Image compression in morphometry studies requiring 21 CFR Part 11 compliance: procedure is key with TIFFs and various JPEG compression strengths

Quantitative pixel intensity- and color-based image analysis on minimally compressed files: implications for whole-slide imaging

Current best practice in the quantitative analysis of microscopy images dictates that image files should be saved in a lossless format such as TIFF. Use of lossy files, including those processed with the JPEG algorithm, is highly discouraged due to effects of compression on pixel characteristics. However, with the growing popularity of whole-slide imaging (WSI) and its attendant large file sizes, compressed image files are becoming more prevelent. This prompted us to perform a color-based quantitative pixel analysis of minimally compressed WSI images. Sections from three tissues stained with one of three reagents representing the colors blue (hematoxylin), red (Oil-Red-O), and brown (immunoperoxidase) were scanned with a whole slide imager in triplicate at 20x, 40x, and 63x magnifications. The resulting files were in the form of a BigTIFF with a JPEG compression automatically applied during acquisition. Images were imported into analysis software, six regions of interest were applied to various morphological locations, and the areas assessed for the color of interest. Whereas the number of designated weakly or strongly positive pixels was variable across the triplicate scans for the individual regions of interest, the total number of positive pixels was consistent. These results suggest that total positivity for a specific color representing a histochemical or immunohistochemical stain can be adequately quantitated on compressed images, but degrees of positivity (e.g., weak vs. strong) may not be as reliable. However, it is important to assess individual whole-slide imagers, file compression level and algorithm, and analysis software for reproducibility.

Modern Imaging Technologies in Toxicologic Pathology: An Overview

Modern imaging technology, now utilized in most biomedical research areas (bioimaging), enables the detection and visualization of biological processes at various levels of the molecule, organelle, cell, tissue, organ and/or whole body. In toxicologic pathology, the impact of modern imaging technology is becoming apparent from digital histopathology to novel molecular imaging for in vivo studies. This overview summarizes recent progresses in digital microscopy imaging and newly developed digital slide techniques. Applications of virtual microscopy imaging are discussed and compared to traditional optical microscopy reading. New generation digital pathology approaches, including automatic slide inspection, digital slide databases and image management are briefly introduced. Commonly used in vivo preclinical imaging technologies are also summarized. While most of these new imaging techniques are still undergoing rapid development, it is important that toxicologic pathologists embrace and utilize these technologies as advances occur.

Digital images are data: and should be treated as such

The scientific community has become very concerned about inappropriate image manipulation. In journals that check figures after acceptance, 20-25% of the papers contained at least one figure that did not comply with the journal's instructions to authors. The scientific press continues to report a small, but steady stream of cases of fraudulent image manipulation. Inappropriate image manipulation taints the scientific record, damages trust within science, and degrades science's reputation with the general public. Scientists can learn from historians and photojournalists, who have provided a number of examples of attempts to alter or misrepresent the historical record. Scientists must remember that digital images are numerically sampled data that represent the state of a specific sample when examined with a specific instrument. These data should be carefully managed. Changes made to the original data need to be tracked like the protocols used for other experimental procedures. To avoid pitfalls, unexpected artifacts, and unintentional misrepresentation of the image data, a number of image processing guidelines are offered.

Image compression impact on quantitative angiogenesis analysis of ovarian epithelial neoplasms

OBJECTIVE

This study aims to investigate the impact of digital image compression on manual and semiautomatic quantification of angiogenesis in ovarian epithelial neoplasms (including benign, borderline, and malignant specimens).

DESIGN

We examined 405 digital images (obtained from a previously validated computer-assisted analysis system), which were equally divided into 5 groups: images captured in Tagged Image File Format (TIFF), low and high compression Joint Photographic Experts Group (JPEG) formats, and low and high compression JPEG images converted from the TIFF files.

MEASUREMENTS

Microvessel density counts and CD34 endothelial areas manually and semiautomatically determined from TIFF images were compared with those from the other 4 groups.

RESULTS

Mostly, the correlations between TIFF and JPEG images were very high (intraclass correlation coefficients >0.8), especially for low compression JPEG images obtained by capture, regardless of the variable considered. The only exception consisted in the use of high compression JPEG files for semiautomatic microvessel density counts, which resulted in intraclass correlation coefficients of <0.7. Nonetheless, even then, interconversion between TIFF and JPEG values could be successfully achieved using prediction models established by linear regression.

CONCLUSION

Image compression does not seem to significantly compromise the accuracy of angiogenesis quantitation in the ovarian epithelial tumors, although low compression JPEG images should always be preferred over high compression ones.

Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

Background

Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.

Methods

Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.

Results

Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.

Conclusions

The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995

Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images

Digital imaging has provided scientists with new opportunities to acquire and manipulate data using techniques that were difficult or impossible to employ in the past. Because digital images are easier to manipulate than film images, new problems have emerged. One growing concern in the scientific community is that digital images are not being handled with sufficient care. The problem is twofold: (1) the very small, yet troubling, number of intentional falsifications that have been identified, and (2) the more common unintentional, inappropriate manipulation of images for publication. Journals and professional societies have begun to address the issue with specific digital imaging guidelines. Unfortunately, the guidelines provided often do not come with instructions to explain their importance. Thus they deal with what should or should not be done, but not the associated 'why' that is required for understanding the rules. This article proposes 12 guidelines for scientific digital image manipulation and discusses the technical reasons behind these guidelines. These guidelines can be incorporated into lab meetings and graduate student training in order to provoke discussion and begin to bring an end to the culture of "data beautification".

Application of wavelet denoising to improve compression efficiency while preserving integrity of digital micrographs

Modern microscopy methods require efficient image compression techniques owing to collection of up to thousands of images per experiment. Current irreversible techniques such as JPEG and JPEG2000 are not optimized to preserve the integrity of the scientific data as required by 21 CFR part 11. Therefore, to construct an irreversible, yet integrity-preserving compression mechanism, we establish a model of noise as a function of signal in our imaging system. The noise is then removed with a wavelet shrinkage algorithm whose parameters are adapted to local image structure. We ascertain the integrity of the denoised images by measuring changes in spatial and intensity distributions of registered light in the biological images and estimating changes of the effective microscope MTF. We demonstrate that the proposed denoising procedure leads to a decrease in image file size when a reversible JPEG2000 coding is used and provides better fidelity than irreversible JPEG and JPEG2000 at the same compression ratio. We also demonstrate that denoising reduces image artefacts when used as a pre-filtering step prior to irreversible image coding.