Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large datasets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs).
The MATLAB code described and provided along with this manuscript is designed for image processing of skeletal muscle immunofluorescent histological sections. The program allows for semi-automated fiber detection along with user correction. The output of the code provides data in accordance with established standards of practice. The results of the program have been validated using a small set of wild-type and mdx muscle sections. This program is the first freely available and open source image processing program designed to automate analysis of skeletal muscle histological sections.
Skeletal muscle has a robust ability to adapt to the pattern of use and to regenerate following injury. These are often quantified using histological techniques. However, the methods for this quantification remain disparate among investigators and often require painstaking manual procedures [1, 2]. The goal of this work is to provide a widely available image processing software package specifically designed for muscle histological analysis.
All of the methods discussed above are commonly performed using immunofluorescence, which provides high contrast in stained and unstained structures. We have developed MATLAB (MATLAB and Image Processing Toolbox 2014a, MathWorks) scripts bundled into a MATLAB App (see Availability and Requirements) that automate, or partially automate determination of fiber size, fiber type, centrally nucleated fibers, and capillary density. These programs are created to comply with standard operating procedures developed by TREAT-NMD when available using sophisticated boundary detection algorithms . The software also includes built-in image editing to manually inspect and manipulate fiber boundaries. Fully automated fiber size determination as well as fiber types and CNFs may be possible with adequate image acquisition [9, 20]. However, these newly designed fully automated programs are not yet available  and/or have a significant cost . Additionally, allowing the user to have manual control over some aspect of image processing allows users to maintain the fidelity established by manual techniques. The open nature of this software also allows custom usage and further advancement of the methods. For users that do not have access to a MATLAB license or the image processing toolbox we have compiled an .exe file that runs using the freely available MATLAB Runtime Compiler (MCR) version 8.3 ( ). Automating a large portion of muscle histology makes it feasible to analyze full muscle cross-sections, eliminating variability introduced by selecting only a portion of the cross section for analysis. This software is validated with muscles from mdx mice, which have many alterations of muscle fiber morphology compared to wild-type mice . The purpose of this study is to develop freely available automatic and standardized image segmentation platform and validate the program using standard muscle histological analysis.
The software has built in several steps of image processing tools within the same script. Initially the user must select an image file (.bmp, .jpg, .png, and .tif.) to be processed. Following selection of the appropriate file the user is provided a list of the built in functions (Figure 1A). A representative image of a soleus muscle from a 1-year-old mdx mouse is used which has been immunostained with laminin (red) and slow myosin heavy chain (green) as well as DAPI (blue) (Figure 1B and C). Dystrophic muscle can be more difficult to process automatically due greater interstitial spaces and the examples highlight some manual adjustments that may be required. The software includes an Excel (Microsoft) file containing default parameter values (Table 1), which may be altered to the needs of the user. The details of each parameter are discussed in the relevant section below.
In addition the gains in robust analysis Table 3 demonstrates that SMASH greatly reduces the time required to analyze images. Manual editing of the fiber mask is still required in the majority of muscle sections for both initial segmentation and fiber filtering and take the majority of the processing time. However, with SMASH this manual editing is reduced to just a few minutes in the case of the soleus muscles tested. The time gains are especially significant when doing multiple analyses on the same image, as manually editing the mask is the major time consumer and additional functions are able to be processed in just seconds.
The software package based in MATLAB provides image processing tools to analyze immunofluorescent muscle cross-sections. The semi-automatic fiber segmentation functions provide advanced algorithms for fiber segmentation as well as provide an interface for users to manually correct any errors. The histological analysis includes functions for fiber CSA, fiber Feret diameter, fiber typing, CNFs, and capillary density. These functions produced expected results comparing wild-type and dystrophic mouse muscle. These functions may be purposed for other analyses. This open source platform provides users a framework to create their own functions or modification of previously incorporated functions. Automated functions improve the speed and consistency of skeletal muscle histological analysis. Although it requires a MATLAB license, this is the only freely available software designed for the analysis of skeletal muscle histology.
Concentrating on the principles and techniques of image processing, this book provides an in-depth presentation of key topics, including many techniques not included in introductory texts. Practical implementation of the various image processing algorithms is an important step in learning the subject, and computer packages such as MATLAB facilitate this without the need to learn more complex programming languages. Whilst two chapters are devoted to the MATLAB programming environment and the image processing toolbox, the use of image processing algorithms using MATLAB is emphasised throughout the book, and every chapter is accompanied by a collection of exercises and programming assignments. Including coverage of colour and video image processing as well as object recognition, the book is augmented with supplementary MATLAB code and hints and solutions to problems are also provided.
The Computational Imaging Group of the TU Delft, to be called CI, has developed and is the owner of theimage processing library written in C and all related documentation known as DIPlib, hereafter called SOFTWARE.
Note: This example references the Low Dose CT Grand Challenge data set, as accessed on May 1, 2021. The example uses chest images from the data set that are now under restricted access. To run this example, you must have a compatible data set with low-dose and high-dose CT images, and adapt the data preprocessing and training options to suit your data.
Teaching image processing with MATLAB to physics studentsImage processing is an engaging activity appropriately taught to beginning students in STEM fields. This essay describes briefly outcomes of teaching image processing to beginning undergraduate physics majors.
Demosaicing is the only required operation to convert single-channel RAW data to a three-channel RGB image. However, without additional image processing operations, the resulting RGB image has subjectively poor visual quality.
A traditional image processing pipeline performs a combination of additional operations including denoising, linearization, white-balancing, color correction, brightness adjustment, and contrast adjustment . The challenge of designing a pipeline lies in refining algorithms to optimize the subjective appearance of the final RGB image regardless of variations in the scene and acquisition settings.
Deep learning techniques enable direct RAW to RGB conversion without the necessity of developing a traditional processing pipeline. For instance, one technique compensates for underexposure when converting RAW images to RGB . This example shows how to convert RAW images from a lower end phone camera to RGB images that approximate the quality of a higher end DSLR camera.
To simulate the minimal traditional processing pipeline, demosaic the RGGB Bayer pattern of the RAW data using the demosaic function. Display the processed image and brighten the display. Compared to the target RGB image, the minimally-processed RGB image is dark and has imbalanced colors and noticeable artifacts. A trained RAW-to-RGB network performs preprocessing operations so that the output RGB image resembles the target image.
The MAE loss penalises the L1 distance between samples of the network predictions and samples of the target image. L1 is often a better choice than L2 for image processing applications because it can help reduce blurring artifacts . This loss is implemented using the maeLoss helper function defined in the Supporting Functions section of this example.
Because of sensor differences between the phone camera and DSLR used to acquire the full-resolution test images, the scenes are not registered and are not the same size. Reference-based comparison of the full-resolution images from the network and the DSLR ISP is difficult. However, a qualitative comparison of the images is useful because a goal of image processing is to create an aesthetically pleasing image. 2b1af7f3a8