Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

Djuričić, Goran; Radulović, Marko; Sopta, Jelena; Nikitović, Marina; Milošević, Nebojša T.

doi:10.3389/fonc.2017.00246

Cited by 20 publications

(29 citation statements)

References 21 publications

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“…Finally, the results of the Kolmogorov-Smirnov test were used as criterion if data were normally distributed (Table 3). In previous publications it has been shown that some parameters of the image can describe similar [25] or complementary [17] properties. Therefore, we tested possible relationship between two parameters of monofractal (D bin and Λ) and textural (S IDM and S E ) analysis.…”

Section: Resultsmentioning

confidence: 99%

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Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis

Vranes¹,

Krstonošić²,

Milošević³

2019

EJB

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The striatum (neostriatum) is one of the principal constituents of the basal nuclei. It is a complex structure which consists of a dorsal and the ventral components. According to the spine distribution and their density, neurons of the human striatum can be classified into two main types: spiny and aspiny cells. Further classification recognizes two groups of spiny, and three groups of aspiny neurons. The goal of this study was to analyze different morphometric properties of the digital images of the group IV and group V aspiny neurons, from the dorsal striatum of both cerebral hemispheres. In this study, a total of 175 two-dimensional images of aspiny neurons were analyzed. Image reconstruction and measurement was performed with the specialized, public software Image J. Four parameters of standard fractal analysis were quantified from these binary images. In addition, five textural parameters were obtained by analyzing grayscale images of the entire neuron. Results of both analyses show that six of nine parameters differed between the group IV and group V aspiny neurons. Moreover, in both groups of neurons, one parameter of the fractal and three of the texture analyses differed between the putamen and the caudate nucleus neurons. Thus, this study corroborates previous classification of aspiny neurons. Although they belong to the same aspiny group, different type of cells can qualify nerve signals in their own way. Therefore, this study supports the hypothesis that neuronal morphology differences can reflect their functional diversity and their role in communication.

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Section: Resultsmentioning

confidence: 99%

“…The term N g is the number of gray levels in the image. Their mathematical definitions and detailed explanations can be found in [17]. The meaning of all parameters (fractal and textural) used in this investigation are summarized in Table 2.…”

Section: Quantification Of the Grayscale Imagementioning

confidence: 99%

Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis

Vranes¹,

Krstonošić²,

Milošević³

2019

EJB

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“…Five GLCM features were calculated: angular second moment (ASM), inverse difference moment (IDM), contrast, correlation, and entropy using the GLCM Texture plugin for ImageJ v1.52u as previously explained in detail [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

Vranes

Vujasinović

Rajković

et al. 2020

IJMS

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Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.

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“…The GLCM calculates how often a pixel with gray-level (grayscale intensity) value i occurs either horizontally, vertically, or diagonally to adjacent pixels with the value j [39]. GLCM indices were computed using ImageJ 1.5.2 software (Nationa Institute of Health, Bethesda, MD, USA), by use of the GLCM TextureToo plugin [40,41].…”

Section: Glcm Analysismentioning

confidence: 99%

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

et al. 2019

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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.

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Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

Cited by 20 publications

References 21 publications

Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis

Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

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

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