Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can offer a second opinion to radiologists, thereby increasing the overall diagnostic accuracy of ultrasound imaging. Although current CAD systems exhibit promising results, their use in clinical practice is limited. Some of the main limitations are that the majority use direction dependent features so, they are only compatible with static images in just one plane (axial or longitudinal), requiring precise segmentation of a nodule. Our intention has been to design a CAD system which will use only direction independent features i.e., not dependent upon the orientation or inclination angle of the ultrasound probe when acquiring the image. In this study, 60 thyroid nodules (20 malignant, 40 benign) were divided into small patches of 17 × 17 pixels, which were then used to extract several direction independent features by employing Two-Threshold Binary Decomposition, a method that decomposes an image into the set of binary images. The features were then used in Random Forests (RF) and Support Vector Machine (SVM) classifiers to categorize nodules into malignant and benign classes. Classification was evaluated using group 10-fold cross-validation method. Performance on individual patches was then averaged to classify whole nodules with the following results: overall accuracy, sensitivity, specificity and area under receiver operating characteristics (ROC) curve: 95%, 95%, 95%, 0.971 for RF and; 91.6%, 95%, 90%, 0.965 for SVM respectively. The patch-based CAD system we present can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can increase the overall accuracy of ultrasound imaging.

• MeSH
• diagnóza počítačová metody   MeSH
• diferenciální diagnóza MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• senzitivita a specificita MeSH
• support vector machine * MeSH
• ultrasonografie metody   MeSH
• uzle štítné žlázy diagnostické zobrazování   patologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

In image-guided percutaneous interventions, a precise planning of the needle path is a key factor to a successful intervention. In this paper we propose a novel method for computing a patient-specific optimal path for such interventions, accounting for both the deformation of the needle and soft tissues due to the insertion of the needle in the body. To achieve this objective, we propose an optimization method for estimating preoperatively a curved trajectory allowing to reach a target even in the case of tissue motion and needle bending. Needle insertions are simulated and regarded as evaluations of the objective function by the iterative planning process. In order to test the planning algorithm, it is coupled with a fast needle insertion simulation involving a flexible needle model and soft tissue finite element modeling, and experimented on the use-case of thermal ablation of liver tumors. Our algorithm has been successfully tested on twelve datasets of patient-specific geometries. Fast convergence to the actual optimal solution has been shown. This method is designed to be adapted to a wide range of percutaneous interventions.

• MeSH
• ablace MeSH
• algoritmy * MeSH
• chirurgie s pomocí počítače metody   MeSH
• játra patofyziologie   chirurgie   MeSH
• lidé MeSH
• modely anatomické * MeSH
• nádory jater chirurgie   MeSH
• počítačová simulace * MeSH
• předoperační období * MeSH
• uživatelské rozhraní počítače MeSH
• zobrazování trojrozměrné MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal ("healthy") and 8 glaucomatous eyes. We achieved significant correlation (normals: ρ=0.72±0.14; p≪0.05, glaucomatous: ρ=0.58±0.10; p≪0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning.

• MeSH
• barva * MeSH
• discus nervi optici patologie   MeSH
• fundus oculi MeSH
• glaukom patologie   MeSH
• lidé MeSH
• Markovovy řetězce * MeSH
• nervová vlákna patologie   MeSH
• normální rozdělení MeSH
• optická koherentní tomografie MeSH
• retinální gangliové buňky patologie   MeSH
• vylepšení obrazu metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated. We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.

• MeSH
• databáze faktografické normy   MeSH
• internacionalita MeSH
• interpretace obrazu počítačem metody   normy   MeSH
• intervenční ultrasonografie metody   normy   MeSH
• lidé MeSH
• nemoci koronárních tepen ultrasonografie   MeSH
• referenční hodnoty MeSH
• reprodukovatelnost výsledků MeSH
• senzitivita a specificita MeSH
• směrnice pro lékařskou praxi jako téma * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

An automatic method of segmenting the retinal vessel tree and estimating status of retinal neural fibre layer (NFL) from high resolution fundus camera images is presented. First, reliable blood vessel segmentation, using 2D directional matched filtering, enables to remove areas occluded by blood vessels thus leaving remaining retinal area available to the following NFL detection. The local existence of rather faint and hardly visible NFL is detected by combining several newly designed local textural features, sensitive to subtle NFL characteristics, into feature vectors submitted to a trained neural-network classifier. Obtained binary retinal maps of NFL distribution show a good agreement with both medical expert evaluations and quantitative results obtained by optical coherence tomography.

• MeSH
• fluoresceinová angiografie metody   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• nemoci zrakového nervu patologie   MeSH
• nervová síť patologie   MeSH
• reprodukovatelnost výsledků MeSH
• retinální cévy patologie   MeSH
• retinoskopie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• senzitivita a specificita MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

new approach to the segmentation of 3D CT images is proposed in an attempt to provide texture-based segmentation of organs or disease diagnosis. 3D extension of Haralick texture features was studied calculating co-occurrences of all voxels in a small cubic region around the voxel. RESULTS: For verification, the proposed method was tested on a set of abdominal 3D volumes of patients. Statistically, the improvement in segmentation was significant for most of the organs considered herein. CONCLUSIONS: The proposed method has potential application in medical image segmentation, including diagnosis of diseases.

• MeSH
• algoritmy MeSH
• lidé MeSH
• počítačová rentgenová tomografie metody   MeSH
• rentgendiagnostika břicha metody   MeSH
• rentgenový obraz - interpretace počítačová metody   MeSH
• reprodukovatelnost výsledků MeSH
• rozpoznávání automatizované metody   MeSH
• senzitivita a specificita MeSH
• umělá inteligence MeSH
• vylepšení rentgenového snímku metody   MeSH
• zobrazování trojrozměrné metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH