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Keywords = Haralick texture analysis

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23 pages, 5770 KiB  
Article
Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images
by Diego Andrade, Howard C. Gifford and Mini Das
Tomography 2025, 11(8), 87; https://doi.org/10.3390/tomography11080087 (registering DOI) - 31 Jul 2025
Abstract
Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. [...] Read more.
Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article
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16 pages, 2228 KiB  
Article
Potential Use of a New Energy Vision (NEV) Camera for Diagnostic Support of Carpal Tunnel Syndrome: Development of a Decision-Making Algorithm to Differentiate Carpal Tunnel-Affected Hands from Controls
by Dror Robinson, Mohammad Khatib, Mohammad Eissa and Mustafa Yassin
Diagnostics 2025, 15(11), 1417; https://doi.org/10.3390/diagnostics15111417 - 3 Jun 2025
Viewed by 474
Abstract
Introduction: Carpal Tunnel Syndrome (CTS) is a prevalent neuropathy requiring accurate, non-invasive diagnostics to minimize patient burden. This study evaluates the New Energy Vision (NEV) camera, an RGB-based multispectral imaging tool, to detect CTS through skin texture and color analysis, developing a machine [...] Read more.
Introduction: Carpal Tunnel Syndrome (CTS) is a prevalent neuropathy requiring accurate, non-invasive diagnostics to minimize patient burden. This study evaluates the New Energy Vision (NEV) camera, an RGB-based multispectral imaging tool, to detect CTS through skin texture and color analysis, developing a machine learning algorithm to distinguish CTS-affected hands from controls. Methods: A two-part observational study included 103 participants (50 controls, 53 CTS patients) in Part 1, using NEV camera images to train a Support Vector Machine (SVM) classifier. Part 2 compared median nerve-damaged (MED) and ulnar nerve-normal (ULN) palm areas in 32 CTS patients. Validations included nerve conduction tests (NCT), Semmes–Weinstein monofilament testing (SWMT), and Boston Carpal Tunnel Questionnaire (BCTQ). Results: The SVM classifier achieved 93.33% accuracy (confusion matrix: [[14, 1], [1, 14]]), with 81.79% cross-validation accuracy. Part 2 identified significant differences (p < 0.05) in color proportions (e.g., red_proportion) and Haralick texture features between MED and ULN areas, corroborated by BCTQ and SWMT. Conclusions: The NEV camera, leveraging multispectral imaging, offers a promising non-invasive CTS diagnostic tool using detection of nerve-related skin changes. Further validation is needed for clinical adoption. Full article
(This article belongs to the Special Issue New Trends in Musculoskeletal Imaging)
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12 pages, 693 KiB  
Article
Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI
by Dang Bich Thuy Le, Ram Narayanan, Meredith Sadinski, Aleksandar Nacev, Yuling Yan and Srirama S. Venkataraman
Bioengineering 2025, 12(1), 47; https://doi.org/10.3390/bioengineering12010047 - 9 Jan 2025
Viewed by 957
Abstract
This study evaluates the feasibility of using Haralick texture analysis on low-field, T2-weighted MRI images for detecting prostate cancer, extending current research from high-field MRI to the more accessible and cost-effective low-field MRI. A total of twenty-one patients with biopsy-proven prostate cancer (Gleason [...] Read more.
This study evaluates the feasibility of using Haralick texture analysis on low-field, T2-weighted MRI images for detecting prostate cancer, extending current research from high-field MRI to the more accessible and cost-effective low-field MRI. A total of twenty-one patients with biopsy-proven prostate cancer (Gleason score 4+3 or higher) were included. Before transperineal biopsy guided by low-field (58–74mT) MRI, a radiologist annotated suspicious regions of interest (ROIs) on high-field (3T) MRI. Rigid image registration was performed to align corresponding regions on both high- and low-field images, ensuring an accurate propagation of annotations to the co-registered low-field images for texture feature calculations. For each cancerous ROI, a matching ROI of identical size was drawn in a non-suspicious region presumed to be normal tissue. Four Haralick texture features (Energy, Correlation, Contrast, and Homogeneity) were extracted and compared between cancerous and non-suspicious ROIs. Two extraction methods were used: the direct computation of texture measures within the ROIs and a sliding window technique generating texture maps across the prostate from which average values were derived. The results demonstrated statistically significant differences in texture features between cancerous and non-suspicious regions. Specifically, Energy and Homogeneity were elevated (p-values: <0.00001–0.004), while Contrast and Correlation were reduced (p-values: <0.00001–0.03) in cancerous ROIs. These findings suggest that Haralick texture features are both feasible and informative for differentiating abnormalities, offering promise in assisting prostate cancer detection on low-field MRI. Full article
(This article belongs to the Special Issue Advancements in Medical Imaging Technology)
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23 pages, 5776 KiB  
Article
Estimating the Workability of Concrete with a Stereovision Camera during Mixing
by Teemu Ojala and Jouni Punkki
Sensors 2024, 24(14), 4472; https://doi.org/10.3390/s24144472 - 10 Jul 2024
Cited by 2 | Viewed by 1330
Abstract
The correct workability of concrete is an essential parameter for its placement and compaction. However, an absence of automatic and transparent measurement methods to estimate the workability of concrete hinders the adaptation from laborious traditional methods such as the slump test. In this [...] Read more.
