Image Processing Ebook

Learn Photo Editing

This online course gives professional advice and instructions for how to photoshop pictures for any purpose that you could need them for. If you need to retouch your portraits, this gives you the tools to edit the image so that your model is sure to be happy with the results. If you need to create cartoon characters, you can learn how to do that in a very short amount of time. You can even learn the more advanced skills, like how to make facial features stand out in the picture without having to retouch the photo. You can learn how to take your normal photos and turn them into glossy, high resolution advertisements. Whatever skills you want to learn, and whatever application your photos will be needed in, this course can give you the tools that you need in order to create the most beautiful photoshoot that you've ever done. Read more...

Learn Photo Editing Summary


4.8 stars out of 24 votes

Contents: Premium Membership
Author: Patrick
Official Website:
Price: $27.00

Access Now

My Learn Photo Editing Review

Highly Recommended

It is pricier than all the other books out there, but it is produced by a true expert and includes a bundle of useful tools.

In addition to being effective and its great ease of use, this eBook makes worth every penny of its price.

Image Processing in Tumor Imaging

Edical image processing has become a major force in the imaging of cancer. Virtually all cancer imaging requires some level of image postprocessing. Among the most critical postprocessing functions are image segmentation in which tumors are localized either manually or semiautomatically image measurement in which physical and physiologic properties of tumors are characterized and mapped onto anatomic images image visualization in which tumors are displayed in ways that are intuitively easy to grasp and image registration in which two or more images are fused so that different tumor properties can be combined into one view. Image fusion and computer-aided diagnosis detection combine many of these methods to produce synthetic images that display multiple parameters and highlight abnormalities that may be otherwise difficult to detect. Image processing methods will undoubtedly continue to contribute to progress in cancer detection and management. The development of medical imaging has...

Currently Available Commercial Image Processing Methods

Currently available image-processing methods produce six different effects on image appearance. They define the ''window of clinically useful exposure data,'' they affect the degree of image blackness and image contrast, they equalize image blackness in different parts of the image, and they provide edge enhancement or provide blurring of noise in parts of the image. The steps The raw data that has been received by the imaging device contains both useful and nonuseful exposure data. Nonuseful data, for example, include the exposure that occurs from X-ray photons that passed through the area outside of the patient and areas outside of the collimated field. Because the imaging device received unimportant exposure data, the digital data will contain unimportant exposure data. To cope with this, the first step in image processing is to define what data is likely to represent the data encoded as the X-ray photons pass through the body. The second method uses information from the exposure...

Image Processing Task

Image processing tasks are needed and used in many research areas and also in many industrial applications. Examples can be found in quality control applications in industry 4 as well as pattern matching applications, for example, for robot control 4 or high energy physics 5, 6 . Image processing is also an important issue especially in the research field of bioinformatics where many biological experiments like Microarray scans are analyzed using specific image processing applications. Image processing is the umbrella term for all kinds of algorithms that process image data. Subgroups of image processing tasks are image generation, image compression, image filtering, pattern matching, pattern extraction, motion detection, transformation, and many others. Image filtering is chosen from the incomplete list of image processing tasks to describe the mapping of an algorithm and programming details that have to be taken into account when a computing task is mapped onto a parallel processing...

Image Processing

Image processing is the computer manipulation of digital image data done with the goal of enhancing image quality. Sometimes the image processing is done to enhance the attractiveness of the image, sometimes it is done to enhance disease conspicuity, and sometimes both attractiveness and conspicu-ity result at the same time. Image processing is discussed in three different parts standard commercial methods, special commercial methods, and experimental methods that may potentially provide additional benefits.

Quantitative analysis

Such techniques are currently an active focus for research and different research groups have adopted somewhat different approaches the approach adopted by our own group is summarized in Plate. .9. In this scheme, the first step in image processing is removal of extracerebral tissues like skull and scalp leaving an image of the brain alone. The brain image is then segmented into the three main tissue classes. There are a variety of techniques for segmentation or brain tissue classification, (8) but generally the quality of segmentation is improved if a dual-echo (fast spin echo) image is available. One method, based on discriminant analysis, requires the operator to select a subset of voxels in the image that are representative of each tissue class these training data are then used to make a probabilistic assignment of each voxel to one of the three possible classes. Other methods have been developed to avoid even this minimal requirement for human operator intervention, thereby...

