A Brief Introduction to Image Processing
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1 A Brief Introduction to Image Processing Alexandre Cunha Center for Advanced Computing Research California Institute of Technology Methods of Computational Science 2009
2 Outline 1 Content 2 Tools Software packages Example - counting and measuring cells 3 Some image processing tasks Enhancement Restoration Segmentation Registration 4 Mathematical Morphology Introduction Erosion Dilation Opening Closing Other operators 5 Examples Random image Cells 6 References Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 2 / 39
3 Outline 1 Content 2 Tools Software packages Example - counting and measuring cells 3 Some image processing tasks Enhancement Restoration Segmentation Registration 4 Mathematical Morphology Introduction Erosion Dilation Opening Closing Other operators 5 Examples Random image Cells 6 References Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 2 / 39
4 Outline 1 Content 2 Tools Software packages Example - counting and measuring cells 3 Some image processing tasks Enhancement Restoration Segmentation Registration 4 Mathematical Morphology Introduction Erosion Dilation Opening Closing Other operators 5 Examples Random image Cells 6 References Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 2 / 39
5 Outline 1 Content 2 Tools Software packages Example - counting and measuring cells 3 Some image processing tasks Enhancement Restoration Segmentation Registration 4 Mathematical Morphology Introduction Erosion Dilation Opening Closing Other operators 5 Examples Random image Cells 6 References Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 2 / 39
6 Outline 1 Content 2 Tools Software packages Example - counting and measuring cells 3 Some image processing tasks Enhancement Restoration Segmentation Registration 4 Mathematical Morphology Introduction Erosion Dilation Opening Closing Other operators 5 Examples Random image Cells 6 References Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 2 / 39
7 Outline 1 Content 2 Tools Software packages Example - counting and measuring cells 3 Some image processing tasks Enhancement Restoration Segmentation Registration 4 Mathematical Morphology Introduction Erosion Dilation Opening Closing Other operators 5 Examples Random image Cells 6 References Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 2 / 39
8 Software and tools Popular image processing tools and libraries (APIs) - might be able to solve 90% of your image processing needs: Netpbm Web: free open source. Description: Netpbm R is a toolkit for manipulation of graphic images in 2D and 3D, including conversion of images between a variety of different formats. There are over 300 separate tools in the package including converters for about 100 graphics formats. Netpbm does not contain interactive tools and doesn t have a graphical interface. Native formats: Images have a very simple format in either plain text or binary (no compression) thus making it easy to interpret and work with. PBM - Portable Bit Map (black & white), PGM - Portable Gray Map (grayscale), PPM - Portable Pixel Map (color) P2 magic number to indicate file type (P2 indicates PGM plain text) image dimensions, height and width 255 maximum itensity value per pixel, quantization Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 3 / 39
9 Software and tools ImageMagick Web: open source. Description: ImageMagick R is a software suite to create, edit, and compose digital images. It can read, convert and write images in a variety of formats (over 100). Use ImageMagick to translate, flip, mirror, rotate, scale, shear and transform images, adjust image colors, apply various special effects, or draw text, lines, polygons, ellipses and Bézier curves. APIs for C, C++, Python, Ruby, PHP, Perl, and many others. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 4 / 39
10 Software and tools Matlab Image Processing Toolbox Web: commercial Description: It provides a comprehensive set of reference-standard algorithms and graphical tools for image processing, analysis, visualization, and algorithm development. You can restore noisy or degraded images, enhance images for improved intelligibility, extract features, analyze shapes and textures, and register two images. Most toolbox functions are written in the open MATLAB R language, giving you the ability to inspect the algorithms, modify the source code, and create your own custom functions. Free for Caltech community. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 5 / 39
11 Software and tools Gimp Web: open source Description: GIMP is an acronym for GNU Image Manipulation Program. It is a program for such tasks as photo retouching, image composition and image authoring. This is your free Photoshop. It can be used as a simple paint program, an expert quality photo retouching program, an online batch processing system, a mass production image renderer, an image format converter, etc. The advanced scripting interface allows everything from the simplest task to the most complex image manipulation procedures to be easily scripted. GIMP is GUI front end for many programs available in Netpbm. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 6 / 39
