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
In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and 'the best' method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross-section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category - biological samples - is shown.
- MeSH
- Algorithms * MeSH
- Microscopy * methods MeSH
- Mice MeSH
- Image Processing, Computer-Assisted methods standards MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
- MeSH
- Algorithms MeSH
- Financing, Organized MeSH
- Hematoma, Subdural, Intracranial diagnosis MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods utilization MeSH
- Markov Chains MeSH
- Tomography, X-Ray Computed methods utilization MeSH
- Image Processing, Computer-Assisted MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.
- MeSH
- Biomechanical Phenomena MeSH
- Diagnostic Imaging * methods utilization MeSH
- Humans MeSH
- Mechanical Phenomena MeSH
- Tomography, X-Ray Computed methods utilization MeSH
- Image Processing, Computer-Assisted methods utilization MeSH
- Visible Human Projects MeSH
- Statistics as Topic MeSH
- Models, Theoretical * MeSH
- Tissues physiology MeSH
- Imaging, Three-Dimensional * methods utilization MeSH
- Check Tag
- Humans MeSH
Ciba Foundation symposium ; 184
[1st ed.] VIII, 347 s. : obr., tab. ; 23 cm
- MeSH
- Neurophysiology MeSH
- Ocular Physiological Phenomena MeSH
- Vision, Ocular physiology MeSH
- Publication type
- Congress MeSH
- Conspectus
- Lékařské vědy. Lékařství
- NML Fields
- fyziologie
- neurovědy
- oftalmologie
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.
Although the field of sleep study has greatly developed over recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory. This examination measures several vital signals by polysomnograph during a full night's sleep using multiple sensors connected to the patient's body. Nevertheless, despite being the gold standard, the sensors and the unfamiliar environment's connection inevitably impact the quality of the patient's sleep and the examination itself. Therefore, with the novel development of accurate and affordable 3D sensing devices, new approaches for non-contact sleep study have emerged. These methods utilize different techniques to extract the same breathing parameters but with contactless methods. However, to enable reliable remote extraction, these methods require accurate identification of the basic region of interest (ROI), i.e., the patient's chest area. The lack of automated ROI segmenting of 3D time series is currently holding back the development process. We propose an automatic chest area segmentation algorithm that given a time series of 3D frames containing a sleeping patient as input outputs a segmentation image with the pixels that correspond to the chest area. Beyond significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can enable a more precise feature extraction. In addition, further tests of the algorithm on existing data demonstrate its ability to improve the sensitivity of a prior solution that uses manual ROI selection. The approach is on average 46.9% more sensitive with a maximal improvement of 220% when compared to manual ROI. All mentioned can pave the way for placing non-contact algorithms as leading candidates to replace existing traditional methods used today.
- MeSH
- Algorithms * MeSH
- Respiration MeSH
- Humans MeSH
- Image Processing, Computer-Assisted methods MeSH
- Polysomnography MeSH
- Sleep MeSH
- Imaging, Three-Dimensional * methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.
- MeSH
- Algorithms * MeSH
- Optic Disk pathology MeSH
- Adult MeSH
- Glaucoma pathology MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Reproducibility of Results MeSH
- Retinoscopy methods MeSH
- Pattern Recognition, Automated methods MeSH
- Aged MeSH
- Sensitivity and Specificity MeSH
- Machine Learning MeSH
- Subtraction Technique MeSH
- Wavelet Analysis MeSH
- Image Enhancement methods MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't 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
