Liver fibrosis staging using CT image texture analysis and soft computing

Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws’ method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws’ texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons.     

Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors

This study aimed to evaluate the value of using 3-D breast MRI morphologic features to differentiate benign and malignant breast lesions. The 3-D morphological features extracted from breast MRI were used to analyze the malignant likelihood of tumor from ninety-five solid breast masses (44 benign and 51 malignant) of 82 patients. Each mass-like lesion was examined with regards to three categories of morphologic features, including texture-based gray-level co-occurrence matrix (GLCM) feature, shape, and ellipsoid fitting features. For obtaining a robust combination of features from different categories, the biserial correlation coefficient (|r_pb|) ≧ 0.4 was used as the feature selection criterion. Receiver operating characteristic (ROC) curve was used to evaluate performance and Student's t-test to verify the classification accuracy. The combination of the selected 3-D morphological features, including conventional compactness, radius, spiculation, surface ratio, volume covering ratio, number of inside angular regions, sum of number of inside and outside angular regions, showed an accuracy of 88.42% (84/95), sensitivity of 88.24% (45/51), and specificity of 88.64% (39/44), respectively. The A_Z value was 0.8926 for these seven combined morphological features. In conclusion, 3-D MR morphological features specified by GLCM, tumor shape and ellipsoid fitting were useful for differentiating benign and malignant breast masses.     

基于SVM的图像纹理特征分类研究

支持向量机（SVM）是一种表现卓越的分类方法,而灰度共生矩阵（GLCM）则是一种很好的纹理分析方法,故而本文提出了一种使用灰度共生矩阵进行特征提取的应用支持向量机的纹理特征分类法。实验结果表明,与直接应用灰度信息进行分类的支持向量机算法相比,本文方法可以取得更为准确的分类结果。     

Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale,texture and image objects

This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single- and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single- and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses.     

Multi-cell nuclei segmentation in cervical cancer images by integrated feature vectors

Automated analysis of cervical cancer images is considered as an attractive research in the biological fields. Due to the intensive advances in the digital technology and the light microscopy, the cellular imaging requires the continuous growing importance. Sophisticated methods are adopted to isolate the nuclei from the cytoplasm based on the boundary estimation and the analysis of intensity of blue cells to improve the abnormality prediction. The cell-based segmentation evolved in research studies assures the automatic assistance with an assumption of a single cell. Previous work [21] concentrates on the segmentation of abnormal region on single-cell images. This paper extends that work into the multi-cell images with the addition of geometrical features. The complex cell structure, poor contrast, and overlapping affect the cell segmentation performance. This paper enhances the performance of single-cell segmentation with the integrated feature vectors of geometrical (area, cell size, cell intensity and the maximum intensity) and Gray level Co-occurrence Matrix (GLCM) to improve the abnormality level prediction.. Initially, the Neighborhood Concentric Filtering (NCF) is applied on the input slides to remove the noise present in the image and enhance the intensity level. Then, the initial level cluster formation and masking are performed on the noise-free image. The Optimal Weight Updating with the Multi-Level set (OWU-ML) estimates the Region of Interest (ROI) and segments the blue cell and cytoplasm. The clear analysis of blue cell indicates the exact classification of abnormal levels in the images. The combination of geometrical and GLCM extracts the texture pattern features of the blue cell, cytoplasm and the nucleus portions in the form of angle variations. Finally, the Neural Network-based RVM classifier predicts the classes of (normal and abnormal) cervical images. The integration of novel methods such as OWU-ML segmentation, GLCM + geometrical feature extraction and NN-RVM classification improves the abnormal prediction performance and assures the suitability in multi-cell cervical image handling in biological applications.     

Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city,Uttar Pradesh

Investigations have been carried out for digital spectral and textural classification of an Indian urban environment using SPOT images with grey level co-occurrence matrix (GLCM), grey level difference histogram (GLDH), and sum and difference histogram (SADH) approaches. The results indicate that a combination of texture and spectral features significantly improves the classification accuracy compared with classification with pure spectral features only. This improvement is about 9% and 17% for an addition of one and two texture features, respectively. GLDH and SADH give statistically similar results to GLCM, and take less computing time than GLCM. Conventional separability measures like transformed divergence, Bhattacharya distance, etc. are not effective in feature selection when classification is carried out with spectral and texture features. An alternative approach using simple statistics such as average coefficient of variation, skewness, and kurtosis and correlation amongst feature sets has shown greater feature selection potential when a combination of spectral and texture features is used.     

Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis

The long-time historical evolution and recent rapid development of Beijing, China, present before us a unique urban structure. A 10-metre spatial resolution SPOT panchromatic image of Beijing has been studied to capture the spatial patterns of the city. Supervised image classifications were performed using statistical and structural texture features produced from the image. Textural features, including eight texture features from the Grey-Level Co-occurrence Matrix (GLCM) method; a computationally efficient texture feature, the Number of Different Grey-levels (NDG); and a structural texture feature, Edge Density (ED), were evaluated. It was found that generally single texture features performed poorly. Classification accuracy increased with increasing number of texture features until three or four texture features were combined. The more texture features in the combination, the smaller difference between different combinations. The results also show that a lower number of texture features were needed for more homogeneous areas. NDG and ED combined with GLCM texture features produced similar results as the same number of GLCM texture features. Two classification schemes were adopted, stratified classification and non-stratified classification. The best stratified classification result was better than the best non-stratified classification result.     

Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution

An abdominal aortic aneurysm (AAA) is a localized abnormal enlargement of the abdominal aorta with fatal consequences if not treated on time. The endovascular aneurysm repair (EVAR) is a minimal invasive therapy that reduces recovery times and improves survival rates in AAA cases. Nevertheless, post-operation difficulties can appear influencing the evolution of treatment. The objective of this work is to develop a pilot computer-supported diagnosis system for an automated characterization of EVAR progression from CTA images. The system is based on the extraction of texture features from post-EVAR thrombus aneurysm samples and on posterior classification. Three conventional texture-analysis methods, namely the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), the gray level difference method (GLDM), and a new method proposed by the authors, the run length matrix of local co-occurrence matrices (RLMLCM), were applied to each sample. Several classification schemes were experimentally evaluated. The ensembles of a k-nearest neighbor (k-NN), a multilayer perceptron neural network (MLP-NN), and a support vector machine (SVM) classifier fed with a reduced version of texture features resulted in a better performance (A_z = 94.35 ± 0.30), as compared to the classification performance of the other alternatives.     

基于SVM的SAR图像分类研究

支持向量机（SVM）是一种卓越的分类方法,灰度共生矩阵（GLCM）则是一种很好的纹理分析方法,而纹理是合成孔径雷达（SAR）图像分类的一个重要特征,故而提出了一种使用灰度共生矩阵进行特征提取的应用支持向量的SAR图像分类法。实验结果证明了支持向量机算法的可行性和有效性。     

Extracting multi-features and optimizing feature space with sparse auto-encoder over WorldView-2 images