Advances in MRI of the Brain

Practically every innovation in magnetic resonance imaging (MRI) has been applied first to the brain. The brain's well-defined anatomy, MR characteristics, and relative absence of motion make it ideal for MRI. Advances in hardware, such as higher field strengths, high performance gradients, and advanced coil designs have led to brain scans with more contrast obtained at higher speeds. Pulse sequence advances such as echo planar imaging, fluid attenuated inversion recovery, diffusion weighted, perfusion imaging, and spectroscopic images have expanded the diagnostic range of MRI. Image processing and image management have also had a profound impact on MRI of the brain. This chapter reviews these developments and provides illustrative examples.

Perspectives For The Future

Advances in bioengineering will make major contributions not only to our understanding of cell and tissue function but also to the quality of human health. In a glass slide consisting of microfabricated wells and channels, for example, reagents can be introduced and exposed to selected parts of individual cells the responses of the cells can then be detected by light microscopy and analyzed by powerful image-processing software. These types of studies will lead to discovery of new drugs, detection of subtle phenotypes of mutant cells (e.g., tumor cells), and development of comprehensive models of cellular processes. Bioengineers also are fabricating artificial tissues based on a synthetic three-dimensional architecture incorporating layers of different cells. Eventually such artificial tissues will provide replacements for defective tissues in sick, injured, or aging individuals.

Signal Quantification

Innovations in image processing of radioactive samples involve the use of direct detection of radioactive emissions or the detection of radioactive energy stored in phosphor screens previously exposed to radioactive samples. These technologies are capable of quantifying radioactive samples with dynamic ranges of 3-5 orders of magnitude without the cumbersome constraints of laser densitometry. The price of these instruments ( 50,000 to 100,000) puts this technology out of the reach of many investigators.

Replicate Experiments Reproducibility and Noise

A ubiquitous and underappreciated problem in microarray analysis is the incidence of microarrays reporting nonequivalent levels of an mRNA or the expression of a gene for a system under replicate experimental conditions. This phenomenon of microarray data irreproducibility is widely attributed to noise in the bioinformatics literature. For example, a common sample probe or target pool that is split and hybridized simultaneously to two separate chips of the same make and following the same scanning protocol will almost surely result in two, not exactly similar-looking, graphic representations of RNA abundance. These images will subsequently translate into two quantitatively different expression data files (e.g., .eel or .chp files on the Affymetrix platform) after they are fed into image-processing and statistical software. This situation is akin to saying that two Northern blot assays for a particular RNA species in a split total RNA sample, having been processed under an identical...

Measuring and reporting expression

Regardless, one will typically find a number ofpLxels within some of the spots that are saturated in one or both channels, and these must be considered and dealt with effectively. One approach is to use the median spot intensity which, as discussed previously, has the advantage of being relatively insensitive to a small number of saturated pixels. However, the median can also be skewed if there are too many saturated pixels in a particular spot. Ob iously, pixel saturation will also have a deleterious effect on the integrated intensity. If pixels in a single channel are saturated, this can result in an underestimate of the expression in the sample to which it corresponds. If pixels are saturated in the images representing both labelled extracts, then the result is an unpredictable distortion of the relative expression examples are shown in Figure 3.2. Consequently, it is generally good practice to eliminate saturated pixels from calculations of spot intensities, and most microarray...

Image Display And Visualization

Conventional films and light boxes can only convey 2D information. With the arrival of 3D images in biomedical field, it is desirable to visualize 3D volume information. Image processing and computer graphics techniques allow intuitive ways to display and visualize medical images. Common medical image display and visualization techniques include multi-planar reformatting (MPR), maximum intensity projection

Computeraided Diagnosisdetection

CAD involves all other aspects of image processing techniques. For instance, image segmentation and registration are necessary for feature extraction, and image visualization and measurement are essential for clinical presentation. activity from PET and MRI images. Analysis of medical images requires sophisticated image processing techniques. In this chapter, we described several medical image processing fields and emphasized their relevance to tumor imaging researches. The purpose of image segmentation is to localize the tumor regions image measurement is to quantify the tumor properties image visualization is to provide intuitive ways to present the tumor image registration is to fuse two images so that different tumor properties can be combined in one view finally, CAD could be used in the clinical diagnosis detection of tumors. There are quite a few medical image processing and analysis software packages available, both for clinical practices and research activities. Major medical...

