Differentiation of Cartilage Repair Techniques Using Texture Analysis from T2 Maps

Objective The aim of this study was to investigate texture features from T2 maps as a marker for distinguishing the maturation of repair tissue after 2 different cartilage repair procedures. Design Seventy-nine patients, after either microfracture (MFX) or matrix-associated chondrocyte transplantation (MACT), were examined on a 3-T magnetic resonance (MR) scanner with morphological and quantitative (T2 mapping) MR sequences 2 years after surgery. Twenty-one texture features from a gray-level co-occurrence matrix (GLCM) were extracted. The texture feature difference between 2 repair types was assessed individually for the femoral condyle and trochlea/anterior condyle using linear regression models. The stability and reproducibility of texture features for focal cartilage were calculated using intra-observer variability and area under curve from receiver operating characteristics. Results There was no statistical significance found between MFX and MACT for T2 values ( P = 0.96). There was, however, found a statistical significance between MFX and MACT in femoral condyle in GLCM features autocorrelation ( P < 0.001), sum of squares ( P = 0.023), sum average ( P = 0.005), sum variance ( P = 0.0048), and sum entropy ( P = 0.05); and in anterior condyle/trochlea homogeneity ( P = 0.02) and dissimilarity ( P < 0.001). Conclusion Texture analysis using GLCM provides a useful extension to T2 mapping for the characterization of cartilage repair tissue by increasing its sensitivity to tissue structure. Some texture features were able to distinguish between repair tissue after different cartilage repair procedures, as repair tissue texture (and hence, probably collagen organization) 24 months after MACT more closely resembled healthy cartilage than did MFX repair tissue.

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The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

Frontiers in Oncology ◽

10.3389/fonc.2021.745242 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoguang Li ◽

Hong Guo ◽

Chao Cong ◽

Huan Liu ◽

Chunlai Zhang ◽

...

Keyword(s):

Texture Analysis ◽

Roc Curve ◽

Breast Imaging ◽

Statistical Significance ◽

Texture Features ◽

Imaging Findings ◽

Mr Images ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Occurrence Matrix

PurposeTo explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs).MethodsA total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches.ResultsRegarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively.ConclusionTA based on DCE-MR images has potential in differentiating BPTs and BMPTs.

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Multi-scale characterizations of colon polyps via computed tomographic colonography

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-019-0032-7 ◽

2019 ◽

Vol 2 (1) ◽

Author(s):

Weiguo Cao ◽

Marc J. Pomeroy ◽

Yongfeng Gao ◽

Matthew A. Barish ◽

Almas F. Abbasi ◽

...

Keyword(s):

Area Under The Curve ◽

Texture Features ◽

Learning Model ◽

Colon Polyps ◽

Texture Descriptor ◽

Operating Characteristics ◽

Computed Tomographic ◽

Multi Scale ◽

Definition Of ◽

Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

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FORMING THE STACK OF TEXTURE FEATURES FOR LIVER ULTRASOUND IMAGES CLASSIFICATION

Herald of Advanced Information Technology ◽

10.15276/hait.04.2020.3 ◽

2020 ◽

Vol 3 (4) ◽

pp. 240-251

Author(s):

Dmitro Yuriiovych Hrishko ◽

Ievgen Arnoldovich Nastenko ◽

Maksym Oleksandrovych Honcharuk ◽

Volodymyr Anatoliyovich Pavlov

Keyword(s):

Random Forest ◽

Texture Analysis ◽

Binary Classification ◽

Texture Features ◽

Normal Liver ◽

Group Method ◽

Ultrasound Images ◽

Liver Ultrasound ◽

Random Forest Models ◽

Occurrence Matrix

This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor

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NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY

Neuro-Oncology ◽

10.1093/neuonc/noab196.554 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi142-vi142

Author(s):

Kaylie Cullison ◽

Garrett Simpson ◽

Danilo Maziero ◽

Kolton Jones ◽

Radka Stoyanova ◽

...

Keyword(s):

Radiation Therapy ◽

Tumor Growth ◽

Texture Feature ◽

Texture Features ◽

Gray Level ◽

Difference Matrix ◽

Patient Response ◽

Significant Difference ◽

Occurrence Matrix ◽

Surrounding Areas

Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.

