| 1. |
- Y Banaem, Hossein, et al.
(författare)
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Brain tumor modeling : glioma growth and interaction with chemotherapy
- 2011
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Ingår i: International Conference on Graphic and Image Processing (ICGIP 2011). - : SPIE. ; 8285
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Konferensbidrag (refereegranskat)abstract
- In last decade increasingly mathematical models of tumor growths have been studied, particularly on solid tumors which growth mainly caused by cellular proliferation. In this paper we propose a modified model to simulate the growth of gliomas in different stages. Glioma growth is modeled by a reaction-advection-diffusion. We begin with a model of untreated gliomas and continue with models of polyclonal glioma following chemotherapy. From relatively simple assumptions involving homogeneous brain tissue bounded by a few gross anatomical landmarks (ventricles and skull) the models have been expanded to include heterogeneous brain tissue with different motilities of glioma cells in grey and white matter. Tumor growth is characterized by a dangerous change in the control mechanisms, which normally maintain a balance between the rate of proliferation and the rate of apoptosis (controlled cell death). Result shows that this model closes to clinical finding and can simulate brain tumor behavior properly.
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| 2. |
- Yousefi, Hossein, et al.
(författare)
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An optimised linear mechanical model for estimating brain shift caused by meningioma tumours
- 2013
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Ingår i: International Journal of Biomedical Science and Engineering. - : Science Publishing Group. - 2376-7227 .- 2376-7235. ; 1:1, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Estimation of brain deformation plays an important role in computer-aided therapy and image-guided neurosurgery systems. Tumour growth can cause brain deformation and change stress distribution in the brain. Biomechanical models exist that use a finite element method to estimate brain shift caused by tumour growth. Such models can be categorised as linear and non-linear models, both of which assume finite deformation of the brain after tumour growth. Linear models are easy to implement and fast enough to for applications such as IGS where the time is a great of concern. However their accuracy highly dependent on the parameters of the models in this paper, we proposed an optimisation approach to improve a naive linear model to achieve more precise estimation of brain displacements caused by tumour growth. The optimisation process has improved the accuracy of the model by adapting the brain model parameters according to different tomour sizes.We used patient-based tetrahedron finite element mesh with proper material properties for brain tissue and appropriate boundary conditions in the tumour region. Anatomical landmarks were determined by an expert and were divided into two different sets for evaluation and optimisation. Tetrahedral finite element meshes were used and the model parameters were optimised by minimising the mean square distance between the predicted locations of the anatomical landmarks derived from Brain Atlas images and their actual locations on the tumour images. Our results demonstrate great improvement in the accuracy of an optimised linear mechanical model that achieved an accuracy rate of approximately 92%.
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| 3. |
- Wahl, Joel, et al.
(författare)
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Impact of preprocessing methods on the Raman spectra of brain tissue
- 2022
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Ingår i: Biomedical Optics Express. - : Optica Publishing Group (formerly OSA). - 2156-7085. ; 13:12, s. 6763-6777
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Tidskriftsartikel (refereegranskat)abstract
- Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.
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| 4. |
- Akter, Nasrin, et al.
(författare)
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Brain Tumor Classification using Transfer Learning from MRI Images
- 2022
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Ingår i: Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021. - Singapore : Springer. ; , s. 575-587
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Bokkapitel (refereegranskat)abstract
- One of the most vital parts of medical image analysis is the classification of brain tumors. Because tumors are thought to be origins to cancer, accurate brain tumor classification can save lives. As a result, CNN (Convolutional Neural Network)-based techniques for classifying brain cancers are frequently employed. However, there is a problem: CNNs are exposed to vast amounts of training data in order to produce good performance. This is where transfer learning enters into the picture. We present a 4-class transfer learning approach for categorizing Glioma, Meningioma, and Pituitary tumors and non-tumors in this study. The three most prevalent types of brain tumors are glioma, meningioma, and pituitary tumors. Our presented method, which employs the theory of transfer learning, utilizes a pre-trained InceptionResnetV1 method for classifying brain MRI images by extracting features from them using the softmax classifier method. The proposed approach outperforms all prior techniques with a mean classification accuracy of 93.95%. For the evaluation of our method we use kaggle dataset. Precision, recall, and F-score are one of the key performance metrics employed in this study.
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| 5. |
- Barman, Sourav, et al.
(författare)
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Transfer Learning Based Skin Cancer Classification Using GoogLeNet
- 2023
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Ingår i: Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings, part 1. - : Springer Science and Business Media Deutschland GmbH. - 9783031346187 - 9783031346194 ; , s. 238-252
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Konferensbidrag (refereegranskat)abstract
- Skin cancer has been one of the top three cancers that can be fatal when caused by broken DNA. Damaged DNA causes cells to expand uncontrollably, and the rate of growth is currently increasing rapidly. Some studies have been conducted on the computerized detection of malignancy in skin lesion images. However, due to some problematic aspects such as light reflections from the skin surface, differences in color lighting, and varying forms and sizes of the lesions, analyzing these images is extremely difficult. As a result, evidence-based automatic skin cancer detection can help pathologists improve their accuracy and competency in the early stages of the disease. In this paper, we present a transfer ring strategy based on a convolutional neural network (CNN) model for accurately classifying various types of skin lesions. Preprocessing normalizes the input photos for accurate classification; data augmentation increases the amount of images, which enhances classification rate accuracy. The performance of the GoogLeNet transfer learning model is compared to that of other transfer learning models such as Xpection, InceptionResNetVe, and DenseNet, among others. The model was tested on the ISIC dataset, and we ended up with the highest training and testing accuracy of 91.16% and 89.93%, respectively. When compared to existing transfer learning models, the final results of our proposed GoogLeNet transfer learning model characterize it as more dependable and resilient.
