Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.

Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.

Application of Feature Definition and Quantification in Biological Sequence Analysis.

Biological sequence analysis is the most fundamental work in bioinformatics. Many research methods have been developed in the development of biological sequence analysis. These methods include sequence alignment-based methods and alignment-free methods. In addition, there are also some sequence analysis methods based on the feature definition and quantification of the sequence itself. This editorial introduces the methods of biological sequence analysis and explores the significance of defining features and quantitative research of biological sequences.

Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study.

In this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.

A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest.

BACKGROUND: Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI). METHODS: Twenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification. RESULTS: The highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively. CONCLUSIONS: Our results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.

Reproducibility of the principal component analysis (PCA)-based data-driven respiratory gating on texture features in non-small cell lung cancer patients with 18 F-FDG PET/CT.

OBJECTIVE: Texture analysis is one of the lung cancer countermeasures in the field of radiomics. Even though image quality affects texture features, the reproducibility of principal component analysis (PCA)-based data-driven respiratory gating (DDG) on texture features remains poorly understood. Hence, this study aimed to clarify the reproducibility of PCA-based DDG on texture features in non-small cell lung cancer (NSCLC) patients with 18 F-Fluorodeoxyglucose (18 F-FDG) Positron emission tomography/computed tomography (PET/CT). METHODS: Twenty patients with NSCLC who underwent 18 F-FDG PET/CT in routine clinical practice were retrospectively analyzed. Each patient's PET data were reconstructed in two PET groups of no gating (NG-PET) and PCA-based DDG gating (DDG-PET). Forty-six image features were analyzed using LIFEx software. Reproducibility was evaluated using Lin's concordance correlation coefficient ( ρ c ${\rho _c}$ ) and percentage difference (%Diff). Non-reproducibility was defined as having unacceptable strength ( ρ c $({\rho _c}$  < 0.8) and a %Diff of >10%. NG-PET and DDG-PET were compared using the Wilcoxon signed-rank test. RESULTS: A total of 3/46 (6.5%) image features had unacceptable strength, and 9/46 (19.6%) image features had a %Diff of >10%. Significant differences between the NG-PET and DDG-PET groups were confirmed in only 4/46 (8.7%) of the high %Diff image features. CONCLUSION: Although the DDG application affected several texture features, most image features had adequate reproducibility. PCA-based DDG-PET can be routinely used as interchangeable images for texture feature extraction from NSCLC patients.

Research on Recognition of Coal and Gangue Based on Laser Speckle Images.

Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines.

Multiparametric Quantitative Analysis of Photodamage to Skin Using Optical Coherence Tomography.

Ultraviolet (UV) irradiation causes 90% of photodamage to skin and long-term exposure to UV irradiation is the largest threat to skin health. To study the mechanism of UV-induced photodamage and the repair of sunburnt skin, the key problem to solve is how to non-destructively and continuously evaluate UV-induced photodamage to skin. In this study, a method to quantitatively analyze the structural and tissue optical parameters of artificial skin (AS) using optical coherence tomography (OCT) was proposed as a way to non-destructively and continuously evaluate the effect of photodamage. AS surface roughness was achieved based on the characteristic peaks of the intensity signal of the OCT images, and this was the basis for quantifying AS cuticle thickness using Dijkstra's algorithm. Local texture features within the AS were obtained through the gray-level co-occurrence matrix method. A modified depth-resolved algorithm was used to quantify the 3D scattering coefficient distribution within AS based on a single-scattering model. A multiparameter assessment of AS photodamage was carried out, and the results were compared with the MTT experiment results and H&E staining. The results of the UV photodamage experiments showed that the cuticle of the photodamaged model was thicker (56.5%) and had greater surface roughness (14.4%) compared with the normal cultured AS. The angular second moment was greater and the correlation was smaller, which was in agreement with the results of the H&E staining microscopy. The angular second moment and correlation showed a good linear relationship with the UV irradiation dose, illustrating the potential of OCT in measuring internal structural damage. The tissue scattering coefficient of AS correlated well with the MTT results, which can be used to quantify the damage to the bioactivity. The experimental results also demonstrate the anti-photodamage efficacy of the vitamin C factor. Quantitative analysis of structural and tissue optical parameters of AS by OCT enables the non-destructive and continuous detection of AS photodamage in multiple dimensions.

