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
• Algorithms MeSH
• DNA * genetics   MeSH
• Image Processing, Computer-Assisted * MeSH
• Polymerase Chain Reaction MeSH
• Reproducibility of Results MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• 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.

• Keywords
• Cryo-electron microscopy, Image processing, Scipion, Single particle,
• MeSH
• Algorithms * MeSH
• Cryoelectron Microscopy methods   MeSH
• Macromolecular Substances ultrastructure   MeSH
• Molecular Biology methods   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Workflow MeSH
• Computational Biology MeSH
• Single Molecule Imaging methods   MeSH
• Imaging, Three-Dimensional methods   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Macromolecular Substances 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.

• Keywords
• Fiji, ImageJ, image analysis, imaging, microscopy, open source software,
• MeSH
• Image Processing, Computer-Assisted * MeSH
• Software * MeSH
• Artificial Intelligence * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't 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.

• Keywords
• Data analysis, Diomedical engineering, Image processing, Skin cancer diagnostics,
• MeSH
• Skin MeSH
• Humans MeSH
• Skin Neoplasms * diagnosis   epidemiology   MeSH
• Image Processing, Computer-Assisted MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Geographicals
• Canada 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
• Algorithms MeSH
• Cell Physiological Phenomena * MeSH
• Microscopy, Confocal instrumentation   MeSH
• Luminescent Proteins metabolism   MeSH
• Image Cytometry instrumentation   methods   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Software * MeSH
• Green Fluorescent Proteins MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH
• Names of Substances
• Luminescent Proteins MeSH
• Green Fluorescent Proteins MeSH

• MeSH
• Algorithms * MeSH
• Image Interpretation, Computer-Assisted instrumentation   methods   MeSH
• Humans MeSH
• Molecular Imaging methods   MeSH
• Computer Graphics instrumentation   trends   MeSH
• Signal Processing, Computer-Assisted instrumentation   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Letter MeSH
• Research Support, Non-U.S. Gov't 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.

• Keywords
• Corona, Image processing,
• Publication type
• Journal Article 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
• Humans MeSH
• Image Processing, Computer-Assisted * MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Editorial 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.

• Keywords
• Bit depth, Bitmap, Computer graphics, Data visualization, ImageJ, Inkscape, Microscopy, Raster, Resolution, Vector,
• MeSH
• Microscopy methods   MeSH
• Computers MeSH
• Computer Graphics MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Software * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't 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.

• Keywords
• Blood vessels, Classification, Feature extraction, Fundus image, Glaucoma, Wavelet transform,
• 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