Gray Level Co-occurrence Matrix Features Research Articles

Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques.

To determine equivalency of multi-slice 3D CTTA and single slice 2D CTTA of pancreas adenocarcinoma. This retrospective study was research ethics board approved. Untreated pancreas adenocarcinomas were segmented on CT in 128 consecutive patients. Tumor segmentation was compared using two techniques: 3D segmentation by contouring all visible tumor in a 3D volume, and 2D segmentation using only a single axial image. First-order CTTA features including mean, minimum, maximum Hounsfield units (HU), standard deviation, skewness, kurtosis, entropy, and second-order gray-level co-occurrence matrix (GLCM) features homogeneity, contrast, correlation, entropy and dissimilarity were extracted. Median values were compared using the Mann-Whitney U test with Holm-Bonferroni correction. Kendall's Rank Correlation Tau assessed for correlation, and agreement was calculated using intraclass correlation coefficients (ICC) using a two-way model with single rating and absolute agreement. Statistical significance defined as P < 0.05. The median values of CTTA features differed significantly between 3 and 2D segmentations for all of the evaluated features except for mean attenuation, standard deviation and skewness (P = 0.2979 each). 3D and 2D segmentations had moderate correlation for mean attenuation (R = 0.69, P < 0.01), while all other features demonstrated poor to fair correlation. Agreement between 3 and 2D segmentations was good for mean attenuation (ICC: 0.87, P < 0.01), moderate for minimum (ICC: 0.65, P < 0.01) and standard deviation (ICC: 0.56, P < 0.01), and poor for all other features. While pancreas adenocarcinoma CTTA features obtained using 3D and 2D segmentation have multiple associations with clinically relevant outcomes, these segmentation techniques are likely not interchangeable other than for mean HU.

Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment

A large number of countries depend on agriculture. However, the biggest challenge for farmers is the plant-related diseases. The most observed part of the plant is leaf but the segmentation of a diseased lesion leaf image suffers from uneven illumination and cluttered complex natural environment problems. To overcome these problems, the proposed methodology is based on the fusion of color balancing and superpixel. First, the input image transforms into a color-balanced image to eliminate the effects of uneven illumination. Second, compact regions are created from the transformed image using superpixel operation and the empirically derived threshold is applied on Histogram of Gradients (HOG) and color channels of superpixel to separate the unwanted background from leaf image. K-means clustering is applied to find a diseased infected image. An extended form of HOG, Pyramid of HOG(PHOG) with Gray Level Co-occurrence Matrix (GLCM) features are used to represent a diseased infected part. Finally, different classifiers are compared and Random Forest (RF) is selected to classify diseases. To assess the robustness of the proposed method, an experiment is conducted to test its accuracy effectively on natural images acquired from various farms, downloaded images from the Internet and Plant-Village color dataset. Results indicate that the proposed method achieved an accuracy of 93.12% on a combined dataset using cross-validation to show that method is effective to classify diseases in the presence of a cluttered background. The proposed system is compared with ALDD method (Dhaygude and Kumbhar, 2013), MVBPDR (Habib et al., 2018), AIMSD (Araujo and Peixoto, 2019) and CCF (Ma et al., 2017). The novelty of the proposed system lies in the fact that the proposed segmentation is effective for all the varied datasets and it is evaluated by True Positive Rate (TPR), False Positive Rate (FPR), False Negative Rate (FNR), Positive Prediction Value (PPV), False Discovery Rate (FDR), Accuracy and F1 score.

Analysis of the Naïve Bayes Method in Classifying Formalized Fish Images Using GLCM Feature Extraction

Fish is one of the foods that are high in protein so that many Indonesians consume fish as protein intake for health. Fish can be found in any waters including Indonesian marine waters, so that some of the Indonesian people work as fishermen. This causes the number of fish catches to increase and the fishermen have to sell the fish quickly in at least one day because the fish will rot easily if not consumed immediately. This has led some traders to cheat by mixing formaldehyde with fish that are not sold out. This action is very detrimental to consumers, so they must be more vigilant in choosing or buying fish on the market. One way for consumers to recognize formaldehyde fish is a technology that can distinguish fresh fish or formalin fish based on the image of the fish, Naive Bayes and GLCM (Gray Level Co-Occurrence Matrix) by using this method the accuracy of this system can reach up to 70%.