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Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2020070103 ◽

2020 ◽

Vol 12 (3) ◽

pp. 27-44

Author(s):

Gulivindala Suresh ◽

Chanamallu Srinivasa Rao

Keyword(s):

Feature Vector ◽

Texture Feature ◽

Texture Features ◽

Support Vector ◽

Discrete Wavelet ◽

Detection Accuracy ◽

Detection Techniques ◽

Copy Move Forgery Detection ◽

Occurrence Matrix ◽

Differential Excitation

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

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First-in-Human Pilot Study of Implantation of a Scaffold-Free Tissue-Engineered Construct Generated From Autologous Synovial Mesenchymal Stem Cells for Repair of Knee Chondral Lesions

The American Journal of Sports Medicine ◽

10.1177/0363546518781825 ◽

2018 ◽

Vol 46 (10) ◽

pp. 2384-2393 ◽

Cited By ~ 20

Author(s):

Kazunori Shimomura ◽

Yukihiko Yasui ◽

Kota Koizumi ◽

Ryota Chijimatsu ◽

David A. Hart ◽

...

Keyword(s):

Stem Cells ◽

Mesenchymal Stem Cells ◽

Magnetic Resonance ◽

Cartilage Repair ◽

Femoral Condyle ◽

Chondral Lesions ◽

Repair Tissue ◽

Biopsy Specimens ◽

Implantation Procedure

Background: Articular cartilage has limited healing capacity, owing in part to poor vascularity and innervation. Once injured, it cannot be repaired, typically leading to high risk for developing osteoarthritis. Thus, cell-based and/or tissue-engineered approaches have been investigated; however, no approach has yet achieved safety and regenerative repair capacity via a simple implantation procedure. Purpose: To assess the safety and efficacy of using a scaffold-free tissue-engineered construct (TEC) derived from autologous synovial membrane mesenchymal stem cells (MSCs) for effective cartilage repair. Study Design: Case series; Level of evidence, 4. Methods: Five patients with symptomatic knee chondral lesions (1.5-3.0 cm2) on the medial femoral condyle, lateral femoral condyle, or femoral groove were included. Synovial MSCs were isolated from arthroscopic biopsy specimens and cultured to develop a TEC that matched the lesion size. The TECs were then implanted into chondral defects without fixation and assessed up to 24 months postoperatively. The primary outcome was the safety of the procedure. Secondary outcomes were self-assessed clinical scores, arthroscopy, tissue biopsy, and magnetic resonance image–based estimation of morphologic and compositional quality of the repair tissue. Results: No adverse events were recorded, and self-assessed clinical scores for pain, symptoms, activities of daily living, sports activity, and quality of life were significantly improved at 24 months after surgery. Secure defect filling was confirmed by second-look arthroscopy and magnetic resonance imaging in all cases. Histology of biopsy specimens indicated repair tissue approaching the composition and structure of hyaline cartilage. Conclusion: Autologous scaffold-free TEC derived from synovial MSCs may be used for regenerative cartilage repair via a sutureless and simple implantation procedure. Registration: 000008266 (UMIN Clinical Trials Registry number).

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Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v15i03.9832 ◽

2019 ◽

Vol 15 (03) ◽

pp. 47 ◽

Cited By ~ 2

Author(s):

Ann Nosseir ◽

Seif Eldin A. Ahmed

Keyword(s):

Texture Feature ◽

Texture Features ◽

Nearest Neighbors ◽

Support Vector ◽

K Nearest Neighbors ◽

A Value ◽

Different Types ◽

Linear Svm ◽

Occurrence Matrix

Having a system that classifies different types of fruits and identifies the quality of fruits will be of a value in various areas especially in an area of mass production of fruits’ products. This paper presents a novel system that differentiates between four fruits types and identifies the decayed ones from the fresh. The algorithms used are based on the colour and the texture features of the fruits’ images. The algorithms extract the RGB values and the first statistical order and second statistical of the Gray Level Co-occurrence Matrix (GLCM) values. To segregate between the fruits’ types, Fine, Medium, Coarse, Cosine, Cubic, and Weighted K-Nearest Neighbors algorithms are applied. The accuracy percentages of each are 96.3%, 93.8%, 25%, 83.8%, 90%, and 95% respectively. These steps are tested with 46 pictures taken from a mobile phone of seasonal fruits at the time i.e., banana, apple, and strawberry. All types were accurately identifying. To tell apart the decayed fruits from the fresh, the linear and quadratic Support Vector Machine (SVM) algorithms differentiated between them based on the colour segmentation and the texture feature algorithms values of each fruit image. The accuracy of the linear SVM is 96% and quadratic SVM 98%.