Visualizing Cell Behavior in the Neural Plate

High-resolution video microscopy of neural plate explants similar to the type described above can be used to characterize the cell behaviors driving neural convergent extension. Protrusive activity of cells in a tissue is best visualized if a scattered population of the cells is labeled with a vital dye. To achieve such a labeling in the neural plate, we inject 20-30 nL of rhodamine dextran amine (RDA, Molecular Probes Inc., Eugene, OR) into the A or B dorsal tiers of 32 cell-stage embryos. Dorsal is identified by the tipping and marking method described above. By the midgastrula stage (stage 11.5), cell rearrangement associated with convergence and extension has occurred among the derivatives of the injected blastomere, so that labeled cells are scattered along the length of the neural plate. At stage 12.5, the outer epithelial layer of the neural plate is removed and discarded, exposing the labeled deep cells of the neural plate. The remaining layer of neural plate deep cells, with...

Clinical PET in Oncology

Both authors are clinicians with radiological and nuclear medicine training and they use FDG-PET in their practices. Medical image processing has become a major force in the imaging of cancer. They discuss FDG-PET in the clinical settings of the common problems faced by radiologists on a daily basis which include tissue characterization, staging, response assessment and evaluation of residual disease. The images in this section are up to date with full use of CT-PET technology.

Image acquisition processing and machine vision moving from images to data files

We considered the type of objects we could create during the presentation stage and how to optimally image them as a single problem to be solved. This tact enabled us to critically engineer features for data acquisition in robust and operationally simple ways. As such, we developed a high-speed imaging instrument (named, genome Zephyr''), or a single molecule scanner, built around a standard fluorescence microscope featuring full computer control over focusing, sample positioning and digital camera functionalities, thus enabling user-free operation launched from a friendly interface. Throughput is greatly enhanced by a distributed laser illumination system offering stable, bright, monochromatic illumination to all of the Zephyr scanners in our laboratory. Essentially, the Zephyr automatically acquires strings of contiguous, overlapping micrographs, by tracking stripes of deposited DNA molecules laid down by the microfluidic system. Automatic image processing takes these images,...

The history of optical mapping

Rather prescient spotting engine complete with an assortment of spotting tips, and this system enabled us to spot multiple samples, such as PCR amplicons (Skiadas et al., 1999), and phage or cosmid clones onto a single optical mapping surface or chip for parallel enzymatic processing. These advancements pointed the way to full automation of restriction mapping through our associated development and integration of several key system components, present in today's optical mapping system automated fluorescence microscopy - image acquisition, image processing vision to deal with images - machine vision, map construction software - algorithms for analysis of single molecule data sets, use of multiple computers for processing data - cluster computing, and a score of user interfaces that gave researcher the ability to analyze and visualize their data. It was, arguably, the first single molecule system for genome analysis, and it was fully automated.

Locating weed problems

Remote sensing offers an automated way to develop weed maps that can be used to guide spot-spraying or to inform field scouts where to seek out economically threatening weed populations. Because the timeliness principle dictates that weeds must be recognised while still small, the first challenge is to distinguish them from the soil in images taken from aircraft (or eventually even satellites). In the face of rapidly improving aerial photography and image processing, research is actively under way on using multispectral imaging and various analysis algorithms, (1) to distinguish weeds from soil, (2) to identify weed types (e.g. grass versus broadleaf) and (3) to identify individual weed species (Lamb et al. 1999 Lippert & Wolak 1999 Medlin et al. 2000 Varner et al. 2000). Attaining high-image resolution is easier (albeit more costly) by proximate sensing. Proximate image acquisition is the first step toward real-time image processing. Research in this field has focused on either...

Constructing single molecule restriction maps from fluorescence micrographs

Image processing. (A) Raw micrographs from CCD camera. (B) A raw image (left) is flattened by FlatOverMerge (right). (C) Images are overlapped into a composite microchannel view by FlatOverMerge. (D) Genomic DNA molecules are identified by PathFinder, and single molecule ordered restriction maps are generated. Fig. 5. Image processing. (A) Raw micrographs from CCD camera. (B) A raw image (left) is flattened by FlatOverMerge (right). (C) Images are overlapped into a composite microchannel view by FlatOverMerge. (D) Genomic DNA molecules are identified by PathFinder, and single molecule ordered restriction maps are generated.

Grid Activities In Europe

Other European grid projects that focus on medical databases and images are the Medical Grid (MEDGRID) to tackle the processing of huge databases of medical images in hospitals 39, 40 , the GridSystems to create an individualized healthcare environment that enables analyzing and processing 3D medical images to aid clinical analysis 41, 42 , and the Grid-Enabled Medical Simulation Services (GEMSS) testbed for advanced image-processing services 43 . The GEMSS testbed also aids medical services applications in the area of neurosurgery, prosthesis design, advance image reconstruction, and others 44 .

Expression Data Analysis

The use of DNA microarrays generates a large number of individual data points, which must then be analyzed and archived. Optimal analysis requires expertise in statistics and bioinformatics, and the time and effort required to progress from the initial data acquisition to the extraction of relevant biological information is substantial. Some of the key aspects involved include image processing, data normalization, differential expression analysis, and database management. Each of the areas is complex, and a comprehensive discussion is beyond the scope of this chapter but can be explored further in several references.11,12

Radiographic techniques Chest radiography

Angiography involves the selective or subselective intravascular injection of a contrast medium to generate single images or image sequences of intraluminal dimensions. With the use of thin catheters and non-ionic contrast media, arterial angiography, including coronary angiography, is a safe procedure with very few severe complications. Quantitative coronary angiography uses digital image processing to measure vessel diameter at stenotic and unobstructed coronary segments and to quantify the severity of coronary stenoses

Requirements for the Lungs

And image processing can form an image in which the bone is largely subtracted away. Special equipment and image processing are necessary for this technique. Third, experimentally, several groups are developing temporal subtraction chest radiography. If one has two chest radiographs that are similar in their positioning, it is possible to process them so that they are nearly superimposed. Once superimposed, the computer can subtract one image from the other. The bony structures are unlikely to change, while the lung can develop pneumonias, cancers, and so on. Thus, the subtraction image provides a potentially better view for the detection of change in the apical lung. This technique is demonstrated below. Portions of the lungs project behind the heart and behind the diaphragm. These areas are difficult to visualize on screen-film chest radiographs because of the larger amount of water density absorbers there than over the central portions of the lungs and because there is less lung...

Special Requirements for Specific Disease Processes

On both screen-film and digital chest radiographs, pneumothoraces may be difficult to detect. Detection of a pneumothorax depends on the detection of two findings the first is the radiolucency of the lung outside of the edge of the lung the second is the detection of the lung edge. Because digital image processing can change the density of regions of the image, density equalization programs may make the radiolucency harder to detect. The pleural edge can be very thin and can be superimposed on the ribs. Detection of the edge of the lung depends somewhat on the thickness of that edge, which varies among individuals. Using a slight degree of image processing to enhance edges increases the conspicuity of the lung edge and makes it easier to see the pneumothorax (Fig. 4). When the edge of the lung is superimposed on a rib, a low-kilovolt-peak chest X-ray technique may make it harder to see the lung edge because the relative radiodensity of the lung is increased, therefore a...

Image Segmentation for Anatomic Definition of Processing Parameters

Figure 12 Segmentation for anatomy-based image processing. In this study, a normal screen-film chest radiograph has been digitized (a). It has then been processed so that the lungs are segmented from the image for organ-specific image processing Figure 12 Segmentation for anatomy-based image processing. In this study, a normal screen-film chest radiograph has been digitized (a). It has then been processed so that the lungs are segmented from the image for organ-specific image processing ence between them. In the chest, work is underway to segment the mediastinum, rib boundary of the lungs, and the lungs themselves 21,22,23 . Once these areas are segmented, different image-processing techniques could be applied to these different regions (Fig. 12). The segmented areas could be adjusted to have different black white scales, different degrees of edge enhancement, and or different contrast scales.

