The digital polymerase chain reaction (dPCR) is an irreplaceable variant of PCR techniques due to its capacity for absolute quantification and detection of rare deoxyribonucleic acid (DNA) sequences in clinical samples. Image processing methods, including micro-chamber positioning and fluorescence analysis, determine the reliability of the dPCR results. However, typical methods demand high requirements for the chip structure, chip filling, and light intensity uniformity. This research developed an image-to-answer algorithm with single fluorescence image capture and known image-related error removal. We applied the Hough transform to identify partitions in the images of dPCR chips, the 2D Fourier transform to rotate the image, and the 3D projection transformation to locate and correct the positions of all partitions. We then calculated each partition's average fluorescence amplitudes and generated a 3D fluorescence intensity distribution map of the image. We subsequently corrected the fluorescence non-uniformity between partitions based on the map and achieved statistical results of partition fluorescence intensities. We validated the proposed algorithms using different contents of the target DNA. The proposed algorithm is independent of the dPCR chip structure damage and light intensity non-uniformity. It also provides a reliable alternative to analyze the results of chip-based dPCR systems.

• MeSH
• algoritmy MeSH
• DNA * genetika   MeSH
• počítačové zpracování obrazu * MeSH
• polymerázová řetězová reakce MeSH
• reprodukovatelnost výsledků MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• DNA * MeSH

Cryo-electron microscopy has established as a mature structural biology technique to elucidate the three-dimensional structure of biological macromolecules. The Coulomb potential of the sample is imaged by an electron beam, and fast semi-conductor detectors produce movies of the sample under study. These movies have to be further processed by a whole pipeline of image-processing algorithms that produce the final structure of the macromolecule. In this chapter, we illustrate this whole processing pipeline putting in value the strength of "meta algorithms," which are the combination of several algorithms, each one with different mathematical rationale, in order to distinguish correctly from incorrectly estimated parameters. We show how this strategy leads to superior performance of the whole pipeline as well as more confident assessments about the reconstructed structures. The "meta algorithms" strategy is common to many fields and, in particular, it has provided excellent results in bioinformatics. We illustrate this combination using the workflow engine, Scipion.

• Klíčová slova
• Cryo-electron microscopy, Image processing, Scipion, Single particle,
• MeSH
• algoritmy * MeSH
• elektronová kryomikroskopie metody   MeSH
• makromolekulární látky ultrastruktura   MeSH
• molekulární biologie metody   MeSH
• počítačové zpracování obrazu metody   MeSH
• průběh práce MeSH
• výpočetní biologie MeSH
• zobrazení jednotlivé molekuly metody   MeSH
• zobrazování trojrozměrné metody   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• makromolekulární látky 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.

• Klíčová slova
• Fiji, ImageJ, image analysis, imaging, microscopy, open source software,
• MeSH
• počítačové zpracování obrazu * MeSH
• software * MeSH
• umělá inteligence * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

• Klíčová slova
• Data analysis, Diomedical engineering, Image processing, Skin cancer diagnostics,
• MeSH
• kůže MeSH
• lidé MeSH
• nádory kůže * diagnóza   epidemiologie   MeSH
• počítačové zpracování obrazu MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Geografické názvy
• Kanada MeSH

The successful development of visualization techniques for live cell imaging leads to the development of suitable software for the acquisition and processing of multidimensional image data. This report compares several possible approaches to image acquisition and processing in confocal in vivo microscopy and suggests new alternatives to the published methods. Special attention is paid to spinning disk systems based either on a classical Nipkow disk or on the microlens principle. This study shows how to optimize image acquisition process in live cell studies using camera binning feature and how to perform object tracking using a new fast image registration method based on the graph theory.

• MeSH
• algoritmy MeSH
• fyziologie buňky * MeSH
• konfokální mikroskopie přístrojové vybavení   MeSH
• luminescentní proteiny metabolismus   MeSH
• obrazová cytometrie přístrojové vybavení   metody   MeSH
• počítačové zpracování obrazu metody   MeSH
• software * MeSH
• zelené fluorescenční proteiny MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH
• Názvy látek
• luminescentní proteiny MeSH
• zelené fluorescenční proteiny MeSH

• MeSH
• algoritmy * MeSH
• interpretace obrazu počítačem přístrojové vybavení   metody   MeSH
• lidé MeSH
• molekulární zobrazování metody   MeSH
• počítačová grafika přístrojové vybavení   trendy   MeSH
• počítačové zpracování signálu přístrojové vybavení   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• dopisy MeSH
• práce podpořená grantem MeSH

UNLABELLED: Extreme ultra-violet images of the corona contain information over a wide range of spatial scales, and different structures such as active regions, quiet Sun, and filament channels contain information at very different brightness regimes. Processing of these images is important to reveal information, often hidden within the data, without introducing artefacts or bias. It is also important that any process be computationally efficient, particularly given the fine spatial and temporal resolution of Atmospheric Imaging Assembly on the Solar Dynamics Observatory (AIA/SDO), and consideration of future higher resolution observations. A very efficient process is described here, which is based on localised normalising of the data at many different spatial scales. The method reveals information at the finest scales whilst maintaining enough of the larger-scale information to provide context. It also intrinsically flattens noisy regions and can reveal structure in off-limb regions out to the edge of the field of view. We also applied the method successfully to a white-light coronagraph observation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11207-014-0523-9) contains supplementary material, which is available to authorized users.

• Klíčová slova
• Corona, Image processing,
• Publikační typ
• časopisecké články MeSH

There are various modern systems for the measurement and consequent acquisition of valuable patient's records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...].

• MeSH
• lidé MeSH
• počítačové zpracování obrazu * MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• úvodníky MeSH

Studying morphogenesis is unthinkable without visualizing shapes, and sharing the results of such studies critically depends on communicating image data. Despite a wealth of literature dealing with acquisition and analysis of image data, visualizing them for publication or presentation purposes remains a craft learned mainly by experience. This chapter provides a practical guide to producing publication-grade illustrations out of raw microscopic (or other) digital images, using mostly or exclusively free software, and points out some common problems and their solutions.

• Klíčová slova
• Bit depth, Bitmap, Computer graphics, Data visualization, ImageJ, Inkscape, Microscopy, Raster, Resolution, Vector,
• MeSH
• mikroskopie metody   MeSH
• počítače MeSH
• počítačová grafika MeSH
• počítačové zpracování obrazu metody   MeSH
• software * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

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.

• Klíčová slova
• Image analysis, image segmentation, microscopic images, performance evaluation,
• MeSH
• algoritmy * MeSH
• mikroskopie * metody   MeSH
• myši MeSH
• počítačové zpracování obrazu metody   normy   MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH