2/24/2012. Image processing and analysis circle. Anatomy Skills Image processing fundamentals. Definitions

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1 Image processing and analysis circle Anatomy Skills Image processing fundamentals Aaron Ponti Definitions Digital image Image processing fundamentals -- Definitions Image resolution Grayscale resolution Spatial resolution Image data types Binary (1 bit) Intensity (8 bit, gray) Indexed (8 bit, color) RGB (24 bit, color) Color spaces 1

2 Definitions Digital image Image resolution Grayscale resolution Spatial resolution Image data types Binary (1 bit) Intensity (8 bit, gray) Indexed (8 bit, color) RGB (24 bit, color) Color spaces Digital image A digital image is a discrete function defined over a rectangular grid (lattice) representing the characteristics of the objects being imaged. sample sampling interval continuous (analog) signal Sampling (digitization): example in 1D. Digital image Digital image In 2D images, each grid element, or pixel (picture element), is defined as a location and a value representing the characteristic of the object in that location. In 3D images, the pixel is called voxel (volume element). A 3D image is just a stack of 2D images. 2

3 Definitions Digital image Image resolution Grayscale resolution Spatial resolution Image data types Binary (1 bit) Intensity (8 bit, gray) Indexed (8 bit, color) RGB (24 bit, color) Image resolution The resolution of an image is a measure of the fidelity of the representation of the original scene. Resolution is related firstly to the characteristics of the imaging system and secondly to the number of pixels (i.e. spatial resolution) and the range of brightness values (i.e. grayscale resolution) that are used for digitization. Color spaces Image resolution :: Grayscale resolution Image resolution :: Grayscale resolution The grayscale resolution of an image is expressed as its bit depth. The maximum number of brightness (i.e. gray) levels in an n-bit image is 2 n. 3

4 Image resolution :: Spatial resolution Image resolution summary Definitions Image data types :: Binary Digital image Image resolution Grayscale resolution Spatial resolution Image data types Binary (1 bit) Intensity (8 bit, gray) Indexed (8 bit, color) RGB (24 bit, color) Binary values (0 or 1) Color spaces 2 gray values : black (0) and white (1) 4

5 Image data types :: Intensity Image data types :: Indexed 256 gray values Intensity (brightness) 256 colors Indices colormap = Image data types :: RGB (24 bit) Definitions Digital image Image resolution Grayscale resolution Spatial resolution Image data types 3 channels RGB, 32 bit color 8 bit gray ( red) Binary (1 bit) Intensity (8 bit, gray) Indexed (8 bit, color) RGB (24 bit, color) Color spaces 8 bit gray ( green) 8 bit gray ( blue) 3 x 256 gray values : 3 x 8 bit = 24 bit image 5

6 Color spaces Image processing fundamentals -- Grayscale image processing basics The RGB color model is an additive color model in which red, green, and blue light are added together in various ways to reproduce a broad array of colors. HSL and HSV are the two most common cylindricalcoordinate representations of points in an RGB color model, which rearrange the geometry of RGB in an attempt to be more perceptually relevant than the Cartesian representation. The Lab color space is designed to approximate human vision. It aspires to perceptual uniformity, and its L component closely matches human perception of lightness. Grayscale image processing basics Color lookup tables (LUT) Grayscale image processing basics Color lookup tables (LUT) Image contrast Image contrast Image histogram Image histogram Pixel statistics Profile statistics Region-of-interest statistics Fundamental spatial image processing tools Neighbors and connections (Matrix) transformations: scaling, rotation, other transforms Interpolation Pixel statistics Profile statistics Region-of-interest statistics Fundamental spatial image processing tools Neighbors and connections (Matrix) transformations: scaling, rotation, other transforms Interpolation 6

7 Color lookup tables (LUT) In biomedical imaging it is common to apply false colors while retaining the original 8 bit scale. Color lookup tables (LUT) Color can also be used to convey a visual meaning to the values or measurements in the image. Colored SEM image of soybean cyst nematode and egg. The color makes the image easier for non-specialists to view and understand the structures and surfaces revealed in micrographs. Differences in blood and oxygen levels in the brain shown by fmri maps. Heat map generated from DNA microarray data reflecting gene expression values in several conditions Grayscale image processing basics Image contrast Color lookup tables (LUT) Image contrast Image histogram Contrast is a measure of brightness difference, both globally (for the whole image) and locally (in neighboring regions). One possible way to express it is: C = ( I S I B ) / I B Pixel statistics Profile statistics Region-of-interest statistics Fundamental spatial image processing tools Neighbors and connections (Matrix) transformations: scaling, rotation, other transforms Interpolation 7