The correct workability of concrete is an essential parameter for its placement and compaction. However, an absence of automatic and transparent measurement methods to estimate the workability of concrete hinders the adaptation from laborious traditional methods such as the slump test. In this paper, we developed a machine-learning framework for estimating the slump class of concrete in the mixer using a stereovision camera. Depth data from five different slump classes was transformed into Haralick texture features to train several machine-learning classifiers. The best-performing classifier achieved a multiclass classification accuracy of 0.8179 with the XGBoost algorithm. Furthermore, we found through statistical analysis that while the denoising of depth data has little effect on the accuracy, the feature extraction of mixer blades and the choice of region of interest significantly increase the accuracy and the efficiency of the classifiers. The proposed framework shows robust results, indicating that stereovision is a competitive solution to estimate the workability of concrete during concrete production. Full article
(This article belongs to the Section Sensing and Imaging)
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20 pages, 5700 KiB  
Article
Relating Macroscopic PET Radiomics Features to Microscopic Tumor Phenotypes Using a Stochastic Mathematical Model of Cellular Metabolism and Proliferation
by Hailey S. H. Ahn, Yas Oloumi Yazdi, Brennan J. Wadsworth, Kevin L. Bennewith, Arman Rahmim and Ivan S. Klyuzhin
Cancers 2024, 16(12), 2215; https://doi.org/10.3390/cancers16122215 - 13 Jun 2024
Cited by 1 | Viewed by 1607
Abstract
Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful [...] Read more.
Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that “cluster shade” and “difference entropy” are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5–4 resolution units, corresponding to 8.7–10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response. Full article
(This article belongs to the Special Issue PET/CT in Cancers Outcomes Prediction)
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17 pages, 39975 KiB  
Article
A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
by Jose Dixon, Oluwatunmise Akinniyi, Abeer Abdelhamid, Gehad A. Saleh, Md Mahmudur Rahman and Fahmi Khalifa
Algorithms 2024, 17(6), 221; https://doi.org/10.3390/a17060221 - 21 May 2024
Cited by 18 | Viewed by 3695
Abstract
The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture [...] Read more.
The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture for improved brain tumor classification. We introduce a hybrid architecture that integrates vision transformer (ViT) and deep neural networks to create an ensemble classifier, resulting in a more robust brain tumor classification framework. The analysis pipeline begins with preprocessing and data normalization, followed by extracting three types of MRI-derived information-rich features. The latter included higher-order texture and structural feature sets to harness the spatial interactions between image intensities, which were derived using Haralick features and local binary patterns. Additionally, local deeper features of the brain images are extracted using an optimized convolutional neural networks (CNN) architecture. Finally, ViT-derived features are also integrated due to their ability to handle dependencies across larger distances while being less sensitive to data augmentation. The extracted features are then weighted, fused, and fed to a machine learning classifier for the final classification of brain MRIs. The proposed weighted ensemble architecture has been evaluated on publicly available and locally collected brain MRIs of four classes using various metrics. The results showed that leveraging the benefits of individual components of the proposed architecture leads to improved performance using ablation studies. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis)
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15 pages, 3572 KiB  
Article
Surface Properties of a Biocompatible Thermoplastic Polyurethane and Its Anti-Adhesive Effect against E. coli and S. aureus
by Elisa Restivo, Emanuela Peluso, Nora Bloise, Giovanni Lo Bello, Giovanna Bruni, Marialaura Giannaccari, Roberto Raiteri, Lorenzo Fassina and Livia Visai
J. Funct. Biomater. 2024, 15(1), 24; https://doi.org/10.3390/jfb15010024 - 15 Jan 2024
Cited by 9 | Viewed by 4313
Abstract
Thermoplastic polyurethane (TPU) is a polymer used in a variety of fields, including medical applications. Here, we aimed to verify if the brush and bar coater deposition techniques did not alter TPU properties. The topography of the TPU-modified surfaces was studied via AFM [...] Read more.