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| 6. |
- Bergström, Per, et al.
(författare)
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Single shot shape evaluation using dual-wavelength holographic reconstructions and regularization
- 2014
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Ingår i: Fringe 2013. - Berlin : Encyclopedia of Global Archaeology/Springer Verlag. - 9783642363580 - 9783642363597 ; , s. 103-108
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Konferensbidrag (refereegranskat)abstract
- The aim of this work is to evaluate the shape of a free form object using single shot digital holography. The digital holography results in a gradient field and wrapped phase maps representing the shape of the object. The task is then to find a surface representation from this data which is an inverse problem. To solve this inverse problem we are using regularization with additional shape information from the CAD-model of the measured object.
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| 7. |
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| 8. |
- Dey, Puja, et al.
(författare)
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Human Age and Gender Prediction from Facial Images Using Deep Learning Methods
- 2024
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Ingår i: The 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40). - : Elsevier. ; , s. 314-321
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Konferensbidrag (refereegranskat)abstract
- Human age and gender prediction from facial images has garnered significant attention due to its importance in various applications. Traditional models struggle with large-scale variations in unfiltered images. Convolutional Neural Networks (CNNs) have emerged as effective tools for facial analysis due to their robust performance. This paper presents a novel CNN approach for robust age and gender classification using unconstrained real-world images. The CNN architecture includes convolution, pooling, and fully connected layers for feature extraction, dimension reduction, and mapping to output classes. Adience and UTKFace datasets were utilized, with the best training and testing accuracies achieved using an 80% training and 20% testing data split. Robust image pre-processing and data augmentation techniques were applied to handle dataset variations. The proposed approach outperformed existing methods, achieving age prediction accuracies of 86.42% and 81.96%, and gender prediction accuracies of 97.65% and 96.32% on the Adience and UTKFace datasets, respectively.
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| 9. |
- Dey, Puja, et al.
(författare)
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Hybrid Deep Transfer Learning Framework for Humerus Fracture Detection and Classification from X-ray Images
- 2024
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Ingår i: 2024 4th International Conference on Intelligent Technologies (CONIT). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- The detection and classification of humerus fractures from X-ray images are crucial for effective medical diagnosis and treatment planning. Manual assessment of such fractures is time-consuming and prone to errors, emphasizing the need for automated systems. In this study, we propose a Hybrid Deep Transfer Learning Framework for Humerus Fracture Detection and Classification from X-ray Images. Leveraging deep learning techniques, we amassed a dataset of 1266 radiographic images from the publicly available MURA dataset, encompassing both negative (non-fractured) and positive (fractured) cases. Preprocessing techniques were employed to enhance image quality, followed by data augmentation to mitigate overfitting and bolster system accuracy. Subsequently, a hybrid model comprising ResNet50 and DenseNet121 architectures was utilized for feature extraction and classification. Through experimentation with various optimizers, we achieved the highest accuracy of 93.41% using the Adam optimizer. Additionally, precision, recall, and F1-score metrics were computed to evaluate model performance comprehensively. Comparative analyses were conducted with other pre-trained models, showcasing the effectiveness of our proposed framework. Our results highlight the deep transfer learning’s effectiveness in humerus fracture detection, providing a promising path forward for the development of medical imaging technologies.
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| 10. |
- Khodadad, Davood, 1985-, et al.
(författare)
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B-spline based free form deformation thoracic non-rigid registration of CT and PET images
- 2011
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Ingår i: International Conference on Graphic and Image Processing (ICGIP 2011). - : SPIE - International Society for Optical Engineering. ; 8285
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Konferensbidrag (refereegranskat)abstract
- Accurate attenuation correction of emission data is mandatory for quantitative analysis of PET images. One of the main concerns in CT-based attenuation correction(CTAC) of PET data in multimodality PET/CT imaging is misalignment between PET and CT images. The aim of this study, is to proposed a hybrid method which is simple, fast and accurate, for registration of PET and CT data which affected from respiratory motion in order to improve the quality of CTAC. The algorithm is composed of three methods: First, using B-spline Free Form Deformation to describe both images and deformation field. Then applying a pre-filtering on both PET and CT images before segmentation of structures in order to reduce the respiratory related attenuation correction artifacts of PET emission data. In this approach, B-spline using FFD provide more accurate adaptive transformation to align the images, and structure constraints obtained from prefiltering applied to guide the algorithm to be more fast and accurate. Also it helps to reduce the radiation dose in PET/CT by avoiding repetition of CT imaging. These advances increase the potential of the method for routine clinical application.
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