Feature selection of very high-resolution (VHR) images is a key prerequisite for supervised classification. However, it is always difficult to acquire the features which have the highest correlation to the type of land cover for improving classification accuracy. To address this problem, this paper proposed a methodology of feature selection using the results of multiple segmentation via genetic algorithm (GA) and correlation feature selection (CFS) integrating sparse auto-encoder (SAE). Firstly, 61 features, including spectral features and spatial features, are extracted from the results of multi-scale segmentation over a WorldView-2 image in Xicheng District, Beijing. Then, 40-dimensional features and 30-dimensional features are derived from the selection with GA+CFS and the optimization with SAE, respectively. Thirdly, the final classification is achieved by logistic regression (LR) based on different subsets of features extracted from the WorldView-2 image. It is found that the result of feature selection could contribute to increase in the intra-species separation and reduction in the inner-species variability. Adding extra lower-ranked features appeared to reduce the accuracy of classification. The results indicate that the overall classification accuracy with 30-dimensional features reached 87.56%, and increased 5.61% compared to the results with 61-dimensional features. For the two kinds of optimized features, the Z-test values are all greater than 1.96, which implied that feature dimensionality reduction and feature space optimization could significantly improve the accuracy of image land cover classification. The texture features in the wavelet domain are the most important features for the study area in the WorldView-2 image classification. Adding wavelet and the grey-level co-occurrence matrix (GLCM) information, especially for GLCM features in wavelet, appeared not to improve classification accuracy. The SAE-based method can produce feature subsets for improving mapping accuracy more efficiently.     

Image mining by spectral features: A case study of scenery image classification

Spectral features of images, such as Gabor filters and wavelet transform can be used for texture image classification. That is, a classifier is trained based on some labeled texture features as the training set to classify unlabeled texture features of images into some pre-defined classes. The aim of this paper is twofold. First, it investigates the classification performance of using Gabor filters, wavelet transform, and their combination respectively, as the texture feature representation of scenery images (such as mountain, castle, etc.). A k-nearest neighbor (k-NN) classifier and support vector machine (SVM) are also compared. Second, three k-NN classifiers and three SVMs are combined respectively, in which each of the combined three classifiers uses one of the above three texture feature representations respectively, to see whether combining multiple classifiers can outperform the single classifier in terms of scenery image classification. The result shows that a single SVM using Gabor filters provides the highest classification accuracy than the other two spectral features and the combined three k-NN classifiers and three SVMs.     

基于综合特征的纹理分割方法

针对灰度共生矩阵只能在单一尺度对纹理进行分析的不足,结合离散框架小波变换产生的尺度共生矩阵与梯度变换图像的灰度共生矩阵,提出了一种具有多尺度分析特性的综合纹理特征提取算法,并利用该特征对纹理图像进行分割.仿真实验结果表明:与基于单一尺度特征的纹理分割方法相比,本文提出的算法能够提高纹理边界定位准确性,减少区域内像素错分,取得了较好的分割效果.     

Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.     

Supervised Vessels Classification Based on Feature Selection

Arterial-venous classification of retinal blood vessels is important for the automatic detection of cardiovascular diseases such as hypertensive retinopathy and stroke. In this paper, we propose an arterial-venous classification (AVC) method, which focuses on feature extraction and selection from vessel centerline pixels. The vessel centerline is extracted after the preprocessing of vessel segmentation and optic disc (OD) localization. Then, a region of interest (ROI) is extracted around OD, and the most efficient features of each centerline pixel in ROI are selected from the local features, grey-level co-occurrence matrix (GLCM) features, and an adaptive local binary patten (A-LBP) feature by using a max-relevance and min-redundancy (mRMR) scheme. Finally, a feature-weighted K-nearest neighbor (FW-KNN) algorithm is used to classify the arterial-venous vessels. The experimental results on the DRIVE database and INSPIRE-AVR database achieve the high accuracy of 88.65% and 88.51% in ROI, respectively.     

Object-based satellite image analysis applied for crop residue estimating using Landsat OLI imagery

ABSTRACT

The main objective of this study is to apply an object-based image analysis (OBIA) approach to satellite image processing and determining crop residue cover (CRC) and tillage intensity. To achieve this goal, we collected ground truth data using line-transect method from 35 plots of farmlands with an area of 528 ha. Accordingly, Landsat Operational Land Imager (OLI) satellite image together with global positioning system (GPS)-based survey data set were considered for applying the OBIA methods and deriving CRC. To process the data, object-based image processing steps including segmentation and classification were applied to develop intelligent objects and establish classification using spectral and spatial characteristics of CRC. We developed three categories of rule sets including mean indices, tillage indices, and grey-level co-occurrence matrix (GLCM) texture features using the OBIA algorithms and assign class method. Results were validated against of ground control data set and were collected by GPS in field survey. Results of this study indicated that the brightness, normalised difference tillage index, and GLCM texture feature mean performed out as effective techniques. Overall accuracy and kappa coefficient (κ) were computed to be about 0.91 and 0.86; 0.93 and 0.90; 0.60 and 0.35, respectively, for the above-mentioned indices. The foregoing discussion has attempted to demonstrate that the remotely sensed data can be effective approach and substitute for ground methods, especially in large areas.     