Thermoplastic polyurethane (TPU) is a polymer used in a variety of fields, including medical applications. Here, we aimed to verify if the brush and bar coater deposition techniques did not alter TPU properties. The topography of the TPU-modified surfaces was studied via AFM demonstrating no significant differences between brush and bar coater-modified surfaces, compared to the un-modified TPU (TPU Film). The effect of the surfaces on planktonic bacteria, evaluated by MTT assay, demonstrated their anti-adhesive effect on E. coli, while the bar coater significantly reduced staphylococcal planktonic adhesion and both bacterial biofilms compared to other samples. Interestingly, Pearson’s R coefficient analysis showed that Ra roughness and Haralick’s correlation feature were trend predictors for planktonic bacterial cells adhesion. The surface adhesion property was evaluated against NIH-3T3 murine fibroblasts by MTT and against human fibrinogen and human platelet-rich plasma by ELISA and LDH assay, respectively. An indirect cytotoxicity experiment against NIH-3T3 confirmed the biocompatibility of the TPUs. Overall, the results indicated that the deposition techniques did not alter the antibacterial and anti-adhesive surface properties of modified TPU compared to un-modified TPU, nor its bio- and hemocompatibility, confirming the suitability of TPU brush and bar coater films in the biomedical and pharmaceutical fields. Full article
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19 pages, 8112 KiB  
Article
Combined Effects of HA Concentration and Unit Cell Geometry on the Biomechanical Behavior of PCL/HA Scaffold for Tissue Engineering Applications Produced by LPBF
by Maria Laura Gatto, Michele Furlani, Alessandra Giuliani, Marcello Cabibbo, Nora Bloise, Lorenzo Fassina, Marlena Petruczuk, Livia Visai and Paolo Mengucci
Materials 2023, 16(14), 4950; https://doi.org/10.3390/ma16144950 - 11 Jul 2023
Cited by 3 | Viewed by 1918
Abstract
This experimental study aims at filling the gap in the literature concerning the combined effects of hydroxyapatite (HA) concentration and elementary unit cell geometry on the biomechanical performances of additively manufactured polycaprolactone/hydroxyapatite (PCL/HA) scaffolds for tissue engineering applications. Scaffolds produced by laser powder [...] Read more.
This experimental study aims at filling the gap in the literature concerning the combined effects of hydroxyapatite (HA) concentration and elementary unit cell geometry on the biomechanical performances of additively manufactured polycaprolactone/hydroxyapatite (PCL/HA) scaffolds for tissue engineering applications. Scaffolds produced by laser powder bed fusion (LPBF) with diamond (DO) and rhombic dodecahedron (RD) elementary unit cells and HA concentrations of 5, 30 and 50 wt.% were subjected to structural, mechanical and biological characterization to investigate the biomechanical and degradative behavior from the perspective of bone tissue regeneration. Haralick’s features describing surface pattern, correlation between micro- and macro-structural properties and human mesenchymal stem cell (hMSC) viability and proliferation have been considered. Experimental results showed that HA has negative influence on scaffold compaction under compression, while on the contrary it has a positive effect on hMSC adhesion. The unit cell geometry influences the mechanical response in the plastic regime and also has an effect on the cell proliferation. Finally, both HA concentration and elementary unit cell geometry affect the scaffold elastic deformation behavior as well as the amount of micro-porosity which, in turn, influences the scaffold degradation rate. Full article
(This article belongs to the Special Issue Biomaterials for Bone Tissue Engineering (Second Edition))
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17 pages, 768 KiB  
Article
Multiscale Analysis for Improving Texture Classification
by Steve Tsham Mpinda Ataky, Diego Saqui, Jonathan de Matos, Alceu de Souza Britto Junior and Alessandro Lameiras Koerich
Appl. Sci. 2023, 13(3), 1291; https://doi.org/10.3390/app13031291 - 18 Jan 2023
Cited by 10 | Viewed by 2535
Abstract
Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian–Laplacian pyramid to separately treat different spatial frequency [...] Read more.
Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian–Laplacian pyramid to separately treat different spatial frequency bands of a texture. First, we generate three images corresponding to three levels of the Gaussian–Laplacian pyramid for an input image to capture intrinsic details. Then, we aggregate features extracted from gray and color texture images using bioinspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix feature descriptors, and Haralick statistical feature descriptors into a single feature vector. Such an aggregation aims at producing features that characterize textures to their maximum extent, unlike employing each descriptor separately, which may lose some relevant textural information and reduce the classification performance. The experimental results on texture and histopathologic image datasets have shown the advantages of the proposed method compared to state-of-the-art approaches. Such findings emphasize the importance of multiscale image analysis and corroborate that the descriptors mentioned above are complementary. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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18 pages, 3460 KiB  
Article
Early Osteogenic Marker Expression in hMSCs Cultured onto Acid Etching-Derived Micro- and Nanotopography 3D-Printed Titanium Surfaces
by Nora Bloise, Erik I. Waldorff, Giulia Montagna, Giovanna Bruni, Lorenzo Fassina, Samuel Fang, Nianli Zhang, Jiechao Jiang, James T. Ryaby and Livia Visai
Int. J. Mol. Sci. 2022, 23(13), 7070; https://doi.org/10.3390/ijms23137070 - 25 Jun 2022
Cited by 14 | Viewed by 3010
Abstract
Polyetheretherketone (PEEK) titanium composite (PTC) is a novel interbody fusion device that combines a PEEK core with titanium alloy (Ti6Al4V) endplates. The present study aimed to investigate the in vitro biological reactivity of human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) to micro- and nanotopographies [...] Read more.
Polyetheretherketone (PEEK) titanium composite (PTC) is a novel interbody fusion device that combines a PEEK core with titanium alloy (Ti6Al4V) endplates. The present study aimed to investigate the in vitro biological reactivity of human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) to micro- and nanotopographies produced by an acid-etching process on the surface of 3D-printed PTC endplates. Optical profilometer and scanning electron microscopy were used to assess the surface roughness and identify the nano-features of etched or unetched PTC endplates, respectively. The viability, morphology and the expression of specific osteogenic markers were examined after 7 days of culture in the seeded cells. Haralick texture analysis was carried out on the unseeded endplates to correlate surface texture features to the biological data. The acid-etching process modified the surface roughness of the 3D-printed PTC endplates, creating micro- and nano-scale structures that significantly contributed to sustaining the viability of hBM-MSCs and triggering the expression of early osteogenic markers, such as alkaline phosphatase activity and bone-ECM protein production. Finally, the topography of 3D-printed PTC endplates influenced Haralick’s features, which in turn correlated with the expression of two osteogenic markers, osteopontin and osteocalcin. Overall, these data demonstrate that the acid-etching process of PTC endplates created a favourable environment for osteogenic differentiation of hBM-MSCs and may potentially have clinical benefit. Full article
(This article belongs to the Section Materials Science)
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14 pages, 1390 KiB  
Article
FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN)
by Farideh Bazangani, Frédéric J. P. Richard, Badih Ghattas and Eric Guedj
Sensors 2022, 22(12), 4640; https://doi.org/10.3390/s22124640 - 20 Jun 2022
Cited by 13 | Viewed by 3826
Abstract
Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due [...] Read more.
Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and data privacy, finding an appropriate dataset (balanced, enough samples, etc.) is quite a challenge. Although image synthesis could be beneficial to overcome this issue, synthesizing 3D images is a hard task. The main objective of this paper is to generate 3D T1 weighted MRI corresponding to FDG-PET. In this study, we propose a separable convolution-based Elicit generative adversarial network (E-GAN). The proposed architecture can reconstruct 3D T1 weighted MRI from 2D high-level features and geometrical information retrieved from a Sobel filter. Experimental results on the ADNI datasets for healthy subjects show that the proposed model improves the quality of images compared with the state of the art. In addition, the evaluation of E-GAN and the state of art methods gives a better result on the structural information (13.73% improvement for PSNR and 22.95% for SSIM compared to Pix2Pix GAN) and textural information (6.9% improvements for homogeneity error in Haralick features compared to Pix2Pix GAN). Full article
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23 pages, 2607 KiB  
Article
Method of Biomass Discrimination for Fast Assessment of Calorific Value
by Jarosław Gocławski, Ewa Korzeniewska, Joanna Sekulska-Nalewajko, Paweł Kiełbasa and Tomasz Dróżdż
Energies 2022, 15(7), 2514; https://doi.org/10.3390/en15072514 - 29 Mar 2022
Cited by 12 | Viewed by 2142
Abstract
Crop byproducts are alternatives to nonrenewable energy resources. Burning biomass results in lower emission of undesirable nitrogen and sulfur oxides and contributes no significant greenhouse effect. There is a diverse range of energy-useful biomass, including in terms of calorific value. This article presents [...] Read more.
