20 results on '"Astaraki, Mehdi"'
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2. Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
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Astaraki, Mehdi, Zakko, Yousuf, Toma Dasu, Iuliana, Smedby, Örjan, and Wang, Chunliang
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- 2021
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3. Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
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Astaraki, Mehdi, Wang, Chunliang, Buizza, Giulia, Toma-Dasu, Iuliana, Lazzeroni, Marta, and Smedby, Örjan
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- 2019
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4. 2766: Automatic segmentation method for OARs and GTVs delineation for nasopharyngeal carcinoma treatments
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Astaraki, Mehdi and Toma-Dasu, Iuliana
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- 2024
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5. 2418: Role of modeled high-grade glioma cell invasion on tumor progression prediction after radiotherapy
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Häger, Wille, Lazzeroni, Marta, Astaraki, Mehdi, and Toma-Dasu, Iuliana
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- 2024
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6. 1119: Radiomics and deep learning for glioma treatment outcome prediction based on tumor invasion modeling
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Astaraki, Mehdi, Häger, Wille, Lazzeroni, Marta, and Toma-Dasu, Iuliana
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- 2024
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7. Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy
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Astaraki, Mehdi, Severgnini, Mara, Milan, Vittorino, Schiattarella, Anna, Ciriello, Francesca, de Denaro, Mario, Beorchia, Aulo, and Aslian, Hossein
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- 2018
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8. Advanced Machine Learning Methods for Oncological Image Analysis
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Astaraki, Mehdi
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Machine Learning ,Medical Image Processing ,Deep Learning ,Medicinsk bildbehandling ,Medical Image Analysis ,Tumor Segmentation ,Tumor Classification ,Survival Analysis ,Early Response Assessment - Abstract
Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally-invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head-neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra-dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis. Cancer är en global hälsoutmaning som uppskattas ansvara för cirka 10 miljoner dödsfall i hela världen, bara under året 2020. Framsteg inom medicinsk bildtagning och hårdvaruutveckling de senaste tre decennierna har banat vägen för moderna medicinska bildgivande system vars upplösningsförmåga tillåter att fånga information om tumörers anatomi, fysiologi, funktion samt metabolism. Medicinsk bildanalys har därför fått en mer betydelserik roll i klinikers dagliga rutiner inom onkologin, för bland annat screening, diagnostik, uppföljning av behandling samt icke-invasiv utvärdering av sjukdomsprognoser. Sjukvårdens behov av medicinska bilder har lett till att det nu på sjukhusen finns en enorm mängd medicinska bilder på alla moderna sjukhus. Med hänsyn till den viktiga roll medicinsk bilddata spelar i dagens sjukvård, samt den mängd manuellt arbete som behöver göras för att analysera den mängd data som genereras varje dag, så har utvecklingen av digitala verktyg för att för att automatiskt eller semi-automatiskt analysera bilddatan alltid haft stort intresse. Därför har en rad maskininlärningsverktyg utvecklats för analys av onkologisk data, för att gripa sig an läkares repetitiva vardagssysslor. Den här avhandlingen syftar att bidra till fältet “onkologisk bildanalys” genom att föreslå nya sätt att kvantifiera tumörers egenskaper från medicinsk bilddata. Specifikt, är denna avhandling baserad på sex artiklar där de första två har fokus att presentera nya metoder för segmentering av tumörer, och de resterande fyra ämnar att utveckla kvantitativa biomarkörer för cancerdiagnostik och prognos. Huvudsyftet för “Studie I” har varit att utveckla en djupinlärnings-pipeline vars syfte är att fånga lungpatalogiers anatomier (inklusive lungtumörer) samt integrera detta med djupa neurala nätverk för segmentering för att nyttja det första nätverkets utfall för att förbättra segmenteringskvalitén. Den föreslagna pipelinen testades på flertalet dataset och numeriska analyser visar en överlägsna resultat för den föreslagna “prior-medvetna” djupinlärningsmetoden. “Studie II” ämnar att ta sig an ett viktig problem som övervakade segmenteringsmetoder ställs inför: ett beroende av enorma annoterade dataset. I denna studie föreslås en icke-övervakad segmenteringsmetod som baseras på konceptet “ifyllnad” (“inpainting”) för att segmentera tumörer i områdena: lungor samt huvud och hals i bilder från olika modaliteter. Den föreslagna metoden lyckas bättre än en familj väletablerade icke-oövervakade segmenteringsmodeller. “Studie III” och “Studie IV” försöker automatiskt diskriminera benigna lungtumörer från maligna tumörer genom att analysera bilder från LDCT (lågdos-CT). I “Studie III“ föreslås ett djupt neuralt nätverk för klassificering vars grafstruktur tillåter lokal analys av tumörens inbördes heterogeniteter samt en helhetsbild från global kontextuell information. “Studie IV” försöker utvärdera noggrant utvalda metoder som grundar sig på att extrahera anatomiska särdrag från medicinska bilder. I studien jämförs konventionella “radiomics”-metoder med särdrag från