A genome-wide association study of mammographic texture variation.

BACKGROUND: Breast parenchymal texture features, including grayscale variation (V), capture the patterns of texture variation on a mammogram and are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of these texture features is limited. METHODS: We conducted a genome-wide association study of V in 7040 European-ancestry women. V assessments were generated from digitized film mammograms. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes, and the top four genetic principal components. We further calculated genetic correlations and performed SNP-set tests of V with MD, breast cancer risk, and other breast cancer risk factors. RESULTS: We identified three genome-wide significant loci associated with V: rs138141444 (6q24.1) in ECT2L, rs79670367 (8q24.22) in LINC01591, and rs113174754 (12q22) near PGAM1P5. 6q24.1 and 8q24.22 have not previously been associated with MD phenotypes or breast cancer risk, while 12q22 is a known locus for both MD and breast cancer risk. Among known MD and breast cancer risk SNPs, we identified four variants that were associated with V at the Bonferroni-corrected thresholds accounting for the number of SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) near SSPN, and rs17817449 (16q12.2) in FTO. We observed significant genetic correlations between V and mammographic dense area (rg = 0.79, P = 5.91 × 10-5), percent density (rg = 0.73, P = 1.00 × 10-4), and adult BMI (rg = - 0.36, P = 3.88 × 10-7). Additional significant relationships were observed for non-dense area (z = - 4.14, P = 3.42 × 10-5), estrogen receptor-positive breast cancer (z = 3.41, P = 6.41 × 10-4), and childhood body fatness (z = - 4.91, P = 9.05 × 10-7) from the SNP-set tests. CONCLUSIONS: These findings provide new insights into the genetic basis of mammographic texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors.

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography.

BACKGROUND AND OBJECTIVES: Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer. MATERIALS AND METHODS: Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance. RESULTS: A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively. CONCLUSION: It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination.

Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.

Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism.

Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.

A multi features based background modelling approach for moving object detection.

Background subtraction always remains an important and challenging task for different applications. Our previous work established the effectiveness of hybrid model by exploiting the oriented patterns present in a video sequences over other statistical method. To extend this approach further, we have proposed a novel approach herein by eliminating GLCM based features with an improved local Zernike moment and color components of intensity. These features are clubbed with the orientation based features extracted from angle co-occurrence matrices (ACMs) to model the background. Furthermore the Mahalanobis distance measure is replaced by Canberra distance to categorized foreground and background pixels, which significantly reduces the computational complexity of the proposed method due to the absence of covariance matrix measure. Comparative results have shown that our proposed method is effective than other competing method on different set of video sequences.

[Performance of the Combined Model Based on Both Clinicopathological and CT Texture Features in Predicting Liver Metastasis of High-risk Gastrointestinal Stromal Tumors].

Objective To investigate the performance of the combined model based on both clinicopathological features and CT texture features in predicting liver metastasis of high-risk gastrointestinal stromal tumors(GISTs). Methods The high-risk GISTs confirmed by pathology from January 2015 to December 2020 were analyzed retrospectively,including 153 cases from the Cancer Hospital of the University of Chinese Academy of Sciences and 51 cases from the Shaoxing Central Hospital.The cases were randomly assigned into a training set(n=142)and a test set(n=62)at a ratio of 7∶3.According to the results of operation or puncture,they were classified into a liver metastasis group(76 cases)and a non-metastasis group(128 cases).ITK-SNAP was employed to delineate the volume of interest of the stromal tumors.Least absolute shrinkage and selection operator(LASSO)was employed to screen out the effective features.Multivariate logistic regression was adopted to construct the models based on clinicopathological features,texture features extracted from CT scans,and the both(combined model),respectively.Receiver operating characteristic(ROC)curve and calibration curve were established to evaluate the predictive performance of the models.The area under the curve(AUC)was compared by Delong test. Results Body mass index(BMI),tumor size,Ki-67,tumor occurrence site,abdominal mass,gastrointestinal bleeding,and CA125 level showed statistical differences between groups(all P<0.05).A total of 107 texture features were extracted from CT images,from which 13 and 7 texture features were selected by LASSO from CT plain scans and CT enhanced scans,respectively.The AUC of the prediction with the training set and the test set respectively was 0.870 and 0.855 for the model based on clinicopathological features,0.918 and 0.836 for the model based on texture features extracted from CT plain scans,0.920 and 0.846 for the model based on texture features extracted from CT enhanced scans,and 0.930 and 0.889 for the combined model based on both clinicopathological features and texture features extracted from CT plain scans.Delong test demonstrated no significant difference in AUC between the models based on the texture features extracted from CT plain scans and CT enhanced scans(P=0.762),whereas the AUC of the combined model was significantly different from that of the clinicopathological feature-based model and texture feature-based model(P=0.001 and P=0.023,respectively). Conclusion Texture features extracted from CT plain scans can predict the liver metastasis of high-risk GISTs,and the model established with clinicopathological features combined with CT texture features has best prediction performance.

Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images.

BACKGROUND: The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT (computed tomography) examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. METHOD: The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are GGO (ground-glass opacity), cord, solid and subsolid. A computer-aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three-dimensional texture descriptors are applied on the volume data of lesions as well as shape and first-order features. The massive feature data are selected by HAFS (hybrid adaptive feature selection) algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. RESULTS: There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (93.06%, 96.84%, 99.58%, and 94.30%), the recall is (95.52%, 91.58%, 95.80% and 80.75%) and the f-score is (93.84%, 92.37%, 95.47%, and 84.42%). CONCLUSION: The three-dimensional radiomics features used in this paper can better express the high-level information of COVID-19 lesions in CT slices. HAFS method aggregates the results of multiple feature selection algorithms intersects with traditional methods to filter out redundant features more accurately. After selection, the subtype of COVID-19 lesion can be judged by inputting the features into the RF (random forest) model, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging.

BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM. METHODS: Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. RESULTS: No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists' assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively. CONCLUSION: T1CE-based radiomics showed better classification performance compared with radiologists' assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

Water Extraction Method Based on Multi-Texture Feature Fusion of Synthetic Aperture Radar Images.

Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, the dynamic changes of the Poyang Lake are monitored by Synthetic Aperture Radar (SAR). In order to extract water from SAR images to monitor water change, a water extraction algorithm composed of texture feature extraction, feature fusion and target segmentation was proposed. Firstly, the fractal dimension and lacunarity were calculated to construct the texture feature set of a water object. Then, an iterated function system (IFS) was constructed to fuse texture features into composite feature vectors. Finally, lake water was segmented by the multifractal spectrum method. Experimental results showed that the proposed algorithm accurately extracted water targets from SAR images of different regions and different imaging modes. Compared with common algorithms such as fuzzy C-means (FCM), the accuracy of the proposed algorithm is significantly improved, with an accuracy of over 98%. Moreover, the proposed algorithm can accurately segment complex coastlines with mountain shadow interference. In addition, the dynamic analysis of the changes of the water area of the Poyang Lake Basin was carried out with the local hydrological data. It showed that the extracted results of the algorithm in this paper are a good match with the hydrological data. This study provides an accurate monitoring method for lake water under complex backgrounds.

Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study.

BACKGROUND: To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC). METHOD: One hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman's correlation coefficient. RESULTS: The grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined. CONCLUSIONS: Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC.

Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.

Fusarium head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed based on the fusion features of image and spectral features. First, the hyperspectral image of FHB infected in the range of the 400-1000 nm spectrum was collected, and the color parameters of wheat ear and spot region were segmented based on image features. Twelve sensitive bands were extracted using the successive projection algorithm, gray-scale co-occurrence matrix, and RGB color model. Four texture features were extracted from each feature band image as texture variables, and nine color feature variables were extracted from R, G, and B component images. Texture features with high correlation and color features were selected to participate in the final model building parameters via correlation analysis. Finally, the particle swarm optimization support vector machine (PSO-SVM) algorithm was used to build the model based on the diagnosis model of disease severity of FHB with different combinations of characteristic variables. The experimental results showed that the PSO-SVM model based on spectral and color feature fusion was optimal. Moreover, the accuracy of the training and prediction set was 95% and 92%, respectively. The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of FHB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method.

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2's red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.