Segmentation of Rumex obtusifolius using Gaussian Markov random fields

Rumex obtusifolius is a common weed that is difficult to control. The most common way to control weeds—using herbicides—is being reconsidered because of its adverse environmental impact. Robotic systems are regarded as a viable non-chemical alternative for treating R. obtusifolius and also other weeds. Among the existing systems for weed control, only a few are applicable in real-time and operate in a controlled environment. In this study, we develop a new algorithm for segmentation of R. obtusifolius using texture features based on Markov random fields that works in real-time under natural lighting conditions. We show its performance by comparing it with an existing real-time algorithm that uses spectral power as texture feature. We show that the new algorithm is not only accurate with detection rate of 97.8 % and average error of 56 mm in estimating the location of the tap-root of the plant, but is also fast taking just 0.18 s to process an image of size $576 \times 432$ pixels making it feasible for real-time applications.     

Species classification of aquatic plants using GRNN and BPNN

Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plants. This work presents a method for plant species identification using the images of flowers. It focuses on the stable feature extraction of flowers such as color, texture and shape features. Color-based segmentation using k-means clustering is used to extract the color features. Texture segmentation using texture filter is used to segment the image and obtain texture features. Sobel, Prewitt and Robert operators are used to extract the boundary of image and to obtain the shape features. From 405 images of flowers, color, texture and shape features are extracted. Classification of the plants into dry land plants and aquatic plants, the aquatic plant species into wet and marsh aquatic plants, wet aquatic plants into Iridaceae and Epilobium family and marsh aquatic plants into Malvaceae and Onagraceae family, the Iridaceae family is again classified into Babiana and Crocus species, the family Epilobium into Canum and Hirsutum, the family Malvaceae into Mallow and Pavonia, the family Onagraceae into Fuschia and Ludwigia species are done using general regression neural network and backpropagation neural network classifiers.     

深度学习多部位病灶检测与分割

目的 多部位病灶具有大小各异和类型多样的特点,对其准确检测和分割具有一定的难度。为此,本文设计了一种2.5D深度卷积神经网络模型,实现对多种病灶类型的计算机断层扫描（computed tomography,CT）图像的病灶检测与分割。方法 利用密集卷积网络和双向特征金字塔网络组成的骨干网络提取图像中的多尺度和多维度信息,输入为带有标注的中央切片和提供空间信息的相邻切片共同组合而成的CT切片组。将融合空间信息的特征图送入区域建议网络并生成候选区域样本,再由多阈值级联网络组成的Cascade R-CNN（region convolutional neural networks）筛选高质量样本送入检测与分割分支进行训练。结果 本文模型在DeepLesion数据集上进行验证。结果表明,在测试集上的平均检测精度为83.15%,分割预测结果与真实标签的端点平均距离误差为1.27 mm,直径平均误差为1.69 mm,分割性能优于MULAN（multitask universal lesion analysis network for joint lesion detection,tagging and segmentation）和Auto RECIST（response evaluation criteria in solid tumors）,且推断每幅图像平均时间花费仅91.7 ms。结论 对于多种部位的CT图像,本文模型取得良好的检测与分割性能,并且预测时间花费较少,适用病变类别与DeepLesion数据集类似的CT图像实现病灶检测与分割。本文模型在一定程度上能满足医疗人员利用计算机分析多部位CT图像的需求。