Image Processing Ebooks Catalog
Approaches described in this section are referred to variously as vessel tracking, vectorial tracking, or tracing 21, 26, 109 . These methods work by exploiting local image properties to trace the vasculature starting from an initial point either specified manually or detected automatically. They only process pixels close to the vasculature, avoiding the processing of every image pixel, and so are appropriately called exploratory algorithms. They have several properties that make them attractive for real-time, live, and highresolution processing because they can provide useful partial results 96 and are computationally efficient. (As an aside, numerous papers have been published on vectorization of binarized images within the document image processing literature for example, 57 ). These algorithms can be grouped in two categories referred to as semi-automated tracing where user input is needed and fully automated tracing where no user interaction is necessary.
One common type of semi-automated tracing algorithm is where the initial and end points of the vessel (sometimes also the direction and width) are entered manually. These are extensively used in quantitative coronary angiography analysis (QCA) 60, 102, 103, 109, 117, 127 . These algorithms are accurate but are unsuitable for real-time retinal image processing because they require manual input and suffer from high computational time, which is not a compelling constraint in QCA. Another QCA approach requiring initialization of points is the use of snakes 60, 104 .
Certainly there is scope for adding image-processing capabilities to group-identification systems in order to better focus their attention on aspects of the morphology already known to be important for group identification (see Steinhag et al., this volume, for an advanced example of the morphometric approach). Such system designs can and will be developed for specialized needs where optimal accuracy is required (e.g. medical and forensic applications) and where the characters being assessed lend themselves to study using simple image-segmentation algorithms (e.g. insect wing venation patterns). Nevertheless, we very much doubt such approaches will be able to serve as generalized platforms for the routine recognition of thousands of species, few of which will have any characteristics in common. For these situations, a more general-purpose solution is needed.
The modified version of Can's exploratory tracing algorithm described in Section 18.104.22.168 has been implemented for both a command-line and graphical user interfaces (GUIs). Known as RPI-Trace, it is written using a public-domain, open-source image-processing library known as VXL 118 that enables the source code to be compiled under multiple compilers in both Unix-based and Windows-based systems for a variety of commonly used graphical environments (GL, VTK, etc.) with no modifications.
The requirements for such quantitative imaging are not many, but they are stringent. All pixels in an image must be on scale, with an image defined as a matrix of numbers (pixels) that report intensity. So if you have something that is too bright and it simply saturates the brightness under your image settings, any such bright pixels are useless for quantification. Just as important, any background fluorescence (e.g. the image you collect when you focus out of your specimen) must also be something greater than zero intensity (black in the image). The second requirement is that if you are going to do ratio imaging, you have to collect both the numerator and the denominator image quickly so that you can be reasonably sure that tissue has not moved and that the cellular value you want to follow has not changed much in the time it took you to collect the two images. The final requirement is that if you are imaging a living moist tissue, you must use an objective lens that uses water as...
Image processing methods have been developed to retrospectively reduce the effect of intensity inhomogeneities in MR images 32, 33 . Some of the most successful versions of these correction methods segment the images into tissue classes in order to estimate a bias correction field that induces consistent statistical tissue characteristics throughout a volume 33 . A method that retrospectively corrects the bias fields in MR angiograms has recently been developed and is illustrated in Figure 11.9.
The maturation of the fields of medical image acquisition and analysis, as well as the apparent need for parallel computation, have lead to the development of increasingly complex image analysis methods. Researchers are learning how to devise methods that consider more detail within and across images and how to incorporate anatomic and expert knowledge 36,37 . These methods generally utilize a similar set of basic image processing modules (building blocks), and it is those modules that must be explicitly programmed to parallelize a method. These modules form a software library, but the required number of modules and the need to optimize the implementation of these modules make them difficult to program and debug. Additionally, if different individuals develop methods using different sets of software modules, then the resulting methods may not be able to interoperate or be easily exchanged or compared. These difficulties are alleviated by the establishment of a standard set of modules...
MRI of carotid plaque is thus likely to figure prominently in many studies of atherosclerosis to come. These studies, however, merely scratch the surface of MRI's potential. In the future, MRI of atherosclerosis is expected to be applied to vessels beyond the carotid arteries and to appear in regular clinical use. To reach this potential, a number of technological and image processing advances are necessary.