Parallel Processing Models

The analysis of the computing task shows whether parallel processing of the complete task is possible or not. This analysis step uses a high-level abstraction of the computing task to see if blocks of data sets can be processed independently on several single processors. A well suited example for such a parallelized computing task is the processing of a large set of independent images. Let us assume a cluster (MIMD) with single processing nodes is available, the complete image processing task can be spread onto the single nodes where each node processes a single image. This distribution of the complete computing task will lead to a reduced overall computing time because each node is running in parallel executing different data. If only one image has to be processed, this parallelized solution is not appropriate because only one single computing node would be needed. Therefore, a single processor solution (SISD) would be selected for this task. The second criterion for the selection of...

Fpga Hardware Accelerators

As we have seen in the previous image processing task section there are algorithms which are not suited for an acceleration on a cluster or other parallel computing architectures, because of their fine-grain granularity. The innermost loop of the image processing task has shown that up to nine additions and one division have to be processed in the innermost loop. If additional coefficients are used for the filter operation, then another nine multiplications have to be computed. If the innermost loop of the image filter is executed an optimal solution for the computation would be a parallel architecture where each operation is performed by an individual processor. All these processors must be connected according to the image filtering algorithm to perform the complete innermost loop operation. the operations without any instruction decoding steps. The two most important disadvantages of the ASIC technology are the long design process time and the total costs of the fabrication setup....

Disease Specific Algorithms

A disease-specific algorithm is a preselected method for image processing that is optimized to identify a specific disease. An example would be that if one knows that a patient is at risk for a pneumothorax, one could have the computer enhance the image so that any pneumothorax would become more conspicuous. While disease-specific image-processing settings do not yet exist, situation-specific image-processing algorithms are commonly used. The clearest example is the use of histogram equalization to enhance the visibility of tubes and lines within the mediastinum and upper abdomen. The settings used enhance this visibility, but with some probable loss of information for subtle disease in the lungs. In the past, optimization methods have emphasized the desire to find image-processing settings that maximize the value of the chest radiograph for all diseases based on both how common the diseases are and their importance to the patient. In the future, it will be possible to have a system...

Printed on Film versus Soft Copy Display

Digital chest radiographs can be viewed either by printing them on film or by viewing them on a computer screen. With current technology, there is no firm evidence that one method of viewing provides greater accuracy than the other. Display on film is a more mature technology and only limited technical improvements that would affect diagnostic quality are foreseen. Computer screen (or monitor) display is a moderately mature technology, but one where technical innovation is more likely. If the changes in computer screen display are favorable to the display of chest radiographs, then this method of display may eventually surpass that of the display on film. Some of the future advances that could make soft-copy display the diagnostically superior method are the ability to rapidly switch between image-processing settings, the incorporation of computer-aided detection and diagnosis, and the ability to label an image that will be incorporated into the report to the patient's treating...

Contrast Enhancement Phase ContrastDIC Nomarski Dark Field

For embryological specimens, some kind of contrast enhancement is usually extremely useful. For example, with fluorescent preparations, standard counterstains may either quench fluorescence or be autofluorescent themselves. There are occasions, however, when a particular contrast enhancement may reduce the information within an image for example, Nomarski optics may make small labeled objects (such as cell nuclei) less easy to see. It is always worth viewing a specimen both with and without contrast enhancement before photographing. The use of three common image enhancement systems is described below.

Digital Image Manipulation

Having produced the optimal photomicrograph, computer software packages allow a range of further image manipulation. The superimposition of, for example, a fluorescence image and a DIC illuminated view of the same specimen, which might normally be achieved by a photographic double exposure, can be routinely composed within software programs, such as Adobe Photoshop (Adobe Systems Inc., Mountain View, CA). Further manipulation can involve spatial filtering to reduce noise or sharpen contrast, and perhaps most frequently, the pseudocoloring of black-and-white digital images. These techniques also raise the possibility of fairly sophisticated touching up of data.