Wayang Image Classification Using MLP Method and GLCM Feature Extraction

Wayang is a form of shadow art that has been known to the Javanese people more than 1500 years ago. For Javanese people, the function of wayang is not only as a spectacle but also as a request, because in the wayang story there are values that are important to Javanese society. Wayang has developed from time to time, there are many types of wayang in Indonesia, with many types of wayang in Indonesia, of course preserving the art of wayang kulit is not an easy thing, especially because this traditional art is not yet very popular among young people, especially in the regionsburban. Today's young people use technology more in finding information, such as using laptops or smartphones. Because to make it easier for people who want to know about puppets and their types, a technology is created that can distinguish the types of puppets based on wayang images. So this research was made using the MLP (The Multi Layer Perceptron) method and its extraction feature GLCM (Gray-Level Co-Occurrence Matrix) with a total system accuracy of recognizing wayang image objects up to 73.4%.

Infrared retinal images for flashless detection of macular edema

This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (AZ) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

The Role of Acquisition Angle in Digital Breast Tomosynthesis: A Texture Analysis Study

Background: Digital breast tomosynthesis (DBT) systems employ a sophisticated set of acquisition parameters to generate an image set, and the DBT acquisition angle is considered to be one of the most important parameters. The aim of this study was to use texture analysis to assess how the DBT acquisition angle might influence DBT images of breast parenchyma. Methods: Thirty-four patients were selected from a clinical study conducted at IRST Institute. Each patient underwent a dual DBT scan performed with Fujifilm Amulet Innovality (Fujifilm Corp, Tokyo, Japan) in standard (ST, angular range = 15°) and high-resolution (HR, angular range = 40°) modalities. Texture analysis was applied on the paired dataset using histogram-based features and gray level co-occurrence matrix (GLCM) features. Wilcoxon-signed rank and Pearson-rank tests were used to assess the statistical differences and correlations between extracted features. Results: The DBT acquisition angle did not affect histogram-based features, whereas there was a significant difference in five GLCM features (p &lt; 0.05) between DBT images generated with 15° and 40° acquisition angles. Correlation analysis showed that two GLCM features were not correlated at a p &lt; 0.05 significance level. Conclusions: DBT acquisition angle affects the textures extracted from DBT images and this dependence should be considered when establishing baselines for classifiers of malignant tissue. Furthermore, texture analysis could be proposed as a quantitative method for comparing and scoring the contrast of DBT images.

A methodology of steel microstructure recognition using SEM images by machine learning based on textural analysis

We attempt image-based recognition of scanning electron microscopy (SEM) of low-carbon steels using machine learning (ML) methodology utilizing feature extraction with textural analysis. Specimens are subjected to eight different heat treatments to generate microstructures of martensite, upper bainite, lower bainite, and these mixed structures are selected as candidates for the proposed recognition method. Additionally, two types of SEM sources, i.e. field emission (FE) and tungsten (W), are used, and the images individually apply to the classifications. To extract features, a textural analysis based on gray-level co-occurrence matrix (GLCM) is adopted based on the captured SEM images. For evaluating GLCM features, the original SEM images are cropped into 16 mini-images, and the following three recognition schemes are then applied: recognize mini-images individually, recognize original image categories by majority vote on predictions of mini-images, and recognize original image by using feature averages of the mini-images for training. We also adopt two decision tree-based machine learning models of random forest (RF) and gradient boosting machine (GBM). The voting schemes for both models accomplish satisfactory accuracies of about 85% for eight steel microstructures. Especially, GBM is found to exhibit more stable performance than that of RF, as differences in accuracies between types of SEM sources are lower. In addition, differences between the SEM sources are discussed by focusing on important features estimated by these ML models, and the features from FE-SEM tend to be distributed more broadly than those of W-SEM. Although a textural value called homogeneity is commonly selected as an important feature, regardless of sources and models, the other important GLCM features exhibit different trends for both SEM sources. Models trained by FE- and W-SEM tend to prefer indices of high sensitivity with regards to local brightness differences and smoothness, respectively. Due to these differences in GLCM features and their importance, models trained with images from one SEM source cannot adequately recognize the other SEM images.