8 Grayscale image processing basics Color lookup tables (LUT) Image contrast Image histogram The grayscale image histogram is a way of illustrating the distribution of gray levels in an image: it shows how many pixels have particular gray values (or gray value ranges: histogram bins ). Image histogram Dynamic range ( for 8 bit) Pixel statistics Profile statistics Region-of-interest statistics Fundamental spatial image processing tools Neighbors and connections (Matrix) transformations: scaling, rotation, other transforms Interpolation The histogram is a very important tool for performing image enhancement. Image histogram Color (RGB) images have three independent channels, displayed in Red, Green and Blue, respectively. Each channel has its own histogram. Grayscale image processing basics Color lookup tables (LUT) Image contrast Image histogram Pixel statistics Profile statistics Region-of-interest statistics Fundamental spatial image processing tools Neighbors and connections (Matrix) transformations: scaling, rotation, other transforms Interpolation 8

9 Pixel statistics :: Profile statistics (plots) Pixel statistics :: ROI statistics Profile Region of interest (ROI) Square Circular Free-form Manually drawn (Semi-)automatically extracted Grayscale image processing basics Common pixel neighborhoods Color lookup tables (LUT) Image contrast Image histogram 4-connected (2D) 8-connected (2D) 6-connected (2D) Pixel statistics Profile statistics Region-of-interest statistics Fundamental spatial image processing tools Neighbors and connections (Matrix) transformations: scaling, rotation, other transforms Interpolation 6-connected (3D) 18-connected (3D) 26-connected (3D) 9

10 Matrix transformations scaling Interpolation Interpolation is a method of constructing new data points within the range of a discrete set of known data points. Used e.g. when scaling or generally transforming an image. There are several interpolation methods: linear, polynomial, spline, rotation free transform Simplest linear interpolation. The five classes of image processing Image processing fundamentals -- The five classes of image processing Image enhancement Image histogram operations Spatial domain filtering Frequency domain filtering Image restoration Deconvolution Correction of geometrical distortions Correction of gray-level inhomogeneities Image analysis Segmentation Classification Image compression Image synthesis Registration Visualization 3D rendering 10

11 Image enhancement Used to improve some aspects of the quality of an image: Increase contrast or and/or brightness Sharpen details Remove noise The five classes of image processing -- Image enhancement Image enhancement operations Contrast enhancement Spatial filtering Frequency filtering Often performed interactively Often performed as preprocessing step in an automated image analysis operation Result is often subjective, but quantitative measures of image quality do exist (i.e. contrast to noise ratio) Image histogram operations :: Contrast enhancement (gray) Image histogram operations :: Contrast enhancement (gray) Datatype (8 bit) dynamic range: Datatype (8 bit) dynamic range: Low contrast image Low contrast image Actual dynamic range Histogram Histogram 11

12 Image histogram operations :: Contrast enhancement (gray) Image histogram operations :: Contrast enhancement (gray) Datatype (8 bit) dynamic range: Datatype (8 bit) dynamic range: Low contrast image Actual dynamic range Low contrast image Actual dynamic range Histogram Histogram Histogram stretching (linear mapping) Histogram stretching (linear mapping) Image histogram operations :: Contrast enhancement (gray) Original Histogram stretching Image histogram operations :: Contrast enhancement (color) Contrast enhancement of color images is typically done by transforming an image to a color space that has image intensity as one of its components (e.g. Lab) and then work on the luminosity layer L of the image. Manipulating luminosity affects the intensity of the pixels, while preserving the original colors. Histogram equalization (global) Adaptive histogram equalization (global) (local) 12

13 Image histogram operations :: Contrast enhancement (color) Original Histogram stretching Spatial filtering operations :: Rank filters (nonlinear filters) Spatial filters are operations applied to a pixel using information taken from the neighborhood of that pixel. In rank (or ordered) filtering, the gray values of pixels within a defined neighborhood around the pixel of interest are arranged in a list in ascending order. The new value of for the pixel of interest is the value at the required rank position in the list. Histogram equalization (global) Adaptive histogram equalization Common rank filters: maximum ( rank = n ) minimum ( rank = 1 ) median ( rank = n/2 ) range ( maximum minimum ) (global) (local) The outcome of the filtering depends on the size and shape of the neighborhood. Rank filters :: Median filter Rank filters :: Median filter Original image Added salt-and-pepper noise Median filtered image Original image Added salt-and-pepper noise Median filtered image The rank filter causes some (limited) blurriness in the image. 13

