Presentation on theme: "From Images to Answers A Basic Understanding of Digital Imaging and Analysis."— Presentation transcript:
From Images to Answers A Basic Understanding of Digital Imaging and Analysis
What is an Image? Computers store data and understand data in numerical form. We can say that a digital image is a numerical representation of a “picture” – a set of numbers interpreted by the computer which creates a visual representation that is understood by humans. 255 255 199143 97 18732 12 3423 22 11 244 198 179123 94 19532 43 5213 32 11 253 217 23468 185 9713 12 2711 14 26
Pixels are identified by their position in a grid (two- dimensional array), referenced by its row (x), and column (y). The Pixel Array Pixel- A “picture element”. Each element contains spatial and intensity information.
Bitmaps At each pixel position the image is sampled and quantified. An integer representing the brightness or darkness of the image is generated for each pixel This integer. A collection of these gray values is called a bitmap. 255 250 200 220 253 254 249 180 0 0 167 254 220 0 0 0 0 240 210 104 0 0 123 234 243 239 160 190 240 251
Resolution Image Resolution- Overall image quality Spatial Resolution- Pixel size, image magnification Brightness Resolution- Pixel depth Optical Resolution- Lens characteristics How Big Is a Pixel?
What components are involved in imaging? Input device- the source of the images; camera, microscope, etc. Interface hardware- the connection between the input device and the computer. Imaging software- the user interface to all the imaging components. Output devices- printers, image storage devices, monitors.
The Analytical Imaging Process Sample Preparation- Sectioning, staining, labeling, etc. Acquisition- how do we acquire an image into the computer? Enhancement- how do we make it look better to extract information? Identification- which attributes of the image are we interested in? Measurement- what information can we obtain? Report Generation- how can we present this information? Archive- how can we store the information?
Acquisition After sample preparation, acquisition is the most important aspect of image analysis. Has the specimen been prepared properly? Is the imaging apparatus properly set up? Kohler Illumination for Microscopes Clean Optics Color cameras should be color-balanced Monochrome cameras should have dark-field subtraction Good dynamic range should be established in the image For fluorescence images- turn OFF autoexposure
Image Enhancement There are basic ways to enhance an image: Modify its intensity index: brightness, contrast, gamma Background correction Apply a spatial filter or arithmetic operation And advanced enhancement methods: Manipulate the image frequencies: fast fourier transform Morphological transformations: erode, dilate
Brightness- Overall amount of “light” in an image Contrast- The degree of difference between lightest and darkest areas Gamma- Enhances ‘midtones’ while leaving extremes unchanged The higher the bit depth, the better the dynamic range of the image – allowing for greater information observance in “sensitive” samples Image Enhancement
Histogram Stretch Low Dynamic Range- Medium Contrast Full Dynamic Range- Good Contrast
Automatic Best-fit EqualizationBackground Flattening Background Correction Original
Spatial filters change the look of an image and are divided into two categories. Convolution Filters: Low-pass- Blurs or smoothes an object Sharpen- Enhances all intensity transitions Median- Removes random impulse noise Morphological Filters: Edge Detection- highlights edges Erosion- Makes objects smaller Dilation- Makes objects larger Spatial Filtering
Once the attributes of an image are enhanced and clearly visible, identification can be done as follows: Thresholding techniques- allowing the software to identify objects, based on intensity variations from background or other objects using either grayscale or color intensities Area of Interest (AOI)- manually defining the objects Object Identification
Grey Scale Threshold The overall objective of thresholding is to extract the objects of interest- to distinguish them from other objects or background.
Size Shape Intensity/Integrated Optical Density Populations Statistics Once objects are identified, we are dealing with a set of pixels, which are a set of numbers. We are then able to measure anything we need such as Measurement Parameters
Area Percentages How much area is covered by the different intensities?
Using intensity transitions it is possible to measure thicknesses of objects. Edge tracing tools may be employed to do this: Thickness Measurements
Specialized Measurements Specialized measurements may be made with standard image analysis tools- Colocalization Object Tracking FRET Analysis Ratiometric Imaging
Dynamic Data Exchange (DDE)- Sending data to Excel for further statistical analysis Data Collection- Collection of analysis data from multiple images into a single space, which can then be sent elsewhere or used to create reports Report Generation- Custom templates used to create standardized reports. Data Output
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