Application of Haralick texture features in brain [&lt;sup&gt;18&lt;/sup&gt;F]-florbetapir positron emission tomography without reference region normalization

Campbell, Desmond L.; Kang, Hakmook; Shokouhi, Sepideh

doi:10.2147/cia.s143307

Cited by 14 publications

(18 citation statements)

References 19 publications

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“…Comparing the findings of the present study to those by Campbell et al [22], the authors reported results that state that energy and entropy best discriminate between the studied groups. While these features generally show statistically significant differences in our study, energy in the [ 18 F]FMM group is also one of the three HFs that are not significant when extracted from the global VOI.…”

Section: Discussionsupporting

confidence: 75%

“…Before GLCM computation, image intensities are quantized into 128 bins using equal probability quantization. A total of 6 HFs are calculated, namely energy, contrast, entropy, homogeneity, correlation, and dissimilarity [24,33], which have previously been used while analyzing amyloid PET images and have shown good performance [22,23]. On the other hand, the SUVRs are obtained by normalizing the average SUV of each region to the average SUV of the grey matter part of the cerebellum [8,14,34].…”

Section: Image Analysismentioning

confidence: 99%

“…Another quantification approach evaluated previously is the textural analysis of amyloid PET images. Campbell et al [22] studied group differences of textural features and SUV ratios (SUVR) of cortical brain regions in patients of different clinical factors. The authors demonstrated statistically significant differences in textural features between the study groups.…”

Section: Introductionmentioning

confidence: 99%

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Texture-Based Analysis of 18F-Labeled Amyloid PET Brain Images

et al. 2021

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Amyloid positron emission tomography (PET) brain imaging with radiotracers like [18F]florbetapir (FBP) or [18F]flutemetamol (FMM) is frequently used for the diagnosis of Alzheimer’s disease. Quantitative analysis is usually performed with standardized uptake value ratios (SUVR), which are calculated by normalizing to a reference region. However, the reference region could present high variability in longitudinal studies. Texture features based on the grey-level co-occurrence matrix, also called Haralick features (HF), are evaluated in this study to discriminate between amyloid-positive and negative cases. A retrospective study cohort of 66 patients with amyloid PET images (30 [18F]FBP and 36 [18F]FMM) was selected and SUVRs and 6 HFs were extracted from 13 cortical volumes of interest. Mann–Whitney U-tests were performed to analyze differences of the features between amyloid positive and negative cases. Receiver operating characteristic (ROC) curves were computed and their area under the curve (AUC) was calculated to study the discriminatory capability of the features. SUVR proved to be the most significant feature among all tests with AUCs between 0.692 and 0.989. All HFs except correlation also showed good performance. AUCs of up to 0.949 were obtained with the HFs. These results suggest the potential use of texture features for the classification of amyloid PET images.

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Section: Discussionsupporting

confidence: 75%

Section: Image Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Texture-Based Analysis of 18F-Labeled Amyloid PET Brain Images

et al. 2021

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“…Haralick texture features extracted from longitudinal 18F-florbetapir PET scans have been used to successfully differentiate between subject groups (eg, normal vs patients with mild cognitive impairment) without normalizing PET intensity using a reference region ( 16 ); it has been shown that first- and higher-order textual features with low-level variations are identified for reproducible solid tumor segmentation using FDG-PET/CT scans ( 17 ). In this study, we have, to the best of our knowledge, shown for the first time that a statistical model (CTI model) combining Haralick texture features computed from F-18 fluciclovine PET/CT images with patients’ clinical information may improve the chances of accurately detecting BCR.…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis of F-18 Fluciclovine PET/CT to Predict Biochemically Recurrent Prostate Cancer: Initial Results

et al. 2020

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Predicting biochemical recurrence of prostate cancer is imperative for initiating early treatment, which can improve the outcome of cancer treatment. However, because of inter- and intrareader variability in interpretation of F-18 fluciclovine positron emission tomography/computed tomography (PET/CT), it is difficult to reliably discern between necrotic tissue owing to radiation therapy and tumor tissue. Our goal is to develop a computational methodology using Haralick texture analysis that can be used as an adjunct tool to improve and standardize the interpretation of F-18 fluciclovine PET/CT to identify biochemical recurrence of prostate cancer. Four main textural features were chosen by variable selection procedure using least absolute shrinkage and selection operator logistic regression and bootstrapping, and then included as predictors in subsequent logistic ridge regression model for prediction (n = 28). Age at prostatectomy, prostate-specific antigen (PSA) level before the PET/CT imaging, and number of days between the prostate-specific antigen measurement and PET/CT imaging were also included in the prediction model. The overfitting-corrected area under the curve and Brier score of the proposed model were 0.94 (95% CI: 0.81, 1.00) and 0.12 (95% CI: 0.03, 0.23), respectively. Compared with a model with textural features (TI model) and that with only clinical information (CI model), the proposed model achieved 2% and 32% increase in AUC and 8% and 48% reduction in Brier score, respectively. Combining Haralick textural features based on the PET/CT imaging data with clinical information shows a high potential of enhanced prediction of the biochemical recurrence of prostate cancer.

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“…Recently, Brynolfsson et al (2017) analyzed Haralick features extracted from apparent diffusion coefficient (ADC) MRI Images and assessed the sensitivity of these features against five image acquisition parameters. Similarly, Campbell et al (2017) also utilized Haralick features in a recent study for textural analysis of brain positron emission tomography (PET) images. In addition to being effectively used in medical textural analysis, Haralick features have also been effectively used in the quality control field ( Corbane et al, 2008 ; Bhandari and Deshpande, 2011 ).…”

Section: Related Workmentioning

confidence: 99%

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning

et al. 2020

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Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development—a process referred to as plant phenotyping—is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyze the data in microscopy images depicting plant growth, either locating or counting regions of cellular features in images. In this paper, we present to the plant community an introduction to and exploration of two machine learning approaches to address the problem of marker localization in confocal microscopy. First, a comparative study is conducted on the classification accuracy of common conventional machine learning algorithms, as a means to highlight challenges with these methods. Second, a 3D (volumetric) deep learning approach is developed and presented, including consideration of appropriate loss functions and training data. A qualitative and quantitative analysis of all the results produced is performed. Evaluation of all approaches is performed on an unseen time-series sequence comprising several individual 3D volumes, capturing plant growth. The comparative analysis shows that the deep learning approach produces more accurate and robust results than traditional machine learning. To accompany the paper, we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (FIJI) software. Network models and example datasets will also be available online.

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Application of Haralick texture features in brain [<sup>18</sup>F]-florbetapir positron emission tomography without reference region normalization

Cited by 14 publications

References 19 publications

Texture-Based Analysis of 18F-Labeled Amyloid PET Brain Images

Texture-Based Analysis of 18F-Labeled Amyloid PET Brain Images

Texture Analysis of F-18 Fluciclovine PET/CT to Predict Biochemically Recurrent Prostate Cancer: Initial Results

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning

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