14 Spatial filtering operations :: Convolution filters (linear filters) Spatial filtering operations Like rank filtering, convolution filter also replaces a pixel value with a new value obtained from the pixel neighborhood. But there are a few more calculations involved. A square kernel is defined to represent the neighborhood around the pixel. Each location in the kernel is associated a numerical value, called weight. a b c d e f g h i Kernel size can vary a lot, depending on the application. This is a 3x3 kernel. The kernel is moved across the image, and the pixel value under the center of the kernel is replaced by the weighted sum of the surrounding pixels. Convolution filters A B C a b c F G H d e f g h i kernel D E I J K L M N O P Q R U V W image S T X Y Mirrored in case of convolution! Used to find similarities between two images. A B C F cg bh K fl em P iq hr U V W A B C F ag bh K dl em P gq hr U V W D ai dn gs X convolution D ci fn is X correlation E J O T Y E J O T Y Spatial filtering operations Spatial filtering operations The convolution kernel is mirrored. For most applications, the kernel is symmetric, and mirroring does not change it. Many (lazy) convolution implementations indeed use correlation. Examples of convolution filters 3x3 average filter 3x3 Gaussian filter High-pass filter Correlation: M = ag + bh + ci + dl+ em + fn + gq + hr + is Convolution: M = ig + hh + gi + fl+ em + dn + cq + br + as 1 h h h

15 Spatial filtering operations Spatial filtering operations Examples of convolution filters 11x11 average filter Prewitt Vertical edge Prewitt Horizontal edge 1/121 1/121 1/ /121 1/121 1/121 1/ /121 h 1/121 1/121 1/ / /121 1/121 1/ / h h Low-pass filter Gaussian kernel Band-pass filter Difference of Gaussians kernel High-pass filter 1 - Gaussian kernel Given the same kernel size, convolution filters introduce more blurring than rank filters. Spatial filtering operations :: Hybrid filters and adaptive filters Hybrid filters may include both rank and convolution steps, or involve other image processing operations. Adaptive filters perform a different operation depending on the image content in the region in which they are being applied. For instance, an adaptive filter could be designed to blur only those regions of an image that do not contain an edge (e.g. anisotropic filtering). Frequency domain filtering :: Spatial and frequency domains Frequency filtering has an effect equivalent to convolution filtering, but the approach is different. Shapes in images are made up of changes in gray levels across the image, from dark to light and back to dark. The rate of this change is called spatial frequency. Increasing spatial frequency A real image will be much more complex and contain many more frequencies. However, we can say that: high spatial frequencies correspond to fine detail, such as noise and edges low spatial frequencies correspond to larger objects with fairly uniform gray values. 15

16 Frequency domain filtering :: Types of frequency filters A high-pass filter preserves high frequencies. A low-pass filter preserves low frequencies. A band-pass (or notch) filter preserves a particular band, or range, or frequencies. Frequency domain filtering :: Approach to frequency filtering Generate a representation to show which spatial frequencies are present in an image: i.e. generate a frequency spectrum using Fourier transformation. Remove selected frequencies from this representation. Reverse the process to get back to the image (spatial representation): i.e. apply the inverse Fourier transformation. Frequency domain filtering :: Advantages of frequency domain filtering It is possible to perform operations that are difficult in the spatial domain, such as removing or enhancing only specific frequencies in the image. Frequency domain filtering Increasing frequency Periodic patterns can be selectively removed or enhanced. Increasing frequency For operations that require larger kernel sizes in the spatial domain (especially in 3D), frequency domain filtering is computationally faster. 16

17 Frequency domain filtering Frequency domain filtering :: Low-pass filtering Set high frequencies to 0. Fourier transform Inverse Fourier transform Remark In practice, one does not create sharp cut-offs in frequency domain, since this creates ringing artifacts that appear as spurious signals near sharp transitions in a signal, i.e. they appear as "rings" near edges. Frequency domain filtering :: High-pass filtering Frequency domain filtering :: Band-pass filtering Set low frequencies to 0. Preserve frequencies only in this band. Inverse Fourier transform Inverse Fourier transform 17

18 Frequency domain filtering Frequency domain filtering Fourier transform? Frequency domain filtering Frequency domain filtering :: Convolution theorem Fourier transform Convolution in direct space = multiplication in frequency (Fourier) space Inverse Fourier transform Inverse filtering 18