neurala nätverk samt en kombination av båda på samma dataset. Resultat från studien visar att en kombination av särdrag från djupa neurala nätverk samt “radiomics” kan ge bättre resultat i klassificeringsproblemet. “Studie V” har fokus på tidig bedömning av lungtumörers respons på behandling genom att utveckla ett set nya fysiologisk observerbara särdrag. Den presenterade metoden har använts för att kvantifiera förändringar i tumörers karaktär i PET-CT-undersökningar för att predicera patienters prognos två år efter senaste behandling. Metoden jämförts mot konventionella “radiomics” och utvärderingen visar att den föreslagna metoden ger förbättrade resultat. Till skilnad från “Studie V”, som fokuserar på att lösa ett binärt klassificeringsproblem, så försöker “Studie VI” predicera överlevnadsgraden hos patienter med lung- samt huvud och hals-cancer genom att undersöka neurala nätverk med sfäriska faltningsoperationer. Metoden jämförs mot, bland annat, “radiomics” och visar liknande resultat för analys på samma dataset, men bättre resultat för analys på olika dataset. Sammanfattningsvis så utnyttjar de sex studierna olika medicinska bildgivande system samt en mängd olika bildbehandling- och maskininlärningstekniker för att utveckla verktyg för att kvantifierar tumörers egenskaper, som kan underlätta fastställande av diagnos och prognos. QC 2022-08-29
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- 2022
9. Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients.
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Sinzinger, Fabian, Astaraki, Mehdi, Smedby, Örjan, and Moreno, Rodrigo
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CONVOLUTIONAL neural networks ,SURVIVAL rate ,PROPORTIONAL hazards models ,CANCER patients ,DEEP learning - Abstract
Objective: Survival Rate Prediction (SRP) is a valuable tool to assist in the clinical diagnosis and treatment planning of lung cancer patients. In recent years, deep learning (DL) based methods have shown great potential in medical image processing in general and SRP in particular. This study proposes a fully-automated method for SRP from computed tomography (CT) images, which combines an automatic segmentation of the tumor and a DL-based method for extracting rotational-invariant features. Methods: In the first stage, the tumor is segmented from the CT image of the lungs. Here, we use a deep-learning-based method that entails a variational autoencoder to provide more information to a U-Net segmentation model. Next, the 3D volumetric image of the tumor is projected onto 2D spherical maps. These spherical maps serve as inputs for a spherical convolutional neural network that approximates the log risk for a generalized Cox proportional hazard model. Results: The proposed method is compared with 17 baseline methods that combine different feature sets and prediction models using three publicly-available datasets: Lung1 (n=422), Lung3 (n=89), and H&N1 (n=136). We observed comparable C-index scores compared to the best-performing baseline methods in a 5-fold cross-validation on Lung1 (0.59 ± 0.03 vs. 0.62 ± 0.04). In comparison, it slightly outperforms all methods in inter-data set evaluation (0.64 vs. 0.63). The best-performing method from the first experiment reduced its performance to 0.61 and 0.62 for Lung3 and H&N1, respectively. Discussion: The experiments suggest that the performance of spherical features is comparable with previous approaches, but they generalize better when applied to unseen datasets. That might imply that orientation-independent shape features are relevant for SRP. The performance of the proposed method was very similar, using manual and automatic segmentation methods. This makes the proposed model useful in cases where expert annotations are not available or difficult to obtain. [ABSTRACT FROM AUTHOR]
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- 2022
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10. A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images.
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Astaraki, Mehdi, Yang, Guang, Zakko, Yousuf, Toma-Dasu, Iuliana, Smedby, Örjan, and Wang, Chunliang
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RADIOMICS ,PULMONARY nodules ,COMPUTED tomography ,DEEP learning ,FEATURE selection - Abstract
Objectives: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules. Methods: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction. Results: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010). Conclusion: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study. [ABSTRACT FROM AUTHOR]
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- 2021
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11. Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis.
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Hamedi, Mahyar, Salleh, Sh-Hussain, Ting, Chee-Ming, Astaraki, Mehdi, and Noor, Alias Mohd
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This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize 11 facial expressions for muscle computer interfacing applications. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. These classifiers are evaluated in terms of classification accuracy and processing time. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. Results show that: (1) the proposed denosing step significantly improves the system performance; (2) root mean square is the most discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI; however, ANOVA reveals no significant difference among the best methods with almost similar performance. [ABSTRACT FROM PUBLISHER]
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- 2018
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12. Brain Tumor Target Volume Segmentation: Local Region Based Approach.
