The paper describes restitution of geometrical distortions and improvement of signal-to-noise ratio of auto-fluorescence retinal images, finally aimed at segmentation and area estimation of the lipofuscin spots as one of the features to be included in glaucoma diagnosis. The main problems - geometrical and illumination incompatibility of frames in the image sequence and a non-negligible "shear" distortion in the individual frames - have been solved by the presented registration procedure. The concept and some details of the MI-based regularized registration, together with evaluation of test results form the core of the contribution.
- MeSH
- Algorithms MeSH
- Financing, Organized MeSH
- Fluorescein Angiography methods MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Microscopy, Confocal methods MeSH
- Humans MeSH
- Reproducibility of Results MeSH
- Retinal Vessels cytology MeSH
- Retinoscopy methods MeSH
- Sensitivity and Specificity MeSH
- Subtraction Technique MeSH
- Image Enhancement methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Evaluation Study MeSH
INTRODUCTION: Image integration is being used in ablation procedures. However, the success of this approach is dependent on the accuracy of the image integration process. This study aims to evaluate the in vivo accuracy and reliability of the integrated image. METHODS AND RESULTS: One hundred twenty-four patients undergoing radiofrequency (RF) ablation catheter ablation for atrial fibrillation (AF) were recruited for this study from three different centers. Cardiac computerized tomography (CT) was performed in all patients and a 3D image of the left atrium (LA) and pulmonary veins (PVs) was extracted for registration after segmentation using a software program (CartoMerge, Biosense Webster, Inc.). Different landmarks were selected for registration and compared. Surface registration was then done and the impact on integration and the landmarks was evaluated. The best landmark registration was achieved when the posterior points on the pulmonary veins were selected (5.6 +/- 3.2). Landmarks taken on the anterior wall, left atrial appendage (LAA) or the coronary sinus (CS) resulted in a larger registration error (9.1 +/- 2.5). The mean error for surface registration was 2.17 +/- 1.65. However, surface registration resulted in shifting of the initially registered landmark points leading to a larger error (from 5.6 +/- 3.2 to 9.2 +/- 2.1; 95% CI 4.2-3.05). CONCLUSION: Posterior wall landmarks at the PV-LA junction are the most accurate landmarks for image integration in respect to the target ablation area. The concurrent use of the present surface registration algorithm may result in shifting of the initial landmarks with loss of their initial correlation with the area of interest.
- MeSH
- Echocardiography * methods MeSH
- Atrial Fibrillation * diagnosis surgery MeSH
- Image Interpretation, Computer-Assisted methods instrumentation MeSH
- Catheter Ablation * methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Tomography, X-Ray Computed methods MeSH
- Reproducibility of Results MeSH
- Heart Atria radiography MeSH
- Pulmonary Veins radiography MeSH
- Imaging, Three-Dimensional methods instrumentation MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Evaluation Study MeSH
2nd ed. xviii, 826 s. : il. ; 25 cm
- MeSH
- Diagnostic Imaging MeSH
- Image Processing, Computer-Assisted MeSH
- Publication type
- Handbook MeSH
- Conspectus
- Speciální počítačové metody. Počítačová grafika
- NML Fields
- lékařská informatika
- MeSH
- Diagnostic Imaging methods utilization MeSH
- Financing, Organized MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods utilization MeSH
- Tomography, X-Ray Computed utilization MeSH
- Image Processing, Computer-Assisted methods utilization MeSH
- Visible Human Projects MeSH
- Statistics as Topic methods MeSH
- Imaging, Three-Dimensional methods utilization MeSH
- Check Tag
- Humans MeSH
The paper describes a set of approaches and routines designed to improve results in CT based 3D subtractive angiography of lower extremities via better global locally defined image data registration. Starting from the generic concept of 3D disparity-based flexible registration, modifications of this idea are made founded on prior anatomical knowledge, as segmentation into individual bone areas, their rigid registration followed by constrained flexible registration, and flexible registration of soft tissue volumes. After final subtraction, fusion of the individually derived volumes into the full volume of extremities provides the medically assessable results. The level of detail in minor vessels, and continuity of vessels including those in direct contact with the bones, have been found much better clinically than those achieved by standard contemporary commercial software.
- MeSH
- Algorithms MeSH
- Artifacts MeSH
- Models, Biological MeSH
- Angiography, Digital Subtraction methods MeSH
- Humans MeSH
- Tomography, X-Ray Computed methods MeSH
- Radiographic Image Interpretation, Computer-Assisted methods MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Automated methods MeSH
- Sensitivity and Specificity MeSH
- Subtraction Technique MeSH
- Radiographic Image Enhancement methods MeSH
- Imaging, Three-Dimensional methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Segmentation of the gray and white matter (GM, WM) of the human spinal cord in MRI images as well as the analysis of spinal cord diffusivity are challenging. When appropriately segmented, diffusion tensor imaging (DTI) of the spinal cord might be beneficial in the diagnosis and prognosis of several diseases. PURPOSE: To evaluate the applicability of a semiautomatic algorithm provided by ITK-SNAP in classification mode (CLASS) for segmenting cervical spinal cord GM, WM in MRI images and analyzing DTI parameters. STUDY TYPE: Prospective. SUBJECTS: Twenty healthy volunteers. SEQUENCES: 1.5T, turbo spin echo, fast field echo, single-shot echo planar imaging. ASSESSMENT: Three raters segmented the tissues by manual, CLASS, and atlas-based methods (Spinal Cord Toolbox, SCT) on T2 -weighted and DTI images. Masks were quantified by similarity and distance metrics, then analyzed for repeatability and mutual comparability. Masks created over T2 images were registered into diffusion space and fractional anisotropy (FA) values were statistically evaluated for dependency on method, rater, or tissue. STATISTICAL TESTS: t-test, analysis of variance (ANOVA), coefficient of variation, Dice coefficient, Hausdorff distance. RESULTS: CLASS segmentation reached better agreement with manual segmentation than did SCT (P < 0.001). Intra- and interobserver repeatability of SCT was better for GM and WM (both P < 0.001) but comparable with CLASS in entire spinal cord segmentation (P = 0.17 and P = 0.07, respectively). While FA values of whole spinal cord were not influenced by choice of segmentation method, both semiautomatic methods yielded lower FA values (P < 0.005) for GM than did the manual technique (mean differences 0.02 and 0.04 for SCT and CLASS, respectively). Repeatability of FA values for all methods was sufficient, with mostly less than 2% variance. DATA CONCLUSION: The presented semiautomatic method in combination with the proposed approach to data registration and analyses of spinal cord diffusivity can potentially be used as an alternative to atlas-based segmentation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1217-1227.