12 Software and tools ImageJ (and Fiji) Web: open source. Description: ImageJ is a public domain Java image processing program inspired by NIH Image for the Macintosh. It runs either as an online applet or as a downloadable application, on any computer with a Java 1.4 or later virtual machine. Downloadable distributions are available for Windows, Mac OS, Mac OS X and Linux. Very popular among biologists. Fiji is a recent image processing package based on ImageJ (actually, a rewrite). Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 7 / 39
13 Counting cells A biological example of an image processing task: counting concentration in the nuclei of HeLa cells. pamchannel -infile hela.ppm -tupletype GRAYSCALE 0 extract red channel pamchannel -infile hela.ppm -tupletype GRAYSCALE 1 extract green channel pamchannel -infile hela.ppm -tupletype GRAYSCALE 2 extract blue channel Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 8 / 39
14 Counting cells A biological example of an image processing task: counting concentration in the nuclei of HeLa cells. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 9 / 39
15 Image processing tasks The majority of image processing tasks belongs in one of the following categories: Enhancement (contrast improvement, smoothing, sharpening) Restoration (noise reduction, deblurring) Segmentation (counting, identification, separation) Registration (comparison) Compression (transmission, storage, fast processing) Pattern recognition (classification, matching) Others (tomography, morphing, inpainting, color correction,...) Alexandre Cunha Introduction to Image Processing 10 / 39
16 Image enhancement Enhance image contrast by manipulating its intensity and/or histogram. Contrast stretching: expand intensities to cover full range [0,255] while discarding the 1% to 2% tails. Alexandre Cunha Introduction to Image Processing 11 / 39
17 Image enhancement Enhancement using the Retinex algorithm by Edwin Land (look for it in GIMP): Retinex tries to ensure that the perceived color of objects remains relatively constant under varying illumination conditions. Images from NASA Langley Research Center. Alexandre Cunha Introduction to Image Processing 12 / 39
18 Image restoration Most images are contaminated with some type of noise which is due either to the acquisition process or noisy transmission lines. The goal of noise reduction is to remove as much noise as possible from the image while maitaining the signal intact. Alexandre Cunha Introduction to Image Processing 13 / 39
19 Image restoration A simple denoising model is to assign to each pixel the average intensity of neighboring pixels. This blurs the image and destroys sharpness. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 14 / 39
20 Image restoration We can do much better. Example of filtering a high resolution MRI where edges are preserved and noise judiciously removed with the nonlocal means method. Note that the thin filaments were not blurred nor destroyed after denoising. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 15 / 39
21 Image segmentation Segmentation is the process of separating distinct, homogeneous regions from other regions and the background. Alexandre Cunha Introduction to Image Processing 16 / 39
22 Image segmentation Robust and efficient segmentation methods are sought everywhere (in Astronomy, Medicine, Neuroscience, Forensics, Biology, Materials Science, Robotics,...): From DIADEM web page: The lack of powerful - and effective - computational tools to automatically reconstruct neuronal arbors in 3D has emerged as a major technical bottleneck in neuroscience research. Despite the advent of computer technology that enables mapping in three dimensions, neuronal reconstructions are still largely performed by hand and reconstructing a single cell may take months. Organizers of a new competition hope to provide incentives for the development of new computer algorithms to advance the field - including a cash prize of up to $75,000 for the qualifying winner. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 17 / 39
23 Image segmentation Neuronal arbor segmentation: Alexandre Cunha Introduction to Image Processing 18 / 39
24 Image registration When we register two images we aim to align them so that their features match as close as possible. A displacement map is usually produced which gives us an idea of how much warping is needed to achieve a good matching. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 19 / 39
25 Image registration A warped image grid gives an idea of the displacements necessary to match the purple contour to the red contour representing the boundaries of human brain ventricles. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 20 / 39
26 Content Tools Some image processing tasks Mathematical Morphology Examples References Image registration Registration is a standard tool in the construction and use of anatomical atlases where we map arbitrary subjects to a canonical space in order to make comparisons of shape and size. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 21 / 39