19 Image analysis The goal of image analysis is to extract and assign identities to objects in images, and then extract some form of (numerical) measurement from them. The five classes of image processing -- Image analysis Operations: Image segmentation Object classification Image arithmetic Binary operations Examples of measurements: Length, area, volume, density, Usually statistical analyses are then performed on the obtained measurements Image analysis :: Segmentation Image segmentation refers to the process of partitioning a digital image into multiple sets of pixels with the goal of simplifying and/or changing the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. Image analysis :: Classification Classification assigns objects obtained from segmentation into categories based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, etc). There are many classification algorithms divided in several categories: Linear classifiers (Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron, Support vector machines) Quadratic classifiers k-nearest neighbor Boosting Decision trees (Random forests) Neural networks Bayesian networks Hidden Markov models The separation between segmentation and classification is quite blurred in all but the simplest segmentation approaches! 19

20 Segmentation There are three approaches to segmentation: Manual Semiautomatic Automatic Manual segmentation Advantages: Simple, provided there are suitable tools Disadvantages: Very time-consuming Subject to human error Subjective Poor (intra-observer) reproducibility Manual segmentation Manual segmentation involves an expert observer outlining the object of interest in the image. Manual segmentation :: Simple thresholding The user chooses an intensity threshold level in the histogram. The software shows a preview to facilitate the selection of the threshold. The computer simply provides tools to help with drawing. 20

21 Manual segmentation :: Simple thresholding After the threshold is set, all values below the threshold are set to 0. The values above the threshold are either left unchanged, or set to white (e.g. 255). Semi-automatic segmentation In semi-automatic segmentation, the observer helps the segmentation by providing the software with a rough selection to refine. Region growing Active contours, snakes, and deformable surfaces Semi-automatic segmentation :: Region growing Semi-automatic segmentation :: Region growing The operator defines a range of gray levels that represent the region, and chooses a pixel (the seed point) that is known to be part of the region. All the pixels that are in the intensity range and are connected to the seed point are selected as being part of the region. Alternatively, the software returns the boundary of the region as an editable line. Range = 10 gray values 21

22 Semi-automatic segmentation :: Active contours, deformable surfaces Semi-automatic segmentation :: Active contours / snakes Active contour model, also called snakes ( = deformable splines), is a framework for delineating an object outline from a possibly noisy 2D image. This framework attempts to minimize an energy associated to the current contour as a sum of an internal and external energy: The external energy is supposed to be minimal when the snake is at the object boundary position (e.g. low energy values for high gradient values); The internal energy is supposed to be minimal when the snake has a shape which is supposed to be relevant considering the shape of the sought object (e.g. high energy to elongated contours (elastic force) and to bended/high curvature contours (rigid force), considering the shape should be as regular and smooth as possible). The framework also allows for training. Range = 10 gray values Semi-automatic segmentation :: Deformable surfaces Automatic segmentation Automatic (computational) segmentation methods are areas of active research. We will only look into a very simple automatic segmentation algorithm: Otsu s method. Range = 10 gray values 22

23 Automatic segmentation :: Otsu s method Automatic segmentation :: Otsu s method Otsu's method is used to automatically perform histogram shape-based image thresholding. The algorithm assumes that the image to be thresholded contains two classes of pixels (e.g. foreground and background) then calculates the optimum threshold separating those two classes so that their combined spread (intra-class variance) is minimal. threshold This image probably contains more than two classes of pixels. Range = 10 gray values Ideal case Range = 10 gray values There are extensions of Otsu s algorithm that find multiple thresholds. Image arithmetic Image arithmetic is the implementation of standard arithmetic operations, such as addition, subtraction, multiplication, and division, on images. Image arithmetic has many uses in image processing both as a preliminary step in more complex operations and by itself. Examples: Image subtraction can be used to detect differences between two or more images of the same scene or object. Image subtraction or division are used for shading corrections. Image multiplication is used for masking. Image addition is used for visualizing composite images (where each channel has a color which is not exclusively either Red, Green, or Blue). Binary and morphological operations Binary and morphological operations can be used to perform common image processing tasks, such as contrast enhancement, noise removal, thinning, skeletonization, filling, and segmentation. Morphology is a broad set of image processing operations that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. By choosing the size and shape of the neighborhood, you can construct a morphological operation that is sensitive to specific shapes in the input image. 23