Further advantages of ratiometric measurement over the use of single-wavelength dyes is that the effects of cell thickness, dye content, or instrumental efficiency that interfere with the interpretation of measurements at single wavelengths are largely eliminated (1). This enables the signal obtained to be accurately calibrated and also enables comparisons to be made between samples and within different areas of the same sample and between different days of experimentation. Combining this powerful ratiometric technique with ultrasensitive low-light-level video cameras or photometers and digital image processing enables the study of both spatial and temporal distribution of ions within single cells (10).
Even richer detail can be obtained with the use of subtle staining procedures where the heavy metal is linked to protein molecules. Other types of shadowing, such as carbon shadowing, can also increase detail. Application of computer image enhancement can provide further striking increases in apparent resolution and resolve features that are obscured in conventional EM. Many examples of such detail can be seen in references cited in the introduction to this book.
IVUS catheters are working hard on obtaining high-resolution images. Still, due to the nature of the ultrasound image, any improvement in this direction represents a significant benefit in interpreting IVUS images. On the other hand, a real challenge and still a field not explored to its full extent by image processing people is to include knowledge on IVUS image formation in the process of image processing, which will definitely make image analysis techniques more robust and will ease the process of extracting important vessel data. Image non-uniform rotational distortion (NURD) and motion artifacts. This shortcoming is common in mechanical catheter systems and results from mechanical binding of the drive cable that rotates the transducer. A distinct motion artifact can result from an unstable catheter position. If the vessel moves before a complete circumferential image is created, a cyclic deformation is obtained. Image processing algorithms should take into account this...
Coomassie blue is less sensitive than silver stain (by a factor of 50), but simpler and more quantitative than the silver stain. A larger quantity of protein is needed (40 ng) in order to be detected by the Coomassie blue stain. Coomassie Blue R-250 (R for red hue) and G-250 (G for green hue) are wool dyes that are used for staining proteins in gel. Prepared Coomassie blue can be purchased, or it can be made in the laboratory. The latter must be filtered before using. Steps for staining with Coomassie blue are outlined in Table 3. Stained gels may be scanned and imaged using a scanner (we use an Epson Expression 800 scanner) with 300-dpi resolution and Photoshop (version 6.2.1, Adobe Systems Incorporated, San Jose, CA) software. The gel images are saved as .tiff files. Figure 1 shows representative Coomassie-blue stained gels from an experiment where the proteomic profile of a resistant C. albicans isolate was compared with that of its susceptible parent.
He is a lifetime member of various research engineering societies including Tau Beta Pi, and Eta Kappa Nu, Sigma Xi, New York Academy of Sciences, Engineering in Medicine and Biology Society (EMBS), SPIE, ACM, and also a Senior Member of IEEE. He is on the editorial board reviewer of several international j ournals, including Real Time Imaging, Pattern Analysis and Applications, Engineering in Medicine and Biology Society, Radiology, Journal of Computer Assisted Tomography, IEEE Transactions on Information Technology in Biomedicine, and IASTED. He has chaired image processing sessions at several international conferences and has given more than 40 international presentations. As an educator, researcher, technologist, and executive, Dr. Laxminarayan has been involved in biomedical engineering and information technology applications in medicine and healthcare for over 25 years and has published over 250 articles in international journals, books, and conferences. He has had the privilege...
In the case of segmenting white-blood vessels, imaging conditions can cause some background areas to be as bright as other vessel areas therefore, thresholding alone cannot be used. In the case of black-blood vessels, imaging conditions can cause some background areas to be as dark as black-blood vessels, making detection between them difficult. From the image processing perspective, BBA is more complicated than WBA. The main reasons for this problem include (a) muscles, bone, air, and vessels show as black in intensity (b) two neighboring bones can touch each other, thereby making the black region thicker (if they do not touch each other, then they can have gray-level tissue in between) (c) air or muscle pockets are irregular shapes which can very large, or very small. The very large ones can be longitudinal or circular in nature (see Figure 2.2, right) (d) the blood vessels are black and sometimes of the same size as that of air pockets or small...