Crop byproducts are alternatives to nonrenewable energy resources. Burning biomass results in lower emission of undesirable nitrogen and sulfur oxides and contributes no significant greenhouse effect. There is a diverse range of energy-useful biomass, including in terms of calorific value. This article presents a new method of discriminating biomass, and of determining its calorific value. The method involves extracting the selected texture features on the surface of a briquette from a microscopic image and then classifying them using supervised classification methods. The fractal dimension, local binary pattern (LBP), and Haralick features are computed and then classified by linear discrimination analysis (LDA). The discrimination results are compared with the results obtained by random forest (RF) and deep neural network (DNN) type classifiers. This approach is superior in terms of complexity and operating time to other methods such as, for instance, the calorimetric method or analysis of the chemical composition of elements in a sample. In the normal operation mode, our method identifies the calorific value in the time of about 100 s, i.e., 90 times faster than traditional combustion of material samples. In predicting from a single sample image, the overall average accuracy of 95% was achieved for all tested classifiers. The authors’ idea to use ten input images of the same material and then majority voting after classification increases the discrimination system accuracy above 99%. Full article
(This article belongs to the Special Issue Thermal and Combustion Applications)
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21 pages, 10251 KiB  
Article
Automatic Gemstone Classification Using Computer Vision
by Bona Hiu Yan Chow and Constantino Carlos Reyes-Aldasoro
Minerals 2022, 12(1), 60; https://doi.org/10.3390/min12010060 - 31 Dec 2021
Cited by 33 | Viewed by 11105
Abstract
This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local [...] Read more.
This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local binary pattern, Haralick texture and grey-level co-occurrence matrix properties) were used in combination with different machine-learning algorithms (Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour, Decision Tree, Random Forest, Naive Bayes and Support Vector Machine). Deep-learning classification with ResNet-18 and ResNet-50 was also investigated. The optimal combination was provided by a Random Forest algorithm with the RGB eight-bin colour histogram and local binary pattern features, with an accuracy of 69.4% on unseen images; the algorithms required 0.0165 s to process the 284 test images. These results were compared against three expert gemmologists with at least 5 years of experience in gemstone identification, who obtained accuracies between 42.6% and 66.9% and took 42–175 min to classify the test images. As expected, the human experts took much longer than the computer vision algorithms, which in addition provided, albeit marginal, higher accuracy. Although these experiments included a relatively low number of images, the superiority of computer vision over humans is in line with what has been reported in other areas of study, and it is encouraging to further explore the application in gemmology and related areas. Full article
(This article belongs to the Special Issue Colours in Minerals and Rocks)
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11 pages, 638 KiB  
Article
Texture-Based Analysis of 18F-Labeled Amyloid PET Brain Images
by Alexander P. Seiffert, Adolfo Gómez-Grande, Eva Milara, Sara Llamas-Velasco, Alberto Villarejo-Galende, Enrique J. Gómez and Patricia Sánchez-González
Appl. Sci. 2021, 11(5), 1991; https://doi.org/10.3390/app11051991 - 24 Feb 2021
Cited by 1 | Viewed by 2100
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image Processing)
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17 pages, 6709 KiB  
Article
Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results
by Charlems Alvarez-Jimenez, Alvaro A. Sandino, Prateek Prasanna, Amit Gupta, Satish E. Viswanath and Eduardo Romero
Cancers 2020, 12(12), 3663; https://doi.org/10.3390/cancers12123663 - 7 Dec 2020
Cited by 49 | Viewed by 5105
Abstract
(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes [...] Read more.
(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas. Full article
(This article belongs to the Special Issue Radiomics and Cancers)
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