Image Processing with MATLAB: Applications in Medicine and Biology explains complex, theory-laden topics in image processing through examples and MATLAB algorithms. It describes classical as well emerging areas in image processing and analysis. Providing many unique MATLAB codes and functions throughout, the book covers the theory of probability an
Image Processing With Matlab Applications In Medicine And Biology Pdf Free Download
Written for undergraduate and graduate students, researchers, and medical physicists, this book helps readers understand advanced concepts in image processing such as Markov random field modeling by providing algorithms and by demonstrating their application on real-world problems in medicine and biology. The book provides extensive coverage of the fundamental tools in image processing and analysis, such as numerical derivatives, image simulation, processing, and manipulation of 3D images, demonstrated with MATLAB code.
Some highly useful applications have been proposed and made available to the scientific community. OME [1, 2], as well as its derivative product OMERO [3], provide a widely used enterprise solution to storing, handling and processing biological and medical images. Another useful application is CellProfiler [4], which is a tool designed specifically for analyzing microscopy images of cells. Other tools focus on single tasks in the process of biological image analysis. An example is the utility FindSpots, which detects objects (e.g., cells) in an image based upon their intensity and size.
Practically, the set of image features extracted from the raw pixels and the several transforms require significant CPU resources. For example, the large set of image features can be extracted from one 256 256 image in 100 seconds using a system with a 2.6 GHZ AMD Opteron and 2 GB of RAM. Computing the smaller feature set requires 38 seconds. The time required for processing a single image increases linearly with the number of pixels, as shown by Figure 1. However, since memory access can become slower when large amounts of memory are used, performance suffers considerably above a certain image size. This can be evident by the sharp decrease in response time when the image size gets larger than 512 512 pixels. In these cases, users may consider breaking very large images into tiles, as will be described later in the paper. Since the images are processed sequentially, the overall response time is linear to the number of images in the dataset.
In the sense of memory usage, wndchrm stores the original image in the main memory along with the 10 image transforms (when the larger feature set is used). Since each pixel requires 11 bytes (eight bytes for the intensity and one additional byte for each of the three color channels), the total memory allocated for the image is 11 11 width height bytes. Computing the Chebyshev-Fourier features requires additional 100 MB. Once image features are extracted from an image, all memory blocks are freed, so that the memory used by wndchrm does not grow when more images are processed.
The purpose of the .sig files is to store the feature values of an image or image tile. I.e., wndchrm creates a .sig file for each tile that it processes, so that the number of .sig files of each image is equal to the number of tiles each image is divided into (one by default). If wndchrm finds an existing .sig file, it avoids recomputing these features and moves on to the next tile. Since the different instances of wndchrm communicate using the .sig files, different machines that have access to the same disk space can effectively process the same dataset simultaneously without wasting their CPU resources by processing the same tiles. To utilize this feature, the user should make sure that the number of running instances of wndchrm is equal to the number of available cores. For instance, a quad-core machine will be most effective if four instances of wndchrm run simultaneously. Since wndchrm does not automatically determine the number of available cores, the user should manually execute the desired number of wndchrm instances (usually by repeating the same command line).
The efficacy of the proposed image analysis utility was tested using iicbu-2008 benchmark suite of biological image datasets [25], which includes biological images of different subjects such as organelles, cells, tissues, and full organisms using different magnifications and different types of microscopy. The number of classes, number of images, microscopy, image formats, and image sizes are specified in Table 2. This benchmark suite represents a broad range of real-life biological imaging problems. The performance of wndchrm on each of the datasets is described by Figure 3. Comparing some of these performance figures to the reported performance of application-specific image classifiers shows that wndchrm is favorably comparable, as can be learned from Table 3. The informativeness of the different image features and their importance for each of these datasets is described in [5]. The benchmark suite of iicbu-2008, as well as sample images of each dataset, are available for free download at [26].
Many biological problems focus not only on the classification of different sets of images, but also on assessing the similarities between the different classes. The similarities between the classes are reflected by the similarity table in the report of the "test" command, and can be visualized using a phylogeny generated by the phylip package. For example, Table 4 shows the similarity values between different classes of C. elegans terminal bulb images, such that each class of images was taken at a different age of 0, 2, 4, 6, 8, 10, or 12 days. As the table shows, the similarities between the different classes correspond to the age differences. Visualizing these data using a phylogeny provides an ordered list of the different ages, shown by Figure 4. This order, inferred automatically by wndchrm, is in agreement with the chronological ages of the worms. The only exception is day 0, in which the worms still grow, and therefore expected to be significantly different from adult worms. An interesting observation is the large difference between day 8 and day 10. This experiment demonstrates that wndchrm can automatically deduce the continuos nature of aging by measuring the similarities between images taken at different ages. Example terminal bulb images as well as a downloadable archive of the entire set can be found at [26].
Image Processing with MATLAB: Applications in Medicine and Biology explains complex, theory-laden topics in image processing through examples and MATLAB algorithms. It describes classical as well emerging areas in image processing and analysis. Providing many unique MATLAB codes and functions throughout, the book covers the theory of probability an
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.
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 [2]. 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 [9] and/or have a significant cost [20]. 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 [21]. 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. 2ff7e9595c
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