The first part of this chapter introduced the importance of vascular image processing and prefiltering. The major part of this chapter focused on the scale-space ellipsoidal filtering. We presented several experiments and demonstrated that the SNR and CNR for the filtered MIP were far superior to the raw MIPs. Our results were compared with three-dimensional median filtering. We also presented the experiments on BBA and compared our algorithm with Alexander et al. and Sun et al.'s algorithms (recently published). A full discussion was presented on the implementation issues of scale-space ellipsoidal filtering.
Interested readers can explore the following articles to understand the importance of vascular image processing needs 9-17 . Having discussed some of the sources of motivations for performing black-blood vascular image processing (BB-VIP), we now present the major difficulties in performing filtering of the BBA volumes. The following include the main reasons that contribute to the complexity in vasculature filtering from BBA data sets (see 18-20 )
Characteristics of the imaging modality and motion blood flow artifacts. The resolution and clarity of the images play a critical role in vascular image processing. This data acquisition process plays an equally critical role in the accuracy and robustness of the vascular segmentation process. For example, artifacts in MR imaging could be due to several kinds (for details, see 21-23 ). In MRA, we might have ringing artifacts (see 24 ), or artifacts due to patient motion or blood flow. This contributes to the intensity nonhomogeneity in the vessel, which makes the segmentation process difficult to perform. 10. Scanning limitation. When tracking the vessels in vascular image processing, we find the imaging plane perpendicular to the vessel central axis. This plane is mathematically computed. Had the scanning system provided this imaging plane, the tracking of the vessels would have been very easy. One of the major limitations in vascular tracking is the weakness of the scanning system...
Having discussed the motivation for introducing the pixel classifier for classifying the black-blood pixels in comparision to the tissue pixels, we here propose a generic pipeline, called vessel detection and analysis system for black-blood angiography (VDAAS-BBA), for filtering the black-blood angiographic volumes. This system is shown in Figure 4.12. VDAAS-BBA consists of three major image processing components (1) the pixel classification block, (2) the black-blood to pseudo-white-blood converter, and (3) the scale-space filtering block. The last stage is the vessel display system based on the classical maximum intensity projection algorithm. Note that our attempt is to show that this system as a pipeline works well for black-blood angiographic volumes that have large black structured geometric shapes in addition to the black-blood vessels. We are not by any means claiming the
The first part of this chapter introduced the importance of vascular image processing and prefiltering. The major part of this chapter focused on scale-space ellipsoidal filtering. We presented several experiments and showed that the SNR and CNR for the filtered MIP were far superior to the raw MlPs. Our results were compared with median filtering. We also presented experiments on BBA and compared our algorithm with Alexander et al.'s (recently published). A full discussion was presented on the implementation issues of scale-space ellipsoidal filtering. 15. Berr, T. S., Hurt, N. S., Ayers, C. R., Snell, J. W., and Merickel, M. B., Assessment of the reliability of the determination of carotid artery lumen sizes by quantitative image processing of MR angiograms and imaging, J. Magn. Reson. Imag., Vol. 13, pp. 827-835, 1995. 16. Han, C., Hatsukami, T. S., Hwang, J.-N., and Yuan, C., A fast minimal path active contour model, IEEE Trans. Image Processing, Vol. 10, No. 6, pp. 865-873, June...
All specimens were imaged using a Leica MZ 12.5 microscope fitted with a Q-imaging MicroPub-lisher CCD camera. Illumination was provided by an EIS fiber optic light source with a dual chrome gooseneck. The camera was connected to either a Dell Dimension 8200 Series or an Apple Titanium Powerbook G4 laptop. Images were converted to greyscale, cropped square, enhanced and resized as necessary in Adobe Photoshop. Ideally, one would use many unique examples from every species to train the ANN, thereby encapsulating both types of likely variation. As previously stated, we did not have an adequate number of replicate specimens for most species. Hoping to compensate partially for this lack of unique samples, we collected either 4 or 12 images of every specimen, depending on how many specimens were available (greater than or less than 15, respectively). An attempt was made to introduce variation in the process by altering the lighting, repositioning the specimen and or changing the rotation...
Accessibility refers to both the ease with which non-specialists can navigate through the identification process as well as the ability of users to gain access to the ID system. SPIDA-web ranks high in both categories. Because the input to SPIDA-web is the whole image of a structure (in the case of the prototype, a picture of the external genitalia of spiders), there is no need for users to measure or dissect or even know the name of what they are taking a picture of. It is as simple as finding the structure, centering it and snapping an image. Instructions on how to find the structure are included in the introductory pages of the website. Aside from rudimentary image processing, such as converting the image to greyscale and cropping it square, users can submit an image without having any technical software or technical knowledge.