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Astaraki, Mehdi and Aslian, Hossein
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- 2015
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13. A modified fast local region based method for image segmentation.
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Astaraki, Mehdi, Aslian, Hosein, and Hamedi, Mahyar
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- 2015
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14. Time-frequency facial gestures EMG analysis using bilinear distribution.
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Hamedi, Mahyar, Salleh, Sh-Hussain, Ismail, Kamarulafizam, Noor, Alias Mohd, Astaraki, Mehdi, and Aslian, Hossein
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- 2015
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15. Comparison of Multilayer Perceptron and Radial Basis Function Neural Networks for EMG-Based Facial Gesture Recognition.
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Hamedi, Mahyar, Salleh, Sh-Hussain, Astaraki, Mehdi, Noor, Alias Mohd, and Harris, Arief Ruhullah A.
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- 2014
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16. Asynchronous multiclass mental tasks classification through very fast Versatile Elliptic Basis Function Neural Network.
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Hamedi, Mahyar, Salleh, Sh-Hussain, Mohammad-Rezazadeh, Iman, Astaraki, Mehdi, and Mohd Noor, Alias
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- 2014
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17. Prior-aware autoencoders for lung pathology segmentation.
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Astaraki, Mehdi, Smedby, Örjan, and Wang, Chunliang
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LUNGS , *NON-small-cell lung carcinoma , *PATHOLOGY , *PULMONARY nodules - Abstract
• A prior-aware Autoencoder-based method is proposed for lung pathology segmentation. • Fake healthy slices were generated by inpainting pathological images. • An Autoencoder model was developed to learn the distribution of healthy lungs by training pairs of healthy-unhealthy slices. • Obtained prior information was integrated into a segmentation network to improve the segmentation accuracy. • Experiments on different lung pathologies in large-scale datasets demonstrate the effectiveness of the proposed pipeline. Segmentation of lung pathology in Computed Tomography (CT) images is of great importance for lung disease screening. However, the presence of different types of lung pathologies with a wide range of heterogeneities in size, shape, location, and texture, on one side, and their visual similarity with respect to surrounding tissues, on the other side, make it challenging to perform reliable automatic lesion segmentation. To leverage segmentation performance, we propose a deep learning framework comprising a Normal Appearance Autoencoder (NAA) model to learn the distribution of healthy lung regions and reconstruct pathology-free images from the corresponding pathological inputs by replacing the pathological regions with the characteristics of healthy tissues. Detected regions that represent prior information regarding the shape and location of pathologies are then integrated into a segmentation network to guide the attention of the model into more meaningful delineations. The proposed pipeline was tested on three types of lung pathologies, including pulmonary nodules, Non-Small Cell Lung Cancer (NSCLC), and Covid-19 lesion on five comprehensive datasets. The results show the superiority of the proposed prior model, which outperformed the baseline segmentation models in all the cases with significant margins. On average, adding the prior model improved the Dice coefficient for the segmentation of lung nodules by 0.038, NSCLCs by 0.101, and Covid-19 lesions by 0.041. We conclude that the proposed NAA model produces reliable prior knowledge regarding the lung pathologies, and integrating such knowledge into a prior segmentation network leads to more accurate delineations. [Display omitted] [ABSTRACT FROM AUTHOR]
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- 2022
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18. EMG-based facial gesture recognition through versatile elliptic basis function neural network.