27 Mathematical morphology Morphological image processing consists of a set of operations that transform images according to rules of set theory. It was originally developed for binary images and later extended to grayscale images. It was primarily developed in the 60 s by French mathematicians Jean Serra and Georges Matheron. The basic idea in binary morphology is to probe an image with a simple, pre-defined shape, called the structured element, drawing conclusions on how this shape fits or misses the shapes in the image. The four basic operations of mathematical morphology are: Erosion: shrink objects Dilation: grow objects Opening: erosion followed by dilation (disconnect parts) Closing: dilation followed by erosion (remove holes) Matlab provides an almost complete set of math morphology routines. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 22 / 39
28 Erosion Given an image A and a structured element B we define the erosion operator as A B = T b B A b The dark blue image A is eroded by the circle B giving rise to a smaller region shown in light blue: Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 23 / 39
29 Dilation Given an image A and a structured element B we define the dilation operator as A B = S b B A b The dark blue image A is dilated by the circle B producing a larger region which includes the additional light blue area: Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 24 / 39
30 Erosion x Dilation, an example Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 25 / 39
31 Opening Given an image A and a structured element B we define the opening operator as an erosion followed by dilation, A B = (A B) B The dark blue square A is opened by the circle B producing a smaller rounded square which are the places ocupied by B when translated inside A. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 26 / 39
32 Closing Given an image A and a structured element B we define the closing operator as an erosion followed by dilation, A B = (A B) B The dark blue region A is closed by the circle B producing a larger region with some rounded corners which are the places between the translation of B outside A and A itself. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 27 / 39
33 Other operators Other important math morphology operators are (mostly can be written as combinations of the basic operators): Thinning : reduce thick lines to thin, 1 pixel wide lines (repeated erosion) Thickening : opposite of thinning, produce wider lines (repeated dilation) Skeleton : generates a skeleton, a central line equidistant to the boundary of the shape Prunning : remove dangling lines, spurs Watershed : image segmentation You can try all these in Matlab. Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 28 / 39
34 Example, random image From left to right, top to bottom: sample image, erosion, dilation, opening, and closing. sample image erosion dilation - opening closing Alexandre Cunha (cunha@caltech.edu) Introduction to Image Processing 29 / 39
35 Example, plant cells sepal cells in the Arabidopsis plant, acquired by confocal microscopy Alexandre Cunha Introduction to Image Processing 30 / 39
36 Example, plant cells denoised cells Alexandre Cunha Introduction to Image Processing 31 / 39
37 Example, plant cells thick but discontinuous edges Alexandre Cunha Introduction to Image Processing 32 / 39
38 Example, plant cells thick edges with filled holes Alexandre Cunha Introduction to Image Processing 33 / 39
39 Example, plant cells thin edges with spurs and dangling segments Alexandre Cunha Introduction to Image Processing 34 / 39
40 Example, plant cells spurs and small islands removed Alexandre Cunha Introduction to Image Processing 35 / 39
41 Example, plant cells totally clean and nicely segmented image; guard cells manually removed Alexandre Cunha Introduction to Image Processing 36 / 39
42 Example, plant cells colored version Alexandre Cunha Introduction to Image Processing 37 / 39
43 References If you have to buy one book... Digital Image Processing Rafael C. Gonzalez and Richard E. Woods Prentice Hall, 3rd edition, Alexandre Cunha Introduction to Image Processing 38 / 39
44 References Other books of interest: General: W. K. Pratt, Digital Image Processing, John Wiley and Sons, J. S. Lim, Two-Dimensional Signal and Image Processing, Prentice Hall, K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, Bovik, Handbook of Image and Video Processing, Academic Press M. Tekalp, Digital Video Processing, Prentice Hall, Mathematical Morphology: P. Soille, Morphological Image Analysis, 2nd edition, Springer, 2004 E. Dougherty & R. Lotufo, Hands-on Morphological Image Processing, SPIE, 2003 Mathematical Analysis and PDEs: J. Chan and J. Shen, Image Processing and Analysis, SIAM, 2005 G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing, 2nd edition, Springer, 2006 Alexandre Cunha Introduction to Image Processing 39 / 39
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