MODELLING OF LAND SURFACE TEMPERATURE USING GRAY LEVEL CO-OCCURRENCE MATRIX AND RANDOM FOREST REGRESSION

Abstract. Modelling of land surface temperature (LST) is conducted to be able to explain the spatial and temporal variations of LST using a set of explanatory variables. LST in a previous study was modelled as a linear function of vegetation cover and built up cover as quantified by the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI), respectively, and other variables, namely, albedo, solar radiation (SR), surface area-volume ratio (SVR), and skyview factor (SVF). SVF requires a digital surface model of sufficient resolution while SVR computation needs 3D volumetric features representing buildings as input. These inputs are typically not readily available. In addition, NDVI and NDBI do not fully describe the spatial variability of vegetation and built-up cover within an LST pixel. In this study, PlanetScope images (3m resolution) were processed to provide soil-adjusted vegetation index (SAVI) and VgNIR Built-up Index (VgNIR-BI) layers. The following gray level co-occurrence matrices (GLCM) were generated from SAVI and VgNIR-BI: Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, and Correlation. Random Forest regression was run for several cases with different combinations of GLCM features and non-GLCM variables. Using GLCM features alone yielded less satisfactory models. However, the use of additional GLCM features in combination with other variables resulted in lower MSE and a slight increase in R2. Considering NDBI, NDVI, SAVI_GLCM_contrast, VgNIR-BI_GLCM_contrast, VgNIR-BI_GLCM_dissimilarity, and SAVI_GLCM_contrast only, the RF model yielded an MSE=1.657 and validation R2=0.822. While this 6-variable model’s performance is slightly less, the need for DSM and 3D building models which are necessary for the generation of SVF and SVR layers is eliminated. Exploratory regression (ER) was also conducted. The best 6-variable ER model (Adj. R2=0.79) consists of SVR, NDBI, NDVI, SAVI_GLCM_second_moment, VgNIR-BI_GLCM_mean, and VgNIR-BI_GLCM_entropy. In comparison, OLS regression using the 6 non-GLCM variables yielded an Adj. R2=0.691. The results of RFR and ER both indicate the value of GLCM features in providing valuable information to the models of LST. LST is best described through a combination of GLCM features describing relatively homogenous areas (i.e., dominant land cover or low-frequency areas) and the more heterogenous areas (i.e., edges or high-frequency areas) and non-GLCM variables.

Classification of sea ice types in Sentinel-1 synthetic aperture radar images

Abstract. A new Sentinel-1 image-based sea ice classification algorithm using a machine-learning-based model trained in a semi-automated manner is proposed to support daily ice charting. Previous studies mostly rely on manual work in selecting training and validation data. We show that the readily available ice charts from the operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data. Furthermore, they can feed highly reliable data to the trainer by indirectly exploiting the best ability of the sea ice experts working at the operational ice services. The proposed scheme has two phases: training and operational. Both phases start from the removal of thermal, scalloping, and textural noise from Sentinel-1 data and calculation of grey level co-occurrence matrix and Haralick texture features in a sliding window. In the training phase, the weekly ice charts are reprojected into the SAR image geometry. A random forest classifier is trained with the texture features on input and labels from the rasterized ice charts on output. Then, the trained classifier is directly applied to the texture features from Sentinel-1 images operationally. Test results from the two datasets spanning winter (January–March) and summer (June–August) seasons acquired over the Fram Strait and the Barents Sea showed that the classifier is capable of retrieving three generalized cover types (open water, mixed first-year ice, old ice) with overall accuracies of 87 % and 67 % in winter and summer seasons, respectively. For the summer season, the classifier failed in distinguishing mixed first-year ice from old ice with accuracy of only 12 %; however, it performed rather like an ice–water discriminator with high accuracy of 98 % as the misclassification between the mixed first-year ice and old ice was between them. The accuracy for five cover types (open water, new ice, young ice, first-year ice, old ice) in the winter season was 60 %. The errors are attributed both to incorrect manual classification on the ice charts and to the semi-automated algorithm. Finally, we demonstrate the potential for near-real-time service of the ice map using daily mosaicked Sentinel-1 images.

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

BackgroundChest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis.MethodsAn integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation.Results and conclusionsThe classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

A Novel Method of Quantifying the Choriocapillaris in Normal and Post-inflammatory Eyes

ABSTRACT Objective To assess the reliability and validity of gray level co-occurrence matrices (GLCM) in the quantification of choriocapillaris and describe GLCM features in normal and eyes with resolved acute posterior multifocal placoid pigment epitheliopathy (APMPPE) and serpiginous choroiditis (SC). Methods In this, multicenter, reliability, validity and comparative study; OCTA was performed on eyes with resolved APMPPE and SC and normal individuals. CC texture classification, low flow area measurements and GLCM feature extraction were performed. Results A total of 13 normal, 8 APMPPE and 15 SC eyes were analyzed. All GLCM parameters demonstrated an excellent reliability. GLCM parameters were differently distributed across the three groups. Decision-tree based on the random forest predictive model provided an overall accuracy of 86% in classifying the three groups using GLCM features. Conclusion These data demonstrated an excellent reliability and validity of GLCM features in quantifying the choriocapillaris in healthy and diseased eyes.