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Hamedi, Mahyar, Salleh, Sh-Hussain, Astaraki, Mehdi, and Noor, Alias Mohd
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HUMAN facial recognition software ,ELECTROMYOGRAPHY ,NEUROMUSCULAR system ,ELECTRODES ,NEURAL circuitry - Abstract
Background: Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating. Methods: In this study, EMGs of ten facial gestures were recorded from ten subjects using three pairs of surface electrodes in a bi-polar configuration. The signals were filtered and segmented into distinct portions prior to feature extraction. Ten different time-domain features, namely, Integrated EMG, Mean Absolute Value, Mean Absolute Value Slope, Maximum Peak Value, Root Mean Square, Simple Square Integral, Variance, Mean Value, Wave Length, and Sign Slope Changes were extracted from the EMGs. The statistical relationships between these features were investigated by Mutual Information measure. Then, the feature combinations including two to ten single features were formed based on the feature rankings appointed by Minimum- Redundancy-Maximum-Relevance (MRMR) and Recognition Accuracy (RA) criteria. In the last step, VEBFNN was employed to classify the facial gestures. The effectiveness of single features as well as the feature sets on the system performance was examined by considering the two major metrics, recognition accuracy and training time. Finally, the proposed classifier was assessed and compared with conventional methods support vector machines and multilayer perceptron neural network. Results: The average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy. Moreover, the results proved a very fast procedure since the training time during classification via VEBFNN was 0.105 seconds. It was also indicated that MRMR was not a proper criterion to be used for making more effective feature sets in comparison with RA. Conclusions: This work was accomplished by introducing the most discriminating facial EMG time-domain feature for the recognition of different facial gestures; and suggesting VEBFNN as a promising method in EMG-based facial gesture classification to be used for designing interfaces in human machine interaction systems. [ABSTRACT FROM AUTHOR]
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- 2013
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19. Brain Tumor Target Volume Segmentation: Local Region Based Approach
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Hossein Aslian, Mehdi Astaraki, David A. Jaffray, Astaraki, Mehdi, and Aslian, Hossein
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region based active contour ,Active contour model ,Treatment Planning ,Computer science ,business.industry ,Brain Tumor Target ,Segmentation ,Pattern recognition ,Distribution fitting ,Hausdorff distance ,Sørensen–Dice coefficient ,Metric (mathematics) ,Artificial intelligence ,Radiation treatment planning ,business ,Energy (signal processing) - Abstract
In this paper, we comprehensively evaluated clinical application of local robust-region based algorithms to delineate the brain target volumes in radiation therapy treatment planning. Localized region based algorithms can optimize processing time of manual target tumor delineation and have perfect correlation with manual delineation defined by oncologist due to high deformability. Accordingly, they can receive much attention in radiation therapy treatment planning. Firstly, clinical target volumes (CTVs) of 135 slices in 18 patients were manually defined by two oncologists and the average of these contours considered as references in order to compare with semi-automatic results from different four algorithms. Then, four localized region based algorithms named Localizing Region Based Active Contour (LRBAC), Local Chan-Vese Model (LCV), Local Region Chan-Vese Model (LRCV) and Local Gaussian Distribution Fitting (LGDF) were applied to outline CTVs. Finally, comparisons between semiautomatic results and baselines were done according to three different metric criteria: Dice coefficient, Hausdorff distance, and mean absolute distance. Manual delineation processing times of target tumors were also performed. Our result showed that LCV has advantage over other algorithms in terms of the processing time and afterward LRCV is the second fastest method. LRBAC was the second slowest technique; however, we found that processing speed in LRBAC can be almost doubled by replacing the time-consuming re-initialization process with energy penalizing term. Accordingly, due to high accuracy performance of LRBAC algorithm, it can be concluded that the modified version of LRBAC has the best performance in brain target volumes in radiation therapy treatment planning among other localized algorithms in terms of speed and accuracy.
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- 2015
20. CTV Delineation for High-Grade Gliomas: Is There Agreement With Tumor Cell Invasion Models?
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Häger W, Lazzeroni M, Astaraki M, and Toma-Daşu I
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Purpose: High-grade glioma (HGG) is a common form of malignant primary brain cancer with poor prognosis. The diffusive nature of HGGs implies that tumor cell invasion of normal tissue extends several centimeters away from the visible gross tumor volume (GTV). The standard methodology for clinical volume target (CTV) delineation is to apply a 2- to 3-cm margin around the GTV. However, tumor recurrence is extremely frequent. The purpose of this paper was to introduce a framework and computational model for the prediction of normal tissue HGG cell invasion and to investigate the agreement of the conventional CTV delineation with respect to the predicted tumor invasion., Methods and Materials: A model for HGG cell diffusion and proliferation was implemented and used to assess the tumor invasion patterns for 112 cases of HGGs. Normal brain structures and tissues as well as the GTVs visible on diagnostic images were delineated using automated methods. The volumes encompassed by different tumor cell concentration isolines calculated using the model for invasion were compared with the conventionally delineated CTVs, and the differences were analyzed. The 3-dimensional-Hausdorff distance between the CTV and the volumes encompassed by various isolines was also calculated., Results: In 50% of cases, the CTV failed to encompass regions containing tumor cell concentrations of 614 cells/mm³ or greater. In 84% of cases, the lowest cell concentration completely encompassed by the CTV was ≥1 cell/mm³. In the remaining 16%, the CTV overextended into normal tissue. The Hausdorff distance was on average comparable to the CTV margin., Conclusions: The standard methodology for CTV delineation appears to be inconsistent with HGG invasion patterns in terms of size and shape. Tumor invasion modeling could therefore be useful in assisting in the CTV delineation for HGGs., (© 2022 The Author(s).)
- Published
- 2022
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