24 Binary and morphological operations :: Example: Image dilation Dilation is one of the two basic operators in the area of morphology, the other being erosion. It is typically applied to binary images, but there is a version for grayscale images. Dilation gradually enlarges the boundary of regions of foreground pixels (i.e. white pixels): thus area of foreground pixels grow in size, while holes within these regions become smaller. The dilation operator takes two pieces of data as inputs. The first is the image which is to be dilated. The second is a (usually small) set of coordinate points known as a structuring element, for example: Binary and morphological operations :: Example: Image dilation Mathematically, dilation is defined as: This basically means, place the structuring element B on some position on the image A. If the pixel value of A under the center of B is 1, replace the n x n pixels in A with the structuring element B. Example: Binary and morphological operations Binary and morphological operations Binary gradient mask Problem: detecting (i.e. segmenting) a cell A first rough segmentation is given by a threshold of gradient of the image, to give the strongest edges. 24

25 Binary and morphological operations Binary and morphological operations Dilated gradient mask Image dilation: the binary gradient mask is dilated. Dilated gradient mask with filled holes Image flood-fill: the dilated gradient mask shows the outline of the cell quite nicely, but there are still holes in the interior of the cell that we flood-fill. Binary and morphological operations Binary and morphological operations Cleared border image Here we got rid of the cell at the border. Admittedly, this is not strictly a morphological operation Segmented image Image erosion: finally, in order to make the segmented object look natural, we smoothen the object by eroding the image twice with a diamond structuring element. 25

26 Binary and morphological operations The five classes of image processing -- Image restoration Segmented image with outline An alternate method for displaying the segmented object would be to place an outline around the segmented cell. Image restoration Required if the acquisition method (e.g. the microscope) causes: geometric distortion blurring gray-level inhomogeneities Geometric distortion Although distortion can be irregular or follow many patterns, the most commonly encountered distortions are radially symmetric, arising from the symmetry of a lens. The radial distortion can usually be classified as one of two main types: Barrel distortion and Pincushion distortion. In barrel distortion, image magnification decreases with distance from the optical axis. The apparent effect is that of an image which has been mapped around a sphere (or barrel). In pincushion distortion, image magnification increases with the distance from the optical axis. The visible effect is that lines that do not go through the centre of the image are bowed inwards, like a pincushion. 26

27 Blurring Any imaging system has a blurring effect. If a point object is imaged, the resulting image is not a perfect point, but a spread-out version of that point. Mathematically, this blurring effect is described using the convolution operation: Removing blur Deconvolution Knowledge of the PSF of a system is useful because of the potential for removing blurring by applying the inverse of convolution, deconvolution. We saw that convolution can be performed in frequency space. Can we deconvolve by inverse filtering, then? f Every imaging system has a characteristic blurring function called point spread function (PSF): h(x) Point object, whose dimensions are below the resolution limit. Point spread function, lateral view. Axial view. f h F (f) F H G Point spread function, lateral view.? F -1 (G/H) g F -1 (G) Axial view. F (h) Deconvolution :: Cookie cutter Deconvolution :: Deconvolving trains Missing frequencies XZ Sub-resolution train Noise-free convolution and deconvolution 27

28 Deconvolution :: Deconvolving trains Deconvolution :: Deconvolving trains Confocal Confocal Restored confocal Widefield Widefield Restored widefield Sub-resolution train Noise-free convolution and deconvolution Sub-resolution train Noise-free convolution and deconvolution Deconvolution :: MLE Correction of gray-level inhomogeneities (shading correction) Artifacts! Extreme noise amplification! Background estimation Rolling ball Morphological Opening H = 0 at many places! Inverse filtering will never allow us to recover the true object function f. Subtraction Deconvolution is performed in practice with iterative algorithms like the maximum likelihood estimation (MLE) algorithm. Future promising algorithms are based on wavelets. 28

29 Correction of gray-level inhomogeneities (shading correction) Correction of gray-level inhomogeneities (shading correction) A correction is important, for instance, for stitching applications to prevent border effects. A correction is important, for instance, for stitching applications to prevent border effects. Image compression Image data files are often very large in terms of number of bytes Large disk space usage Slow transfer over networks The five classes of image processing -- Image compression Compression algorithms exists Lossless Lossy 29

30 Image compression :: Lossy compression Image compression :: Lossy compression 100%, 64kb Jpeg, 80%, 12kb Image compression :: Lossy compression Image compression :: Lossy compression Jpeg, 50%, 7kb Jpeg, 10%, 3kb 30

