Fractal analysis and Gray level co-occurrence matrix method for evaluation of reperfusion injury in kidney medulla

2016 ◽  
Vol 397 ◽  
pp. 61-67 ◽  
Author(s):  
Igor Pantic ◽  
Zorica Nesic ◽  
Jovana Paunovic Pantic ◽  
Sanja Radojević-Škodrić ◽  
Mila Cetkovic ◽  
...  
Keyword(s):  
Reperfusion Injury ◽  
Fractal Analysis ◽  
Matrix Method ◽  
Gray Level ◽  
Kidney Medulla ◽  
Occurrence Matrix

scholarly journals Analysis of vascular architecture and parenchymal damage generated by reduced blood perfusion in decellularized porcine kidneys using a gray level co-occurrence matrix

2021 ◽  
Author(s):  
Igor V. Pantic ◽  
Adeeba Shakeel ◽  
Georg A Petroianu ◽  
Peter R Corridon
Keyword(s):  
Wavelet Coefficient ◽  
Blood Perfusion ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Kidney Medulla ◽  
Vascular Architecture ◽  
Second Moment ◽  
Arterial Perfusion ◽  
Occurrence Matrix ◽  
Parenchymal Damage

There is no cure for kidney failure, but a bioartificial kidney may help address this global problem. Decellularization provides a promising platform to generate transplantable organs. However, maintaining a viable vasculature is a significant challenge to this technology. Even though angiography offers a valuable way to assess scaffold structure/function, subtle changes are overlooked by specialists. In recent years, innovative image analysis methods in radiology have been suggested to detect and identify subtle changes in tissue architecture. The aim of our research was to apply one of these methods based on a gray level co-occurrence matrix (GLCM) computational algorithm in the analysis of vascular architecture and parenchymal damage generated by hypoperfusion in decellularized porcine. Perfusion decellularization of the whole porcine kidneys was performed using previously established protocols. We analyzed and compared angiograms of kidneys subjected to pathophysiological arterial perfusion of whole blood. For regions of interest (ROIs) covering kidney medulla and the main elements of the vascular network, five major GLCM features were calculated: angular second moment as an indicator of textural uniformity, inverse difference moment as an indicator of textural homogeneity, GLCM contrast, GLCM correlation, and sum variance of the co-occurrence matrix. In addition to GLCM, we also performed discrete wavelet transform analysis of angiogram ROIs by calculating the respective wavelet coefficient energies using high and low-pass filtering. We report statistically significant changes in GLCM and wavelet features, including the reduction of the angular second moment and inverse difference moment, indicating a substantial rise in angiogram textural heterogeneity. Our findings suggest that the GLCM method can be successfully used as an addition to conventional fluoroscopic angiography analyses of micro/macrovascular integrity following in vitro blood perfusion to investigate scaffold integrity. This approach is the first step toward developing an automated network that can detect changes in the decellularized vasculature.

Photogrammetric system for tree measurement based on gray-level co-occurrence matrix method

2009 ◽  
Author(s):  
Xiaolan Wu ◽  
Lin Gao ◽  
Jiangming Kan ◽  
Wenbin Li
Keyword(s):  
Matrix Method ◽  
Gray Level ◽  
Occurrence Matrix

scholarly journals Recent Deforestation Pattern Changes (2000–2017) in the Central Carpathians: A Gray-Level Co-Occurrence Matrix and Fractal Analysis Approach

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 308 ◽  
Author(s):  
Ana-Maria Ciobotaru ◽  
Ion Andronache ◽  
Helmut Ahammer ◽  
Herbert F. Jelinek ◽  
Marko Radulovic ◽  
...  
Keyword(s):  
Fractal Analysis ◽  
Sustainable Forest Management ◽  
Matrix Analysis ◽  
Tree Cover ◽  
Gray Level ◽  
Forest Change ◽  
Landsat 7 ◽  
Occurrence Matrix ◽  
Central Carpathians ◽  
Fractal Fragmentation

The paper explores the distribution of tree cover and deforested areas in the Central Carpathians in the central-east part of Romania, in the context of the anthropogenic forest disturbances and sustainable forest management. The study aims to evaluate the spatiotemporal changes in deforested areas due to human pressure in the Carpathian Mountains, a sensitive biodiverse European ecosystem. We used an analysis of satellite imagery with Landsat-7 Enhanced Thematic Mapper Plus (Landsat-7 ETM+) from the University of Maryland (UMD) Global Forest Change (GFC) dataset. The workflow started with the determination of tree cover and deforested areas from 2000–2017, with an overall accuracy of 97%. For the monitoring of forest dynamics, a Gray-Level Co-occurrence Matrix analysis (Entropy) and fractal analysis (Fractal Fragmentation-Compaction Index and Tug-of-War Lacunarity) were utilized. The increased fragmentation of tree cover (annually 2000–2017) was demonstrated by the highest values of the Fractal Fragmentation-Compaction Index, a measure of the degree of disorder (Entropy) and heterogeneity (Lacunarity). The principal outcome of the research reveals the dynamics of disturbance of tree cover and deforested areas expressed by the textural and fractal analysis. The results obtained can be used in the future development and adaptation of forestry management policies to ensure sustainable management of exploited forest areas.

scholarly journals Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

2019 ◽  
Vol 11 (24) ◽  
pp. 6927 ◽  
Author(s):  
Ana-Maria Ciobotaru ◽  
Ion Andronache ◽  
Helmut Ahammer ◽  
Marko Radulovic ◽  
Daniel Peptenatu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Fractal Analysis ◽  
Forest Dynamics ◽  
Agricultural Land ◽  
Probability Distributions ◽  
Quantitative Information ◽  
Matrix Analysis ◽  
Gray Level ◽  
Occurrence Matrix ◽  
Matrix Evolution

The mountain ecosystems face significant damage from deforestation and environmental forest changes. We investigated the evolution of tree types of cover areas, deforested areas and total deforested areas from Curvature Carpathians using Gray-Level Co-occurrence Matrix and fractal analysis. The forest dynamics mapping was one of the main objectives of this study and it was carried out using multiple fractal and GLCM indices. We approached the analysis of satellite forest images by calculation of four fractal indices such as Pyramid dimension, Cube Counting Dimension, Fractal Fragmentation-Compaction Index and Tug-of-War lacunarity. We also calculated fractal dimension because it is an index of complexity comparing how the detail in a pattern changes with the scale at which it is measured. Fractal dimension is useful for estimation of irregularity or roughness of fractal and natural objects that do not conform to Euclidian geometry. While the fractal dimension quantifies how much space is occupied, the Tug-of-War lacunarity complements fractal dimension with its ability to quantify how space is occupied. Analysis was further supplemented by the Gray-Level Co-occurrence Matrix analysis because it quantifies spatial probability distributions of gray level values between pixel pairs within an image. The calculated Gray-Level Co-occurrence Matrix features included Angular Second Moment, Contrast, Correlation, Inverse Difference Moment and Entropy. Such comprehensive analysis has the advantage of combining fractal analysis that extracts quantitative information about the morphological complexity of the image with the spatial distribution of the gray pixel intensities as calculated by the co-occurrence features provided by Gray-Level Co-occurrence Matrix. Evolution of deforested areas, expansion of agricultural land and the increased demand for quality timber have affected the forests ecosystems and, the regional sustainable development of local communities.

scholarly journals A Novel Statistical Based Methodology for the Feature Extraction of both MRI and CT images

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1788-1794
Keyword(s):  
Feature Extraction ◽  
Statistical Method ◽  
Principle Component Analysis ◽  
Matrix Method ◽  
Ct Images ◽  
Gray Level ◽  
Common Features ◽  
Principle Component ◽  
The Common ◽  
Occurrence Matrix

Design of a common methodology for the diagnosis of different image types is the objective of the work presented in this paper. The software is developed and can be used to diagnose MRI and CT images by the laboratory technician The paper presents a statistical method for the feature extraction of MRI and CT images. About thirteen features are extracted using the methodology adopted for the proposed work. The thirteen features are based on texture, shape and intensity. The data dimensionality is reduced using the Principle Component Analysis (PCA). The common features are extracted using the Gray level co-occurrence matrix method. The software is developed using MATLAB and PYTHON for IoT support.

scholarly journals Flower Classification using Texture and Color Features

10.29007/6mt1 ◽  
2018 ◽  
Author(s):  
Riddhi Shaparia ◽  
Narendra Patel ◽  
Zankhana Shah
Keyword(s):  
Neural Network ◽  
Matrix Method ◽  
Texture Feature ◽  
Noise Removal ◽  
Gray Level ◽  
Neural Network Classifier ◽  
Color Features ◽  
Segmented Images ◽  
Occurrence Matrix ◽  
Color Moment

In this research paper, we have used texture and color features for flower classification. Standard database of flowers have used for experiments. The pre- processing like noise removal and segmentation for elimination of background are apply on input images. Texture and color features are extracted from the segmented images. Texture feature is extracted using GLCM (Gray Level Co-occurrence Matrix) method and color feature is extracted using Color moment. For classification, neural network classifier is used. The overall accuracy of the system is 95.0 %.

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