Future Directions

The image management infrastructure should be able to accommodate data sets that differ as results of difference in scanner types, acquisition channels, image formats, tissue preparations, and consequently the information content. The system needs to be able to support query, retrieval, browse and image processing of these diverse set of images in a generic way.

Tendons adnexa and ligaments

Quadriceps Tendon Anchor Failure

Nowadays US represents the gold standard technique for the assessment of tendons 24,25 . With the advent, for clinical purposes, of high resolution transducers and specific image processing software, it became possible to make detailed analysis of the shape and structure of tendons. In addition, US is the only technique that allows the radiologist to perform a dynamic study of tendons, which is extremely important for the diagnosis of tendon pathology. In longitudinal ultrasound views (long axis), the tendons appear as

Dna Microarray Platforms

Microsatellite Instability Probes

In addition to the grid of green, red, yellow, and blank spots, most cDNA image files demonstrate a variety of irregular streaks and spots. Figure. 2 was selected explicitly to illustrate an extreme range of such defects, noting that a more typical field of view would have far fewer artifacts or none at all. Here, we see irregularities resulting from background noise from sources such as dust, local drying effects, and mechanical spotting difficulties. Much of this noise can be attenuated through software and analytical techniques involved in image processing, but arrays should generally be inspected for severe artifacts. Commercial arrays are often shipped with quality control measures in place to minimize these concerns. Unlike the cDNA array, the oligonucleotide array does not require the intensity of each spot be interpreted in relation to a reference. Intensity can be interpreted directly as a linear unit-less measure called expression. However, because a given gene target is...

Description Of The Model

Zebrafish Well Plate Swim

Visual observation of freely behaving animals is a reasonable and commonly used method to document seizure behaviors. However, automated observation systems can provide several distinct advantages. In particular, behaviors can be recorded more reliably because computer algorithms do not suffer from observer fatigue or interobserver variability. Further, computer automated detection is open to a wide array of measurements, for example, distance traveled, duration of movement, and velocity. For automated detection of zebrafish seizure behavior, we set up a computer-based locomotion tracking system. Using a stereomicroscope and high-speed charge-coupled device (CCD) camera acquiring images at 30 Hz, we can record the behavior of single zebrafish larvae during exposure to PTZ. Subsequently video output is digitized by a frame grabber and passed directly to a computer running EthoVision software (Noldus Information Technology, Wageningen, The Netherlands). Image-processing algorithms are...

Technical Setup of the PALM Micro Beam System

Modern detection methods are often based on fluorescence techniques. Thus the PALM MicroBeam can optionally be equipped with features for fluorescence microscopy, allowing simultaneous fluorescence observation and LMPC. The high degree of automation realized in the latest generation of PALM systems (MicroBeam-HT) can be optionally augmented by image-analyzing software modules allowing automated fast scanning functions for specimen identification and image processing. Both fluorescence and bright field microscopy can be used for automated detection of cells or regions of interest. Coupled with any one of these software modules, the MicroBeam system is able to scan, detect, isolate, and finally capture the specimen of interest, e.g., immunostained areas (Fig. 6A See Color Plate 6 following p.18.), metaphases, or fluorescent-labeled rare cells (Fig. 6B), in a fully automated manner. Recognized areas can subsequently be extracted automatically by the appropriate laser function. These...

Image Interpretation And 3d Volumerendering

Surface-shaded display renderings have been used for NSS surgical planning but are limited by the need for intensive image editing, which is too time consuming for most radiologists. Volume-rendering typically requires little image editing and preserves the entire dataset (Figs. 6 and 7) (9,10,38,39).

Interpretation Of Oct Images

The evaluation of OCT tomograms depends on the ability of the observer to identify both differences in the relative reflectivity of different tissue layers and morphological changes in tissue structures 8 . In some cases, because of the high axial resolution of the OCT images, small changes in morphology may be difficult to assess by direct observation of the images. In these cases, automated computer image processing tools may be used to extract precise quantitative measurements from the images.


