necessary to interpret large time series of biofilm images. Image analysis based on the evaluation of Spatial Gray Level Dependence Matrices (SGLDM) enabled us to monitor biofilm structure development in response to external disturbances (i.e., periodic increases of wall shear stress) at a large scale (i.e., >1 mm). We applied our method to an experiment conducted in an annular reactor over a 10-week period. Six states of biofilm development were differentiated by their unique structure. Previous exposure to rapidly increased shear influenced the resulting biofilm structure after additional shear increases. In addition, on the scale of the biofilm images, the biofilm structure after a shear increase was spatially heterogeneous and resulted in spatially differentiated regrowth after detachment at different locations in the biofilm. SGLDM was developed further as an alternative to approaches based on image binarization as binarization leads to information loss for low-magnification and low-resolution images. During post-processing of image data, structural states of biofilm development were identified by K-means clustering and data display in Principal Component plots. Quantitatively selected representative images were used to illustrate the meaning of the clusters. Post-treatment of image data was essential for managing several thousands of raw biofilm images and therefore improved the usefulness of the image analysis.     

Differential response of embryonic cells to culture on tissue matrices.

Cell responses to different natural substrates have been followed by scanning microscopy in order to evaluate the role of these substrates in morphogenesis. Matrix has been isolated then repopulated with suspensions of embryonic cells from chick skin, spinal ganglia, duodenal epithelium and heart. In some cases outgrowth from amphibian embryonic tissue was used. Basal lamina of the Xenopus tail may be exposed by freezing and thawing the tissue, or by EDTA treatment. The underlying lamella of orthogonally oriented collagen fibers may be exposed by use of trypsin or hyaluronidase. Trypsin causes more clumping of collagen fibers and a coarser texture of the matrix. On trypsin isolated basement lamella, nerve cell processes grow out on the surface and show no strong tendency to penetrate the lamella while skin mesenchymal cells commonly burrow among the collagen plies. Epithelial cells remain on the surface. On the basal lamina mesenchymal cells ruffle in early stages of culture, then flatten. Epithelial cells flatten rapidly on the lamina. These differences in cell response are in some cases closely related to cell behavior in vivo and suggest that cells show a selective response to the chemical composition of the substrate as well as to its physical conformation.