Digital Image Processing Unit-5.pdf

7/29/2019 Digital Image Processing Unit-5.pdf

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LCE/7.5.1/RC 01TEACHING NOTES

Department : ELECTRONICS& COMMUNICATION ENGINEERING Unit : V Date :Topic name : Color Image Processing - 1 No. of marks allotted by JNTUK :

Books referred : 01. Digital Image Processing by R C Gonzalez and R E Woods02. www.wikipedia.org 03. www.google.com

Introduction: Color image processing has many advantages over human assessment of wounds and skin

lesions; digital image processing techniques are objective and reproducible. Color image processinghas significant potential, since the analysis and comparison of color images is a task which humansfind particularly difficult. With the current technological trends in computer hardware and scanners,computerized systems are becoming increasingly affordable.

There are two main applications of color image processing in the field of skin imaging. Theyare the assessment of the healing of skin wounds or ulcers, and the diagnosis of pigmented skinlesions such as melanomas. The analysis of lesions involves more traditional image processingtechniques such as edge detection and object identification, followed by an analysis of the size,shape, irregularity and color of the segmented lesion. However, in wound analysis, although it isnecessary to detect the wound border and to calculate its area, analysis of the colors within thewound site is often more important. In short, wounds generally have a non-uniform mixture of yellow slough, red granulation tissue and black necrotic tissue, and the proportions of each are animportant determining factor in the healing state of the wound.

In the case of assessing skin lesions in the clinic, clinicians have to decide whether or not askin lesion should be tested further, and analysis using color image processing could provideadditional information to aid such decisions. A very general review on digital imaging has been

written by Perednia et al. Their review covers the basics of image analysis, transmission and storageon computer. One problem of storage is that image files can be very large. However this can bereduced to some extent by use of data compression techniques without significantly reducing theinformation content or quality of an image. One of the groups reviewed found that dermatologistswere able to diagnose lesions with compressed digital images without significant change from theirperformance with the original digital image.Color Image Processing:

There are relatively few research groups around the world involved in color imageprocessing of wounds or lesions. Fewer still have experimented with techniques for assessing skinwounds using color image processing. Herbin at the Department de Biostatistiques et InformatiqueMedicale, Hopital Cochin, Paris, France analyzed RGB color images digitized from Kodachrome colorslides of wounds, in order to quantitatively assess wound healing kinetics. They studied artificiallycreated blister wounds on the forearms of eight volunteers over twelve days. The wounds werephotographed with a 2mm white paper disk placed adjacent to the wound site, which served as botha color and geometric reference. Each digitized slide image was corrected, using the white referencepatch. They evaluated a simple color index of healing for these uniformly colored wounds and usedan automated approach to determine the wound area.

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Although they have tackled the problem of automating wound analysis, their method wasnot as complex as would be necessary for the analysis of natural wounds which have a highlyvariegated coloring. Another group, Arnqvist at the Department of Scientific Computing, Universityof Uppsala, Sweden, experimented with a method for the semi-automatic classification of secondaryhealing ulcers. Color photographs were acquired with a 35mm still camera with ring flash. Thephotographs were then digitized into a 24-bit RGB image. Photographs were taken at an optimalangle of thirty degrees to the wound plane normal in order to reduce reflections from the flash. Ineach scene they placed a scale, calibrated in millimeters, to enable estimation of the wound area.The wound tissue types were divided into black necrotic Escher, yellow necrosis/fibrin, redgranulation tissue, and a fourth class which contained the undesired reflections from glossy parts of the wound which were almost entirely white. Their method was only semi-automatic because askilled operator had to use a mouse to track around the wound boundaries to define the region of interest. The operator then chose one wound classifier from a database of 16 which had beencreated using hundreds of photographs of different wounds taken under various lighting conditions.An algorithm then segmented the wound image into the three tissue types, the segmentationdepending on the classifier chosen. Each classifier related to a type of wound. Finally, the operator-defined binary image and the segmentation performed by the classifier were combined to give anoverall wound classification. Finally the areas of each of the three tissue type zones and the total

wound area were computed using the scale.At the University of Glamorgan, Wales, Jones and Plassmann, of the Department of

Computer Studies have developed an instrument, known as MAVIS ( Measurement of Area and

Volume Instrument ), to measure the dimensions of skin wounds. It involves capturing two images of the wound in quick succession whilst the wound is illuminated with color-coded structured light. Thisenabled phvolume measurements to be made. A color CCD video camera with a 250 W tungstenhalogen bulb was used for imaging the skin directly. MAVIS is capable of measuring the area phandvolume of deep three-dimensional wounds. For each acquisition, a magnesium oxide chip, placedalongside the wound, was used as a white standard. The group has experimented with algorithmsthat use color to segment an image into one of three tissue types: healthy skin, wound tissue andepithelialisation tissue. They found that epithelialisation tissue is often a darkened band around thewound, separating skin from wound. In all, they tried six measurement parameters: the R, G, and Bintensities; Hue; Saturation; and gray-level intensity. The R, G and B intensities were only examinedin isolation and they concluded that, `It is clear from inspection of Red, Green and Blue planeintensity-level histograms for the different tissue types that straightforward thresholding of theseplanes cannot produce a good segmentation which distinguishes between wound and skin or woundand surrounding connected tissue'.