OCT is a new method for high-resolution, cross-sectional visualization of tissue 1,2 , The physical basis of imaging depends on the contrast in optical reflectivity between different tissue microstructures. When combined with the conventional clinical techniques of direct and indirect ophthalmoscopy, fluorescein angiography, and visual field testing, OCT is a powerful diagnostic tool for a variety of ocular diseases. In many cases, a definitive diagnosis may be established directly from the OCT images. Because OCT is an inherently digital technique, quantitative measurements may be easily extracted from the tomograms, and automated computer image processing techniques may be applied to the problem of image interpretation. The availability of quantitative information makes OCT useful for longitudinally tracking small changes in tissue structure and the development or resolution of disease processes.

Future Developments

The major requirements are in the fields of instrumentation and guidance. The present armamentarium could usefully be expanded to enable better methods of lifting and incising tissues. Methods of image enhancement should be brought to bear so as to increase the information available from images obtained, especially with fiberoptic neuroendoscopes.


Yang et al.14 reported our initial experience with anal ultrasonography for anal fistulas. Sonographic data were compared with surgical findings in 11 patients with fistulas and 6 patients with a suspicion of abscess. In 82 of the patients, sonographic findings correlated with the operative findings. In one patient, a horseshoe fistula was incorrectly assessed as a lateral transsphincteric fistula, and in another patient with Crohn's disease, the primary tract was not visualized. We have since used hydrogen peroxide injection of the tract as an image-enhancement technique during anal ultrasonography for complex and recurrent fistulas.15 Fistula tracts typically have a hypoe-choic appearance. With the injection of hydrogen peroxide, the tract becomes hyperechoic as a result of the bubble-induced increased echogenicity. We believe this technique has helped us to identify tracts more easily. Poen et al.16 also have found hydrogen peroxide injection to be useful in delineating the...

Image Management

The increasingly sophisticated imaging capabilities of MRI necessitate the development of equally advanced image processing tools. Many of the techniques described above generate huge amounts of data. To be useful, these images must be synthesized into formats that are accessible to clinicians. Image management in MRI can be viewed as a series of events. After image acquisition, the images are sent to electronic workstations. Here they are stored in digital imaging and communications in medicine (DICOM) format, and then are processed using one or more different software tools. Since this is an increasingly time-consuming process, often a radiology department will form an image processing team comprising one or more individuals. Once the images are processed, they are sent back to the picture archiving and communications system (PACS) for storage and distribution. Current studies may generate between 1000 and 5000 images, and thus tax the storage capabilities of many conventional PACS...


MRI of the brain is a vital part of modern oncology. In addition to very accurate Tl-weighted, T2-weighted, and gadolinium-enhanced Tl-weighted MRI, a number of other techniques including FLAIR, BOLD, DCE MRI, T2* perfusion imaging, MRSI, and DWI add diagnostic value. The increasing complexity of interpreting and teaching neuroradiology has led to increased specialization and the development of customized software tools. Hardware and image processing advances have combined to improve the diagnostic capabilities of brain MRI. Future developments include the discovery of molecular imaging probes with specificity for brain tumors, the ability to image tumors with intact blood brain barriers, and the development of early biomarkers of therapeutic success or failure that will aid in the treatment monitoring of patients undergoing therapy.

Image analysis

The first step in the analysis of microarray data is image processing. Most commercially available microarray scanner manufacturers provide software that handles this aspect of the analysis, and there are a number of additional image processing packages available. Nevertheless, it is important to understand how data are extracted from the images as they represent the primary data collected from each experiment and everything else is derived from those images and their initial analysis. Image processing involves three stages. In the first stage, the spots representing the arrayed features must be identified and distinguished from spurious signals that can arise as a result of precipitated dye or odier hybridisation artefacts, contaminants such as dust on the surface of the slide, and other sources of nonspecific background. The problem offinding a distributed collection offeatures is a difficult one, but for microarrays this is greatly simplified as the systems used to create the...