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MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment

Contrast Media & Molecular Imaging ◽

10.1155/2020/2126768 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Yang Zhang ◽

Chaoyue Chen ◽

Yangfan Cheng ◽

Danni Cheng ◽

Fumin Zhao ◽

...

Keyword(s):

Surgical Treatment ◽

Anaplastic Astrocytoma ◽

Cox Regression ◽

Texture Feature ◽

Texture Features ◽

Prognostic Biomarkers ◽

Rank Test ◽

Operating Characteristics ◽

Surgical Interventions ◽

Cox Regression Analysis

Objectives. The purpose of this study was to investigate whether texture features from magnetic resonance imaging (MRI) were associated with the overall survival (OS) of anaplastic astrocytoma (AA) patients undergoing surgical treatment. Methods. A total of 51 qualified patients who were diagnosed with AA and underwent surgical interventions in our institution were enrolled in this retrospective study. Patients were followed up for at least 30 months or until death. Texture features derived from histogram-based matrix (HISTO) and grey-level co-occurrence matrix (GLCM) were extracted from preoperative contrast-enhanced T1-weighted images. Each texture feature was dichotomized based on its optimal cutoff value calculated by receiver operating characteristics curve analysis. Kaplan–Meier analysis and log rank test were conducted to compare the 30-month OS between the dichotomized subgroups. Multivariate Cox regression analysis was performed to determine independent prognostic factors. Results. Three HISTO-derived features (HISTO-Energy, HISTO-Entropy, and HISTO-Skewness) and five GLCM-derived features (GLCM-Contrast, GLCM-Energy, GLCM-Entropy, GLCM-Homogeneity, and GLCM-Dissimilarity) were found to be significantly correlated with 30-month OS. Moreover, GLCM-Homogeneity (p=0.001, hazard ratio = 6.351) was suggested to be the independent predictor of the patient survival. Conclusion. MRI-based texture features have the potential to be applied as prognostic biomarkers in AA patients undergoing surgical treatment.

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Evaluation and comparison of cartilage repair tissue of the patella and medial femoral condyle by using morphological MRI and biochemical zonal T2 mapping

European Radiology ◽

10.1007/s00330-008-1249-6 ◽

2008 ◽

Vol 19 (5) ◽

pp. 1253-1262 ◽

Cited By ~ 41

Author(s):

Goetz H. Welsch ◽

Tallal C. Mamisch ◽

Sebastian Quirbach ◽

Lukas Zak ◽

Stefan Marlovits ◽

...

Keyword(s):

Cartilage Repair ◽

T2 Mapping ◽

Femoral Condyle ◽

Medial Femoral Condyle ◽

Repair Tissue

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Relationships of Image Texture Properties with Chewing Activity and Mechanical Properties during Mastication of Bread

International Journal of Food Engineering ◽

10.1515/ijfe-2015-0309 ◽

2016 ◽

Vol 12 (4) ◽

pp. 311-321

Author(s):

Qian Mao ◽

Yonghai Sun ◽

Jumin Hou ◽

Libo Yu ◽

Yang Liu ◽

...

Keyword(s):

Mechanical Properties ◽

Texture Analysis ◽

Texture Features ◽

Image Texture ◽

Emg Activity ◽

Analysis Method ◽

Texture Properties ◽

Property Changes ◽

Occurrence Matrix ◽

Chewing Activity

Abstract The objective of this study was to investigate the relationships of image texture properties with chewing behaviors, and mechanical properties during mastication of bread. Gray-level gradient co-occurrence matrix (GGCM) was used to process the images of boluses. The chewing behaviors were recorded by electromyography (EMG), and the mechanical properties were measured by texture analyzer. The results showed that among the texture features, the inverse difference moment (IDMGGCM) was selected as the main parameter to describe the decomposition of boluses. IDMGGCM was positively related to the weight gain (r = 0.865, p < 0.01), negatively correlated with hardness (r = –0.835, p <0.01) and EMG activity per cycle (r = –0.767, p < 0.01). GGCM is an effective texture analysis method that could correctly identify 70.1–80.8 % of food bolus images to the corresponding chewing cycles. This study provided a new clue for texture analysis of bread bolus images and offered data revealing the bolus property changes during the mastication of bread.

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