Study of Haralick’s and GLCM texture analysis on 3D medical images

Dhruv, Bhawna; Mittal, Neetu; Modi, Megha

doi:10.1080/00207454.2018.1536052

Cited by 40 publications

(25 citation statements)

References 18 publications

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Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

“…The geometry features describe the tumor volume, surface area and shape, and their ratios. The texture features describe the heterogenetic of ROIs based on the gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and local binary pattern (LBP) [24][25][26][27]. In the feature extraction process, the feature aggregation of GLCM and GLRLM values was performed by averaging over 3D directional matrices to improve rotational invariance [28].…”

Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

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Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Liao

Lee

Yang

et al. 2021

Cancers

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The diagnosis of brain metastasis (BM) is commonly observed in non-small cell lung cancer (NSCLC) with poor outcomes. Accordingly, developing an approach to early predict BM response to Gamma Knife radiosurgery (GKRS) may benefit the patient treatment and monitoring. A total of 237 NSCLC patients with BMs (for survival prediction) and 256 patients with 976 BMs (for prediction of local tumor control) treated with GKRS were retrospectively analyzed. All the survival data were recorded without censoring, and the status of local tumor control was determined by comparing the last MRI follow-up in patients’ lives with the pre-GKRS MRI. Overall 1763 radiomic features were extracted from pre-radiosurgical magnetic resonance images. Three prediction models were constructed, using (1) clinical data, (2) radiomic features, and (3) clinical and radiomic features. Support vector machines with a 30% hold-out validation approach were constructed. For treatment outcome predictions, the models derived from both the clinical and radiomics data achieved the best results. For local tumor control, the combined model achieved an area under the curve (AUC) of 0.95, an accuracy of 90%, a sensitivity of 91%, and a specificity of 89%. For patient survival, the combined model achieved an AUC of 0.81, an accuracy of 77%, a sensitivity of 78%, and a specificity of 80%. The pre-radiosurgical radiomics data enhanced the performance of local tumor control and survival prediction models in NSCLC patients with BMs treated with GRKS. An outcome prediction model based on radiomics combined with clinical features may guide therapy in these patients.

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Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Liao

Lee

Yang

et al. 2021

Cancers

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“…In the literature review, it was seen that the dorsal striatum was not analyzed by texture analysis in patients with FND before. In present study, a statistical approach has been adopted that determines the texture information in images based on the gray level distribution of pixels in medical imaging, giving better results than older methods such as structural approaches (Alam & Faruqui, 2011;Dhruv, Mittal, & Modi, 2019;Haralick, 1979;Yildirim & Baykara, 2020).…”

Section: Discussionmentioning

confidence: 99%

Texture analysis of dorsal striatum in functional neurological (conversion) disorder

Baykara

2021

Brain Imaging and Behavior

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In this study, it was aimed to evaluate the dorsal striatum nuclei of patients diagnosed with Functional Neurological Disorder by tissue analysis method from magnetic resonance imaging images and to compare them with healthy controls.Study groups consisted of 20 female patients and 20 healthy women. The brains of patients and controls were scanned for high-resolution images with a 1.5 T scanner using the sagittal plane and 3D spiral fast spin echo sequence. Using the texture analysis method, mean, standard deviation, minimum, maximum, median, variance, entropy, size %L, size %U, size %M, kurtosis, skewness and homogeneity values of the dorsal striatum nuclei were calculated from the images. The data were compared with comparison tests according to Kolmogorov-Smirnov test results.There was no statistically signi cant difference between paired regions in terms of texture analysis ndings in the cross-sectional images of the participants. In patients, mean, standard deviation, minimum, maximum, median, variance and entropy values for the putamen nucleus, and mean, standard deviation, minimum, maximum, median, variance, entropy and kurtosis values for the caudate nucleus were found signi cantly higher than controls. Additional receiver operating characteristic curve and logistic regression analyzes were performed.The implications of the results of the study are that there are signi cant microstructural changes in the dorsal striatum nuclei of patients and their re ection on brain images. Texture analysis is a useful technique to show tissue changes in the dorsal striatum of patients using images. It is highly recommended to use tissue analysis to identify and evaluate potentially affected areas of the brain in new studies.

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“…The form factor encapsulates the shape of the hippocampus. GLCM was characterized by statistical voxels of different directions and step of probability to get a co-occurrence matrix; and then to quantify the distribution of the co-occurrence matrix, the information such as the complexity of the lesion, the level of change, the thickness of texture was described (Dhruv et al, 2019). Haralick provides texture information of samples of GLCM from four directions (0°, 45°, 90°, and 135°) and an offset of 1 to calculate the sum of the means (Haralick et al, 1973).…”

Section: Methodsmentioning

confidence: 99%

Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method

Feng

Song

Wang

et al. 2019

Front. Aging Neurosci.

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Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building the classification models in aMCI diagnosis.Methods: For this target, we recruited 42 subjects with aMCI and 44 normal controls (NC). The right and left hippocampi were segmented for each subject using an efficient learning-based method. Then, the radiomic analysis was applied to calculate and select the radiomic features. Finally, two logistic regression models were built based on the selected features obtained from the right and left hippocampi.Results: There were 385 features derived after calculation, and four features remained after feature selection from each group of data. The area under the receiver operating characteristic (ROC) curve, specificity, sensitivity, positive predictive value, negative predictive value, precision, recall, and F-score of the classification evaluation index of the right hippocampus logistic regression model were 0.76, 0.71, 0.69, 0.69, 0.71, 0.69, 0.69, and 0.69, and those of the left hippocampus model were 0.79, 0.71, 0.54, 0.64, 0.63, 0.64, 0.54, and 0.58, respectively.Conclusion: Results demonstrate the potential hippocampal radiomic biomarkers are valid for the aMCI diagnosis. The MRI-based radiomic analysis, with further improvement and validation, can be used to identify patients with aMCI and guide the individual treatment.

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Study of Haralick’s and GLCM texture analysis on 3D medical images

Cited by 40 publications

References 18 publications

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Texture analysis of dorsal striatum in functional neurological (conversion) disorder

Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method

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