Image Segmentation

Image segmentation is a technique to classify image pixels into anatomic regions, such as bones, muscles, and blood vessels, or pathological regions, such as tumors, tissue deformities, or multiple sclerosis lesions (1). In some applications, the goal of image segmentation is to extract the boundaries of the structures of interest. Image segmentation usually serves as the preprocessing step for further image processing tasks such as feature extraction, image registration, and quantitative measurement.

Background on Fold

For microarrays, fold analysis starts off from the level of a text file (e.g., a .chp file in the case of the Affymetrix technology) which contains information such as a probe identifier and corresponding indicators of the level of sample probe or transcript that was detected. Theoretically, this text file is a numerical representation of the scanned chip image, specifically of its detected levels of different RNAs, and is generated by the chip manufacturer's software program which implements (often) proprietary statistical image-processing algorithms that are typically opaque to the user. There is always some loss of information in going from the true image to its machine image file, and to the numerical representation of the image file. The relevance of this loss is, of course, context-specific. Normally, in chip data analysis, the bioinformatician will perform statistical tests, classification, or clustering algorithms based on these preprocessed numerical representations of RNA...

Digital Methods

The advantages of digital methods is that they allow the separation of four different parts that are included in an analog system acquisition of image data representing different amounts of X-ray exposure, image processing (or enhancement), image storage, and image display. Image processing and enhancement by computer provides the main disease detection and diagnostic advantage of digital methods. Image storage and retrieval provides the main economic argument for its benefit. the original image was of high quality, since there should be no very dark regions of the image. If the lungs are too dark, however, information may be lost. More important is the effect in the more transparent parts of the chest image (for example, behind the heart). In these regions, the original film image is often of low contrast and this low contrast is maintained in the digitized image. Image processing may not be sufficient to restore the contrast. Techniques to Enhance the Quality of Digitized Film Chest...


Technologically mature and digital chest radiography is still in a process of moderate innovation, it is likely that the diagnostic quality of digital chest radiography will, in the future, be superior to that of conventional chest radiography, replacing it for many uses. Digital chest radiographs can be produced by three competing methods film digitization, systems that store the energy of the encoded X-ray photons for later extraction, and near-real-time systems for extracting the encoded X-ray information. For each of these methods, there are trade-offs in labor versus machine costs. To date, there is no evidence that any one method is diagnostically superior to the other, although digitized film requires that the original film be of high quality to achieve a high-quality digitized image. The two other types of digital acquisition are more robust to exposure differences. Once in digital form, image processing provides important advantages in correcting and improving disease...

Color Photography

Fig. 4. (continued) images of the same specimen illuminated for different fluorescent dyes are color-coded and merged to produce an image equivalent to a photographic double exposure. This image is then merged with a DIC picture of the same specimen. (Bottom) Confocal microscope optical sections are color-coded and merged to show the differences in cell dispersal at different depths in the tissue. Pseudocolor pictures were prepared within Adobe Photoshop from 8-bit 768 x 512 pixel images produced on a Bio-Rad MRC-600 confocal system mounted on a Nikon Diaphot inverted compound microscope. (See color plate 4 appearing after p. 368.)

Transverse Scan

Visual Acuity

Computer Image Processing and the Correction of Eye Motion Since the resolution of OCT is extremely high, it is essential to compensate for motion of the eye during image acquisition, since eye motion can cause image blurring. Movements of the eye can be caused by a variety of processes including fluctuations in intraocular pressure produced by pulse, microsaccades and tremor, and changes in the patient's fixation point, Since OCT measures the absolute distance or range of the tissue specimen, it is essential to correct for motion of the eye. This problem is addressed by powerful yet simple computer image processing techniques which can be used to dramatically enhance imaging performance by virtually eliminating image blurring from involuntary patient eye motion. Figure 1 -9 shows an optical coherence tomogram of the fovea showing the raw image data without image processing and the image achieved after processing to correct for eye motion. The dominant motion which blurs the image...

Photoshop Secrets

Photoshop Secrets

Are You Frustrated Because Your Graphics Are Not Looking Professional? Have You Been Slaving Over Your Projects, But Find Yourself Not Getting What You Want From Your Generic Graphic Software? Well, youre about to learn some of the secrets and tips to enhance your images, photos and other projects that you are trying to create and make look professional.

Get My Free Ebook