- MeSH
- Algorithms MeSH
- Anisotropy MeSH
- White Matter diagnostic imaging MeSH
- Diffusion Magnetic Resonance Imaging * MeSH
- Adult MeSH
- Echo-Planar Imaging * MeSH
- Cervical Cord diagnostic imaging MeSH
- Humans MeSH
- Young Adult MeSH
- Observer Variation MeSH
- Image Processing, Computer-Assisted methods MeSH
- Spinal Cord Injuries diagnostic imaging MeSH
- Prospective Studies MeSH
- Gray Matter diagnostic imaging MeSH
- Machine Learning MeSH
- Diffusion Tensor Imaging * MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
A model-based radiostereometric analysis (MBRSA) is a method for precise measurement of prosthesis migration, which does not require marking the implant with tantalum beads. Instead, the prosthesis pose is typically recovered using a feature-based 2D-3D registration of its virtual model into a stereo pair of radiographs. In this study, we evaluate a novel intensity-based formulation of previously published nonoverlapping area (NOA) approach. The registration is capable of performing with both binary radiographic segmentations and nonsegmented X-ray images. In contrast with the feature-based version, it is capable of dealing with unreliable parts of prosthesis. As the straightforward formulation allows efficient acceleration using modern graphics adapters, it is possible to involve precise high-poly virtual models. Moreover, in case of binary segmentations, the nonoverlapping area is simply interpretable and useful for indicating the accuracy of the registration outcome. In silico and phantom evaluations were performed using a cementless Zweymüller femoral stem and its reverse engineered (RE) model. For initial pose estimates with difference from the ground-truth limited to ±4 mm and ±4°, respectively, the mean absolute translational error was not higher than 0.042 ± 0.035 mm. The error in rotation around the proximodistal axis was 0.181 ± 0.265°, and the error for the remaining axes was not higher than 0.035 ± 0.037°.
- MeSH
- Equipment Failure Analysis methods MeSH
- Phantoms, Imaging MeSH
- Femur diagnostic imaging MeSH
- Hip Prosthesis MeSH
- Humans MeSH
- Image Processing, Computer-Assisted methods MeSH
- Radiostereometric Analysis methods MeSH
- Tantalum MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Characterization of brain infarct lesions in rodent models of stroke is crucial to assess stroke pathophysiology and therapy outcome. Until recently, the analysis of brain lesions was performed using two techniques: (1) histological methods, such as TTC (Triphenyltetrazolium chloride), a time-consuming and inaccurate process; or (2) MRI imaging, a faster, 3D imaging method, that comes at a high cost. In the last decade, high-resolution micro-CT for 3D sample analysis turned into a simple, fast, and cheaper solution. Here, we successfully describe the application of brain contrasting agents (Osmium tetroxide and inorganic iodine) for high-resolution micro-CT imaging for fine location and quantification of ischemic lesion and edema in mouse preclinical stroke models. We used the intraluminal transient MCAO (Middle Cerebral Artery Occlusion) mouse stroke model to identify and quantify ischemic lesion and edema, and segment core and penumbra regions at different time points after ischemia, by manual and automatic methods. In the transient-ischemic-attack (TIA) mouse model, we can quantify striatal myelinated fibers degeneration. Of note, whole brain 3D reconstructions allow brain atlas co-registration, to identify the affected brain areas, and correlate them with functional impairment. This methodology proves to be a breakthrough in the field, by providing a precise and detailed assessment of stroke outcomes in preclinical animal studies.
- MeSH
- Stroke * diagnostic imaging pathology MeSH
- Infarction, Middle Cerebral Artery diagnostic imaging pathology MeSH
- Iodine * MeSH
- Disease Models, Animal MeSH
- Mice MeSH
- Osmium Tetroxide MeSH
- X-Ray Microtomography MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
This article deals with non-contact measurement of temperature in a human faces and describes program for the evaluation of temperature changes. It describes the algorithm of the program, the possibility of using and further deals with the possibilities of the segmentation of thermal images. Variety of image processing methods were used to design this algorithm including registration of images, segmentation using k-means clustering, Hough transformation, thresholding and others. The aim is to distinguish a human face from the hair and background. It also describes the possibility of detection of individual facial details. The functionality of those procedures was tested on experimental data.
Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.