Image Processing are recorded as two gray-scale images, either simultaneously or one by one. Usually the scanner output is two 16-bit TIFF (tagged image file format) images, which provide 216 (65,536) different gray scales. These images are then imported into specialized image processing software, frequently sold as a package together with the laser scanner. There, DNA spots are defined by superimposing a grid that reflects the architecture of the array in terms of rows and columns and links each spot with information on clone sequence identity and chromosomal location.
Vazquez, L., Sapiro, G., and Randall, G., Segmenting neurons in electronic microscopy via geometric tracing, Proc. 1998 Int. Conf. Image Processing, ICIP98 IEEE Comput. Soc, Los Alamitos, CA, Vol. 3, pp. 814-818,1998. 41. Wang, Y. P., Lee, S. L., and Toraichi, K., Multiscale curvature-based shape representation using B-spline wavelets, IEEE Trans. Image Processing, Vol. 8, No. 11, pp. 1586-1592, Nov. 1999. 44. Fuchs, H., Kedem, Z. M., and Uselton, S. P., Optimal surface reconstruction from planar contours, Graphics and Image Processing, Vol. 20, No. 10, pp. 693-702,1977.
At this stage, the cardiologist locates the catheter head position before and after the pull-back in both angiogram views, and two, three-dimensional points are reconstructed. Note that these points represent the location in space of the center of the first and last IVUS images. Figure 10.38 shows two views of the catheter after finishing the pull-back. Once located, the position of the catheter head in one x-ray projection, the epipolar line, suggests the position of the catheter head in the other view. A local histogram-based image enhancement can be applied to the angiograms (see Figure 10.38) to emphasize the appearance of catheter position.
Either because of tumor neovascularity or because of increased transcapillary leakage of red blood cells within areas of malignancy. To take advantage of the enhanced tumor detection that comes with use of the near infrared spectrum, some transillumination techniques record images with special infrared sensitive photographic film (Isard, 1981 Ohlsson et al., 1980). A more technically advanced application of breast transillumination involves the recording of images by a television camera sensitive to near infrared radiation coupled to a standard television monitor (Bartrum and Crow, 1984 Carlsen, 1982 Watmough, 1982). This provides both real-time viewing of near infrared-rich images and hard-copy recording of images with videotape or with a multiformat film camera. A sophisticated modification of television-based transillumination involves post acquisition image processing with false color rendition of transmitted near infrared wavelength light to maximize visibility of those findings...
510 Angiography and Plaque Imaging Advanced Segmentation Techniques 12.4.2 Advances in Image Processing The image processing tools described in Chapters 8 and 9 provide the basic needs for quantitatively analyzing MRI of atherosclerosis. As such, they are entirely suitable for scientific study of the disease. For clinical applications, however, increased automation, reliability, andrepeatability are needed. Thus, MRI of atherosclerotic plaque also poses a challenge to the image processing community. Ultimately, tools are needed thatwill allow the clinician to rapidly extract the most significant features from plaque images. This will permit the techniques described here to make the jump to regular practice.
The following case depicts a typical medical scan process from scan phase to 3D data reconstruction phase. A skull of a patient was chosen. Part of the patient's skull was removed by surgery. A biocompatible tissue part needed to be produced to fill in the hole and help the patient to heal the region again. The image processing segmentation was performed using tools provided by the Materialise software package MIMCS and CTM module. Mimics is a fully integrated, user-friendly 3D image processing and editing software based on CT or MRI data. The software imports scanner data in a wide variety of formats and offers extended visualization and segmentation functions. Mimics translates CT or MRI data into full 3D CAD, finite element meshes or rapid prototyping data within minutes. By the separation into different modules it can be tuned for every situation.
Understanding Adobe Photoshop Features You Will Use
Adobe Photoshop can be a complex tool only because you can do so much with it, however for in this video series, we're going to keep it as simple as possible. In fact, in this video you'll see an overview of the few tools and Adobe Photoshop features we will use. When you see this video, you'll see how you can do so much with so few features, but you'll learn how to use them in depth in the future videos.