Mid-term response assessment in multiple myeloma using a texture analysis approach on dual energy-CT-derived bone marrow images — A proof of principle study

PurposeTo identify textural features on dual-energy CT (DECT)-generated virtual non calcium (VNC) bone marrow images in a small group of patients with multiple myeloma undergoing systemic treatment which could potentially help for mid-term response assessment. Methods44 patients (59.1 ± 11.2 yr.) with multiple myeloma who underwent unenhanced whole-body reduced-dose DECT before and after systemic therapy were evaluated. All patients had current hematologic laboratory tests including serum levels of immunoglobulins, albumin, and total proteins. Using DECT post-processing, bone marrow images of the axial skeleton were reconstructed. The vertebral bodies T10–L5 were segmented for quantification of 1st order (n = 18) and 2nd order Gray Level Co-occurrence Matrix (GLCM) textural features (n = 23) based on an open-source radiomics library (Pyradiomics), which were then compared with the hematologic response category to treatment. Five patients underwent only active surveillance at intervals after previous successful therapy. ResultsAccording to hematologic diagnosis, 29 patients were classified as complete response (CR), 10 as partial response (PR) and 5 as stable disease (SD). We observed a significant drop of the 1st order textural features “10th percentile” (p = 0.009), “median” (p = 0.01), and “minimum” (p < 0.0001) after treatment, whereas the 1st order feature “range” (p = 0.0004) and the 2nd order GLCM feature “difference variance” (p = 0.007) significantly increased in patients experiencing CR. A similar trend, however, without statistical significance, could be observed in patients achieving PR after treatment. 2nd order GLCM feature “difference variance” proved to be a significant discriminator (p = 0.01) between patients with CR and PR (sensitivity 0.93, specificity 0.70) for a cut-off value of -0.28. In patients classified CR, both the mean serum protein and the beta-2 microglobulin decreased after treatment, whereas the serum albumin increased (p < 0.01). The same trend without significance could be observed in patients classified PR. ConclusionsChanges in textural features applied on VNC bone marrow images in the pre- and posttreatment settings correlate well with myeloma-specific hematologic parameters and provide complementary information for the assessment of the late effects of treatment on the bone marrow.

Road surface classification based on LBP and GLCM features using kNN classifier

Autonomous Ground Vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these process require a fast computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One can experiences difficulties because the condition of the road varies, this research proposes a combination of Gray Level Co-occurrence Matrix (GLCM) a statistical method to extract feature and Local Binary Pattern (LBP) feature to improve the robustness of the features. The kNN classifier is used to do the classification with the accuracy of 98% and 12 picture processed per second.

A Hybrid Data-Driven Method for Wire Rope Surface Defect Detection

Visual inspection method (VIM) has attracted more and more attention because it is fast, nondestructive, automatic, and objective, which can replace manual inspection method or assist other non-destructive testing methods for wire ropes (WR) to a certain extent. However, it is still a challenging task to accurately detect the potential defects and identify the types from the WR surface morphology. In this paper, an efficient hybrid data-driven method based on texture features and optimized support vector machine (SVM) is proposed to solve this problem, which is called WR-IFOA-SVM. Uniform local binary pattern and gray-level co-occurrence matrix features were extracted and fused from image dataset which contains three most common states, i.e. healthy, broken and worn WRs. The inertial dynamic weight function was introduced into the fruit fly optimization algorithm (FOA) to overcome the problem that the traditional FOA cannot balance the global and local search ability. And the data mining experiments of the established feature dataset were carried out relying on the proposed WR-IFOA-SVM model, which was then compared with other methods. The experimental results show that this method can effectively detect various defect types on the WR surface, furthermore demonstrate that our method outperforms the state-of-the-art works in WR visual inspection field.

Detection of Breast Lesion Using Improved GLCM Feature Based Extraction in Mammogram Images

A classic mammogram image processing method commonly illustrates of mammogram image pre-processing, segmentation, feature extraction and classification techniques. Feature extraction is a significant measure in Computer Assisted Diagnosis (CAD) of Breast cancer abnormalities. Feature Extraction is the method of shrinking the dimension of image data by finding an essential detail as of the segmented image. The visual substance of a segmented image portions can be confined using this procedure. Feature extraction is one of the Image retrieval methods for achieving higher accuracy. In this paper an Improved Gray Level Co-occurrence Matrix (GLCM) Feature based Extraction with shape and Tamura features are discussed. Tamura features are extracted from Optimized Kernel Fuzzy Clustering Algorithm (OKFCA) which is one of the segmentation methods. This paper proposed Improved GLCM Feature Based Extraction algorithm to obtain the trained features which will be used for further processing in future. Experimental results of 322 images were evaluated from Mammographic Image Analysis Society (MIAS) database and used to evaluate the feature extraction process.

