Medical imaging has transformed the field of medicine. The presence and severity of disease profo... more Medical imaging has transformed the field of medicine. The presence and severity of disease profoundly influence the provision of clinical care. Segmentation is of paramount importance in medical imaging, particularly in the context of radiotherapy planning. Image segmentation is the fundamental function of image analysis and development, influencing subsequent processes such as representation, object description, feature measurement, and even higher-level tasks such as object classification. It is essential for facilitating the characterization and visualization of regions of interest in medical images. In computer vision, segmentation is an essentially technical task in analyzing medical images, essential for many applications, including diagnosis, prognosis, and treatment planning. There are several segmentation methods, the most commonly used of which are thresholding, edge detection, region growth, active edges, and graphical slicing. The emergence of deep learning, in particular, has revolutionized the segmentation field. Deep learning methods effectively segment medical images and are widely used in clinical and surgical practice. Using computeraided diagnostic systems to improve the sensitivity and specificity of lesion detection and other assessment indices has become a priority in medical research and diagnostic radiology. This article reviews the task of segmentation in medical imaging. It begins by highlighting the importance of segmentation in the analysis of medical images. It then moves on to an in-depth and comparative examination of the various segmentation techniques, including thresholding, edge detection, region growth, active contours, graphical slicing, and combining methods.
3D face recognition is attracting more attention due to the recent development in 3D facial data ... more 3D face recognition is attracting more attention due to the recent development in 3D facial data acquisition techniques. It is strongly believed that 3D Face recognition systems could overcome the inherent problems of 2D face recognition such as facial pose variation, illumination, and variant facial expression. In this paper we present a novel technique for 3D face recognition system using a set of parameters representing the central region of the face. These parameters are essentially vertical and cross sectional profiles and are extracted automatically without any prior knowledge or assumption about the image pose or orientation. In addition, these profiles are stored in terms of their Fourier Coefficients in order to minimize the size of input data. Our approach is validated and verified against two different datasets of 3D images covers enough systematic and pose variation. High recognition rate was achieved.
Tableau B.6. Les valeurs médianes des coefficients d'absorption et des coefficients de diffusion ... more Tableau B.6. Les valeurs médianes des coefficients d'absorption et des coefficients de diffusion pour le sein calculées à différentes longueurs d'ondes [Spinelli, 2004]164 D'un autre côté, la technique IPAS est considérée comme une méthode invasive et coûteuse pour des raisons de radioprotection. La thermothérapie par laser interstitiel (LITT) est une technique ablative, mini-invasive, et en cours d'évaluation. Nous présentons des résultats préliminaires de la simulation de la distribution de la chaleur et du dommage thermique de l'application la méthode LITT pour la thérapie focale du cancer du sein.
The main purpose of segmentation is to partition an image based on features into different region... more The main purpose of segmentation is to partition an image based on features into different regions. Unsupervised classification algorithms K means, K-nearest neighbor, neural networks can be used to perform efficient image segmentation. Image segmentation is an important step to perform classification of images. Segmentation algorithms such as watershed segmentation, support vector machines can be used to find the region of interest. A genetic algorithm based image segmentation algorithm, ant colony optimization algorithm is proposed and we compare it with k-means segmentation. We apply some segmentation algorithms in industry-standard datasets and view the results of our segmentation algorithms. Segmentation is a basic task in image processing and can be applied in large number of domains. We emphasize on how a segmentation algorithm can be developed to segment out tumors from medical magnetic resonance images. We have used the open CV python package for our a image processing tasks
Medical imaging has transformed the field of medicine. The presence and severity of disease profo... more Medical imaging has transformed the field of medicine. The presence and severity of disease profoundly influence the provision of clinical care. Segmentation is of paramount importance in medical imaging, particularly in the context of radiotherapy planning. Image segmentation is the fundamental function of image analysis and development, influencing subsequent processes such as representation, object description, feature measurement, and even higher-level tasks such as object classification. It is essential for facilitating the characterization and visualization of regions of interest in medical images. In computer vision, segmentation is an essentially technical task in analyzing medical images, essential for many applications, including diagnosis, prognosis, and treatment planning. There are several segmentation methods, the most commonly used of which are thresholding, edge detection, region growth, active edges, and graphical slicing. The emergence of deep learning, in particular, has revolutionized the segmentation field. Deep learning methods effectively segment medical images and are widely used in clinical and surgical practice. Using computeraided diagnostic systems to improve the sensitivity and specificity of lesion detection and other assessment indices has become a priority in medical research and diagnostic radiology. This article reviews the task of segmentation in medical imaging. It begins by highlighting the importance of segmentation in the analysis of medical images. It then moves on to an in-depth and comparative examination of the various segmentation techniques, including thresholding, edge detection, region growth, active contours, graphical slicing, and combining methods.
3D face recognition is attracting more attention due to the recent development in 3D facial data ... more 3D face recognition is attracting more attention due to the recent development in 3D facial data acquisition techniques. It is strongly believed that 3D Face recognition systems could overcome the inherent problems of 2D face recognition such as facial pose variation, illumination, and variant facial expression. In this paper we present a novel technique for 3D face recognition system using a set of parameters representing the central region of the face. These parameters are essentially vertical and cross sectional profiles and are extracted automatically without any prior knowledge or assumption about the image pose or orientation. In addition, these profiles are stored in terms of their Fourier Coefficients in order to minimize the size of input data. Our approach is validated and verified against two different datasets of 3D images covers enough systematic and pose variation. High recognition rate was achieved.
Tableau B.6. Les valeurs médianes des coefficients d'absorption et des coefficients de diffusion ... more Tableau B.6. Les valeurs médianes des coefficients d'absorption et des coefficients de diffusion pour le sein calculées à différentes longueurs d'ondes [Spinelli, 2004]164 D'un autre côté, la technique IPAS est considérée comme une méthode invasive et coûteuse pour des raisons de radioprotection. La thermothérapie par laser interstitiel (LITT) est une technique ablative, mini-invasive, et en cours d'évaluation. Nous présentons des résultats préliminaires de la simulation de la distribution de la chaleur et du dommage thermique de l'application la méthode LITT pour la thérapie focale du cancer du sein.
The main purpose of segmentation is to partition an image based on features into different region... more The main purpose of segmentation is to partition an image based on features into different regions. Unsupervised classification algorithms K means, K-nearest neighbor, neural networks can be used to perform efficient image segmentation. Image segmentation is an important step to perform classification of images. Segmentation algorithms such as watershed segmentation, support vector machines can be used to find the region of interest. A genetic algorithm based image segmentation algorithm, ant colony optimization algorithm is proposed and we compare it with k-means segmentation. We apply some segmentation algorithms in industry-standard datasets and view the results of our segmentation algorithms. Segmentation is a basic task in image processing and can be applied in large number of domains. We emphasize on how a segmentation algorithm can be developed to segment out tumors from medical magnetic resonance images. We have used the open CV python package for our a image processing tasks
Uploads
Papers by bounegta ahmed