Creating clear and informative image-based figures for scientific publications

Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in 3 fields; plant sciences, cell biology, and physiology (n = 580 papers). Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

Single- and Multi-Step Image Enlargement for Medical Image Coding

Background: Some interpolators cannot be used in an image magnification problem in a freely scalable form. For instance, when we want to magnify an image to a 16-time bigger scale, some interpolators have to do this process in two steps including two 4-time magnification steps, however, some are able to do it directly. Materials and Methods: For generating data of this study, MATLAB as a simulator has been used. Bi-; Linear (BL) and Cubic Convolution (CC) interpolators are the two applied re-samplers in the reconstruction of digital images. Results: Data shows that the performance of both free-size interpolators (BL and CC) is obviously different in both direct and indirect pixel reconstruction. Conclusion: The acquired data shows a less error in the condition of direct interpolation. The relative results of experiments are different from the type of core interpolators (BL and CC).

Creating Clear and Informative Image-based Figures for Scientific Publications

Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in three fields; plant sciences, cell biology and physiology. Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

Biomechanical Evaluation of the Accuracy in Radiographic Assessment of Femoral Component Migration Measurement after Total Hip Arthroplasty

Introduction: Implant subsidence is one criteria utilized to monitor for prosthesis loosening after total hip arthroplasty (THA) with initial implant subsidence assessment often done utilizing plain radiographs. The specific aim of this study was to identify the most reliable references when using plain radiographs to establish an image magnification with the goals being easy to use, inexpensive, reliable, and accurate. Methods: Two femoral stem implants (stem lengths: 127mm, 207mm) were utilized to simulate hemiarthroplasty of the hip with composite femurs. Different combinations of femoral stem distances from the radiographic film (ODD), source-detector differences (SDD), hip rotation, and hip flexion were elected. Standardized anterior-posterior pelvis for each parameter combination setup were taken. Radiographic measurements (head diameter, stem length, stem seating length) were undertaken five times by three examiners. Radiographic image magnification factors were generated from two references (head diameter and stem length). Radiograph measurement reproducibility and stem seating length errors using these magnification factors were evaluated. Results: High level of repeated measurements reliability was found for head diameter (99 ± 0%) and stem length (90 ± 7%) measurements, whereas seating length measurements were less reliable (76 ± 6%). Stem length error using the femoral head magnification factor yielded 11% accuracy. Stem seating length error using both magnification factors were not reliable (< 7% accuracy). All parameters, except SDD, showed significant effect on calibrated measurement error. Conclusions: Current methods of assessing implant subsidence after THA using plain radiographs are inaccurate or reliable. Clinicians should recognize these limitations and be cautious when diagnosing implant stability using plain radiographs alone.

Image Magnification Based on Bicubic Approximation with Edge as Constraint

Image magnification can be reduced to construct an approximation surface with data points in the image while keeping image details and edge features. In this paper, a new image magnification method is proposed by constructing piecewise bicubic polynomial surfaces constrained by edge features. The main innovation includes three parts. First, on the small adjacent area of each pixel, the new method constructs a quadratic polynomial sampling patch to approximate pixels on the small neighborhood with edge features as constraints. All quadric polynomial sampling patches are weighted to generate piecewise whole bicubic polynomial sampling surface. Second, a technique for calculating the error image is proposed: the error image is used to construct a correction surface to improve the accuracy and visual effect of the magnified image. Finally, in order to improve the accuracy of the approximation surface, a technology of balancing polynomial coefficients is put forward. Experimental results show that, compared with other methods, the proposed method makes better use of the local feature information of the image, which not only improves the PSNR/SSIM numerical accuracy of the magnified image but also improves the visual effect of the magnified image.