Implementation of Gray Level Coocurence Matrix on the Leaves of Rice Crops

Rice is one of the cultivation plants that are very important for human survival. The success of rice harvesting affects the level of farmers' income. However, farmers often suffer losses as a result of illness in rice. Rice plants infected with the disease will show symptoms in the form of patches that have certain patterns and colors on some parts of the body of rice plants, such as stems, leaves, and roots. Disease symptoms that emerge on the leaves are most easily identified because the leaves have a wider cross-section than other body parts of rice. Therefore, in this study, the leaf was used as an initial step parameter for disease detection in rice. This research aimed to identify diseases that exist in rice plants using the method of Gray Level Co-occurrence Matrix (GLCM). The GLCM method is a feature extraction method. The disease detection process on the leaves of the rice plants was done by retrieving the original image for the initial step; then, the original image was segmented before converted to greyscale imagery. After that, feature extraction was carried out using the GLCM features: Entropy, Eccentricity, Contrast, Energy, Correlation, Homogeneity. The results showed 90% accuracy results using GLCM extraction. The recognition of the emerging diseases on rice leaves can help to identify the type of disease infecting the rice plants.

Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor

BackgroundThis study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images.MethodsIn this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test.ResultsA total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC ≥ 0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves (AUC) of these features were 0.731 ± 0.075, 0.717, 0.748 ± 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features (Child features).ConclusionRadiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

Baseline Computed Tomography Radiomic and Genomic Assessment of Head and Neck Squamous Cell Carcinoma.

To determine the relationship between computed tomography (CT) radiomic features and gene expression levels in head and neck squamous cell carcinoma (HNSCC). This retrospective study included 66 patients with HNSCC primary lesions (36 oropharyngeal, 6 hypopharyngeal, 10 laryngeal, 14 oral cavity). Gene expression information for 6 targetable genes (fibroblast growth factor receptor [FGFR]1, epidermal growth factor receptor [EGFR], FGFR2, FGFR3, EPHA2, PIK3CA) was obtained via Agilent microarrays from samples collected between 1997 and 2010. Pretreatment contrast-enhanced soft tissue neck CT scans were reviewed, and 142 radiomics features were derived. R was used to calculate Pearson correlation coefficients were calculated between gene expression levels and each radiomic feature. P values were adjusted using the false discovery rate (FDR) method. There were significant correlations between FGFR1 and 5 gray level cooccurrence matrix (GLCM) features with FDR-adjusted P values less than 0.05: inertia (r = 0.366, FDR-adjusted P = 0.006), absolute value (r = 0.31, FDR-adjusted P = 0.024), contrast (r = 0.366, FDR-adjusted P = 0.006), difference average (r = 0.31, FDR-adjusted P = 0.024), and difference variance (r = 0.37, FDR-adjusted P = 0.005). There was 1 correlated feature for FGFR2 with an FDR-adjusted P value less than 0.05: fractal dimension box-coarse (r = 0.33, FDR-adjusted P = 0.018). There was 1 correlated feature for EPHA2 with an FDR-adjusted P value less than 0.05: GLCM entropy (r = -0.28, FDR-adjusted P = 0.049). Six of the 7 features that showed significant correlation belonged to the GLCM class of features. The CT radiomic features demonstrate correlations with FGFR1 status in HNSCC and should be further investigated for their potential to predict FGFR1 status.

Recognition of Lung Tumor Area Based on Watershed Segmentation and CNN

Early recognition of tumor would assist in saving an enormous number of lives over the globe frequently. Study and remedy of lung tumor have been one of the greatest troubles faced by humans over the latest few decades. Effective recognition of lung tumor is a vital and crucial aspect of image processing. Several Segmentation methods were used to detect lung tumor at an early stage. An approach is presented in this paper to diagnose lung tumor from CT scan images. The input image (CT scan image) will be preprocessed initially using median filter to remove the noise. After applying preprocessing technique, the Dual-Tree Complex Wavelet Transform (DTCWT) segmentation technique is used for the edge detection. The Gray-Level Co-occurrence Matrix (GLCM) features are calculated based on the pixel values of the extracted image. These features can be compared with database images using Convolutional Neural Network (CNN) which facilitates in categorizing it as tumorous. After confirming that the affected area is tumorous, watershed segmentation algorithm is used to get the color features of the tumor.