31 Image compression :: Lossy compression Image compression :: Lossless compression Jpeg, 1%, 1kb 100%, 64kb Image compression :: Lossless compression PNG, best compression, 48kb The five classes of image processing -- Image synthesis 31

32 Image synthesis Image synthesis is the general term for bringing together information from more than one image. Synthesis can be separated into two parts: Image registration, covering processes required to bring images into spatial alignment Image registration How to map one onto the other? Image 1 Visualization, which allows information (from the aligned datasets) to be viewed Reasons for performing image synthesis include: Assessment of disease progression or growth using series of images Combination of structural and functional information from different imaging modalities Comparison of corresponding regions in different individuals by matching both to a standard coordinate system Generation and analysis of atlases or templates representing the typical appearance in health or disease Arithmetical or statistical operations which require registered images Image 2 Smaller, rotated version of image 1. Image registration The four steps of image registration: Feature extraction One needs to identify features that appear in both images. This can be done manually, by placing markers on the images, or automatically. All pixels intensities could also be used. Pairing (also called: identifying correspondences ) Image registration Example: rigid transformation 1. The user chooses some fiduciary marks by clicking on them 2. The positions are refined by cross-correlation 3. The (rigid) transformation matrix is obtained by Least-Square fitting 4. Image 1 is transformed (mapped) onto image 2 by matrix transformation and interpolation. Once features have been identified in both images, they need to be analyzed to determine which feature in one image matches which feature in the other image. The features can be fiducial landmarks or pixel intensities (or related values). The term similarity metric is the general way of describing a measure that is used in the process of pairing points with similar properties. Examples: correlation coefficient, mutual information, entropy 1 Calculation of transformation The transformation is the mathematical operation that will give the best alignment (mapping) of all pairs of features. There are four different types of transformations: Rigid: in a rigid transformation all parts of the object are assumed to move as a whole, e.g. translation, rotation Affine: in affine transformations, straight lines remain straight and parallel lines remain parallel: e.g. scaling, shearing Projective: projective transformation include further deformation. Straight lines remain straight, but parallel lines do not remain parallel Curved or elastic (non-rigid): this is the most general case of transformation. Straight lines need not remain straight. It should be used with care. Application of transformation 34 This step is often called matrix transformation or matrix operation. Applying the transformation will very often require interpolation. 32

33 Image registration End result of registration Image registration example :: Atlas generation 1) 2) Mouse hippocampus 3) A user draws the outline of the hippocampus over many aligned slices to reconstruct the 3-D shape. The same operation is performed on 3 mice, and the resulting 3D objects are registered on top of each other to generate an atlas (i.e. a model of the hippocampus). Image registration example :: Correlative microscopy Biocytin labelled cell, Z projection of 80 confocal planes. (200nm X-Y pixel size) Visualization Visualization is the display of image data. Visualization in biomedical imaging needs specialized approaches: Visualized objects can have a huge range of scales, from single molecules and cells to body parts Different attributes of these objects can be visualized, including biophysical, biomechanical and physiological properties. The result of visualization can be used for diagnosis, treatment planning, rehearsal, assessment, and intra-operative guidance. There are several approaches to visualization: Slice mode Projection mode Volume mode Surface mode Same cell imaged in SBFSEM (98x98x30 microns) 33

34 2/24/2012 Visualization :: Slice mode Shown: one slice (focal plane) in the middle of the 3D acquisition. Visualization :: Slice mode Shown: all slices in one view. Fluorescence image of the mouse retina. Red: collagen IV, Green: GFAP (source Imaris demos) Visualization :: Maximum intensity projection Visualization :: Volume rendering 34

35 Visualization :: Surface rendering Visualization :: Surface rendering (fancy) Last remarks Last remarks Every image processing action taken must make sense: Do not apply any filter just because like this the image looks better. Remember that applying the wrong filter can destroy real features in the image and also introduce spurious ones that actually DO NOT EXIST. Keep a complete protocol of the actions taken: The result (i.e. the figure in a paper) must be reproducible by you and by others. Make sure you know the characteristics of your acquisition system (e.g. a microscope), because this will define: The parameters for acquiring the images. The parameters for processing the images. Image processing was born to process different kinds of pictures than biomedical ones (which are diffraction-limited, with extremely low SNR, 3D or more, ) There is a lot of work going on extending standard image processing techniques for these new fields. Image processing of microscopy data requires a lot of statistical modeling, learning algorithms, and the like (which we haven t touched in this introductory course). 35

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