Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

Automatic Identification of Butterfly Species Based on Gray-Level Co-occurrence Matrix Features of Image Block

In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.

Identification of Textile Defects Based on GLCM and Neural Networks

In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Based on the co-occurrence matrix and its statistical features, as an approach for defects textile identification in the digital image, TAI can potentially provide an objective and reliable evaluation on the fabric production quality. The goal of most TAI systems is to detect the presence of faults in textiles and accurately locate the position of the defects. The motivation behind the fabric defects identification is to enable an on-line quality control of the weaving process. In this paper, we proposed a method based on texture analysis and neural networks to identify the textile defects. A feature extractor is designed based on Gray Level Co-occurrence Matrix (GLCM). A neural network is used as a classifier to identify the textile defects. The numerical simulation showed that the error recognition rates were 100% for the training and 100%, 91% for the best and worst testing respectively.

Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma

BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC experience postoperative pulmonary infections.Thus,it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery.Radiomics data were selected for statistical analysis,and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables.We then developed a pulmonary infection prediction model using three different models:An artificial neural network model;a random forest model;and a generalized linear regression model.Finally,we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.RESULTS Among the 505 patients,86 developed a postoperative pulmonary infection,resulting in an incidence rate of 17.03%.Based on the gray-level co-occurrence matrix,we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models.Among these,energy,contrast,the sum of squares(SOS),the inverse difference(IND),mean sum(MES),sum variance(SUV),sum entropy(SUE),and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models.The random forest model algorithm,in combination with IND,SOS,MES,SUE,SUV,and entropy,demonstrated the highest prediction efficiency in both the training and internal verification sets,with areas under the curve of 0.823 and 0.801 and a 95%confidence interval of 0.766-0.880 and 0.744-0.858,respectively.The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95%confidence intervals of 0.677-0.791 and 0.766-0.864,respectively.CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND,SOS,MES,SUE,SUV,energy,and entropy.The prediction model in this study based on diffusion-weighted images,especially the random forest model algorithm,can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy,providing valuable guidance for postoperative management.

洪泽湖湿地纹理特征参数分析

应用纹理特征进行影像分类,关键在于纹理特征参数的确定。以洪泽湖湿地典型地区为研究对象,选择灰度共生矩阵进行纹理特征计算,探讨灰度共生矩阵窗口尺寸、移动步长、方向和纹理特征统计量对淡水湖泊湿地的区分能力;然后,利用纹理特征和地物光谱特征,结合决策树方法对研究区湿地及其他主要地类进行分类,并通过混淆矩阵进行精度评价。结果表明:研究区湿地分类中纹理特征的最佳窗口大小为3像元×3像元,方向为90°,步长为1个像元,纹理特征统计量组合为均值、熵和相关度;分类精度为83.24%,Kappa为0.788,其结果验证了纹理特征参数选择的科学性和合理性。

基于多特征的金属断口图像疲劳条带分割

疲劳条带是疲劳断口典型的微观特征,分割是对金属断口图像进行定量分析以反推疲劳寿命和疲劳应力的重要环节。由于断裂过程中的复杂性使得实际断口多表现为多样性的混合形态,且不同区域的疲劳条带周期差别很大,使得疲劳条带纹理区域和纹理边缘的准确定位成为分割的一大难点。传统单一纹理特征对这类复杂的自然纹理分割准确性低。通过分析断口的自然纹理特性,提出结合灰度共生矩阵和小波包变换,采用多特征对断口图像的疲劳条带进行准确分割,从而发挥了时域和频域两类特征的双重优势。实验结果表明,改进的多特征方法对疲劳条带自动分割精度优于传统方法。

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

The size,shape,and physical characteristics of the human skull are distinct when considering individual humans.In physical anthropology,the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner.For example,labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections.Given the multiple issues associated with the manual identification of skulls,we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features,Gabor features,fractal features,discrete wavelet transforms,and combinations of features.Each underlying facial bone exhibits unique characteristics essential to the face’s physical structure that could be exploited for identification.Therefore,we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches.Using our proposed approach,we were able to achieve an accuracy of 92.3–99.5%in the classification of human skulls with mandibles and an accuracy of 91.4–99.9%in the classification of human skills without mandibles.Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.

Detection of fabric defects based on frequency-tuned salient algorithm

The correct rate of detection for fabric defect is affected by low contrast of images. Aiming at the problem,frequencytuned salient map is used to detect the fabric defect. Firstly,the images of fabric defect are divided into blocks. Then,the blocks are highlighted by frequency-tuned salient algorithm. Simultaneously,gray-level co-occurrence matrix is used to extract the characteristic value of each rectangular patch. Finally,PNN is used to detect the defect on the fabric image. The performance of proposed algorithm is estimated off-line by two sets of fabric defect images. The theoretical argument is supported by experimental results.

Study of Texture Segmentation and Classification for Grading Small Hepatocellular Carcinoma Based on CT Images

To grade Small Hepatocellular Car Cinoma(SHCC)using texture analysis of CT images,we retrospectively analysed 68 cases of Grade II(medium-differentiation)and 37 cases of Grades III and IV(high-differentiation).The grading scheme follows 4 stages:(1)training a Super Resolution Generative Adversarial Network(SRGAN)migration learning model on the Lung Nodule Analysis 2016 Dataset,and employing this model to reconstruct Super Resolution Images of the SHCC Dataset(SR-SHCC)images;(2)designing a texture clustering method based on Gray-Level Co-occurrence Matrix(GLCM)to segment tumour regions,which are Regions Of Interest(ROIs),from the original and SR-SHCC images,respectively;(3)extracting texture features on the ROIs;(4)performing statistical analysis and classifications.The segmentation achieved accuracies of 0.9049 and 0.8590 in the original SHCC images and the SR-SHCC images,respectively.The classification achived an accuracy of 0.838 and an Area Under the ROC Curve(AUC)of 0.84.The grading scheme can effectively reduce poor impacts on the texture analysis of SHCC ROIs.It may play a guiding role for physicians in early diagnoses of medium-differentiation and high-differentiation in SHCC.

Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.

Vibration-based hypervelocity impact identification and localization

Hypervelocity impact(HVI)vibration source identification and localization have found wide applications in many fields,such as manned spacecraft protection and machine tool collision damage detection and localization.In this paper,we study the synchrosqueezed transform(SST)algorithm and the texture color distribution(TCD)based HVI source identification and localization using impact images.The extracted SST and TCD image features are fused for HVI image representation.To achieve more accurate detection and localization,the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features.Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization.To demonstrate the effectiveness of the proposed algorithm,the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation.The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms.Finally,based on sensor distribution,an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.