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The use of computer technology in medical sciences is spreading with technology. The use of computers especially for imaging has become a third eye for physicians. In orthopedic surgeons, after simple roentgenograms for fracture detection, the use of computerized tomography and magnetic resonance has provided great convenience in the detection of fracture, typing, and therefore the appropriate treatment...
From the preoperative partial nephrectomy planning perspective, it is essential to expose separately different kidney structures and to analyze their mutual topological relations. Only then, the identification of possible conflicts prior to surgical intervention can be facilitated. To enable this, we propose a segmentation frameworks for renal vascular tree, kidney and pelvicalyceal system from corresponding...
Medical images are being utilized progressively inside the healthcare services for diagnosis, guiding treatment, planning treatment and checking illness progression. In fact, medical imaging chiefly processes uncertain, lost, vague, complementary, conflicting, redundant contradictory, distorted information and data has powerful structural character. As a general approach, the comprehension of any...
Detection of implanted iodine-125 seeds in postoperative CT is a necessary step for evaluating the output of seed implantation brachytherapy of lung tumor. In this paper, we propose a semi-automated method to detect implanted seeds in postoperative lung CT. Three main steps are included in our approach. Firstly, the ROI (Region Of Interest) containing all seeds is extracted from the original image...
We present a new frame of lung parenchyma segmentation. Optimal threshold value method and the boundary tracking method are used to get rid of the background interference and segment the lung region. Then new algorithm is used for lung region boundary repairing based on the mathematical morphology method. The experimental results show the new algorithm can segment lung regions from the chest CT images...
Medulloblastoma (MB) is the most common brain tumor in children. Recent studies have demonstrated a relationship between specific signaling pathway abnormalities, a tendency to more favorable outcomes, and a histopathological feature: nodular growth patterns. In this work we present a new segmentation scheme which requires minimal user interaction to segment nodules on MB histopathological sections...
In this paper 2D Otsu algorithm based on particle swarm optimization (PSO) is proposed to segment CT lung images. This method can extract pulmonary parenchyma from multisliced CT images, which is primary step to detect the pulmonary disease such as lung cancer, tumor, and mass cells. In the automated pulmonary disease diagnosis, image segmentation plays an important role and image analysis result...
Human heart anatomy is the study of morphological structures and relationships between morphological structures. Hence such a study when visualized in the form of a two dimensional atlas can hold strong interactive sessions between doctors and medical students. Atlas is a visualization technique developed for understanding the morphological structures of a human body. Interacting directly with morphological...
Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed...
To remove noise from biomedical images polluted by excessive and inhomogeneous additive or multiplicative noise, most of the denoising algorithms cannot keep a desirable balance between denoising and preservation of fine features; only work for one specific noise; and involve heuristic parameter tuning. We present a fully automatic approach to preserve sharp edges and fine details while removing noise...
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a conditional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus...
The segmentation of the myocardium in echocardiographic images is an important task for the diagnosis of heart diseases. This step is made difficult due to the inherent problems of echographic images (e.g. low contrast, speckle noise). In this article, we propose a method to segment the whole myocardium (endocardium and epicardium) from 2D echographic scans. This is achieved using a level-set model...
Ant Colony Systems (ACS) have been applied to solve complex problems. The first Ant System was proposed in the earlier nineties, and since then several studies were performed to apply this paradigm in real problems. Several researchers have explored the idea of applying ACS to image processing. Herein, the original ACS models applied to image processing are presented. Moreover, two new models, based...
We present a framework for cell tracking in a highly cluttered environment in live cell imaging from mouse brain cortex. Our goal is to track cells over a long period of time for intracellular calciumion concentration in order to detect important cellular events such as neural activity and cell division. Since traditional object tracking approach such as segmentation followed by tracking is not applicable...
Intensity based classification relies on contrast between tissue types adjacent in feature space and adequate signal compared to image noise. Contrast between brain tissue types in Multiple Sclerosis patients Magnetic Resonance Imaging is reduced due to the presence of lesions which intensity values overlap with healthy tissue, resulting in tissue misclassification. We propose a new, extended classifier...
In this paper, we present a novel methodology for computing statistical shape models (SSM's) by leveraging the medial axis model to determine shape variations between objects. Landmark based SSM's (LSSM's) are a popular approach to describing valid shape variation in an object of interest by applying principal component analysis to a set of landmarks on the surface of the object. However, defining...
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
We present a unified approach to Expectation-Maximization (EM) and Level Set image segmentation that combines the advantages of the two algorithms via a geometric prior that encourages local classification similarity. Compared to level sets, our method increases the information returned by providing probabilistic soft decisions, is easily extensible to multiple regions, and does not require solving...
Segmentation and tracking of tagged MR images is a critical component of in vivo understanding for the heart dynamics. In this paper, we propose a novel approach which uses multi-dimensional features and casts the left ventricle (LV) extraction problem as a maximum posteriori estimation process in both the feature and the shape spaces. Exact integration of multi-dimensional boundary and regional statistics...
A novel automated framework for quantification of myocardial viability in contrast enhanced cardiac magnetic resonance images (CE-CMRI) is proposed. The framework consists of three main steps. First, the inner and outer borders of the left ventricle (LV) wall (myocardium wall) are segmented from the surrounding tissue. Second, the pathological tissue in the myocardium wall is identified using a MAP-based...
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