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They conclude that in looking at such 1D histograms, segmentation is only partiallyachievable, but using a 3D RGB histogram space, volume clusters may be more widely separated.

One group has made some progress with such a 3D RGB color histogram clusteringtechnique. Mekkes at the Department of Dermatology, University of Amsterdam, The Netherlands,have been using color images to assess the healing of wounds. They recognized that many of theenormous number of wound care materials that have been introduced into the market have notbeen properly tested in randomized, double-blind clinical trials. They pointed out that such trials aredesperately needed to supply clinicians with information to guide them in their choice of woundcare products. They compared a degrading product with an old form of treatment using salinesoaked gauzes. They found that for a proper evaluation of the cleansing effect of both treatments,color aspects were more important than wound size. Their technique measured the shift from blackto yellow necrotic tissue to red granulation tissue. Their aim is to create an automatic computerizedmethod which can be used as a reference standard or `gold-standard' for color wound analysis. Intheir system, images were acquired directly with an RGB video camera and frame grabber. Theyused two polarized filters to reduce unwanted reflections. A clinician’s knowledge of the colors insecondary healing ulcers was used for calibration of the system. The computer had to be instructedin advance as to which colors can be encountered in the granulation region and which in the necroticregion of a wound. They found that clusters in RGB space for a given tissue type formed an

irregularly shaped 3D cloud, and so simple thresholding along the R, G and B axes would not help tosegment the image into these three tissue types. For this reason, large classification tables, of thecolors present in each tissue type, were created semi-automatically by the computer with the aid of a clinician. One problem discovered was that although digital image analysis could detect the woundmargins automatically, the color differences between granulation tissue, surrounding skin and thethin partly transparent layer of newly formed epithelium were too small to allow automaticdetection.

Finally, there are a few other groups that have done some work in color image processing of wounds. El Gammal at the Dermatological clinic of Ruhr University, Germany, wrote a very shortpaper on the use of the black-yellow-red classification scheme to evaluate the debridement activityof wounds. Solomon at the University of Otego Medical School, New Zealand developed a simpleand rapid technique to measure the size of skin wounds and ulceration using two-dimensional colorvideo images of ulcers. The images were stored on video cassette, thus rendering low image quality.The work did not involve the development of color image processing algorithms, but a novel methodto correct for limb convexity was presented. Smith at the University of Akron, Ohio, USA, evaluatedwound repair in humans and animals using video images. Images were stored on VHS video tape,and only basic color image processing techniques were applied to the digitized images.


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Department : ELECTRONICS& COMMUNICATION ENGINEERING Unit : V Date :Topic name : Image Acquisition, Digitization & Calibration No. of marks allotted by JNTUK :


Image Acquisition, Digitization and Calibration: Some groups used photography to capture the original wound image, others decided to use

a video camera and frame grabber for direct image capture and digitization. If photography is used,then the type of film used will affect the image quality. Once the film has been processed then theslides need to be digitized and for this a color slide scanner can give a very high spatial resolution, upto 2,700 dots per inch. Such a scanner can capture 95% of the information in a high resolution colorslide. For 35mm film, this means that a resolution of over 3000 pixels across the image is possible.Standard 35mm still cameras have the added advantage that they are highly portable, and can easilybe used outside the laboratory or clinic, in the patient's home for instance. Care must be taken withthe exposure setting on the camera. In considering just grayscale images, Hall discovered thatdifferent exposures of the film have a significant effect on the histogram of the image. This has manyrepercussions in image processing since histogram analysis is a major tool of the image processor.

Frame grabbers, the digitizer boards in computers that connect to the video camera, do nothave such high resolution. Typically frame grabbers digitize images to only 512 pixels by 512 pixels,and resolution does not meet up to the standards of photography or color slide scanners. Colorresolution is also inferior for frame grabbers; typically a color frame grabber has a color resolution of 24-bits, corresponding to 16 million colors. Their advantage is that digitization of the images takesplace as they are acquired, and consequently no photographic processing time is incurred. However,

although video cameras can be as compact as a still camera, and use of a laptop computer allows thesystem to be portable, such a system tends to be less versatile than using a 35mm still camera. Thisrenders imaging outside the laboratory less suitable. The best solution would be to use a phdigitalstill camera, but these are still fairly new on the market and rather expensive. Still, costs aregradually falling and so they are becoming a viable option. They are quick, as no photographicprocessing is needed, digitization occurs immediately, and they render high resolution images,comparable with slide scanners.

Calibration is a very important step and often overlooked by programmers since they oftenaim to improve results by writing more complex algorithms rather than aim to improve the quality of the original input image. By considering the nature of non-uniformities in an image acquisitionsystem due to the non-linear response of electronic devices and non-uniform lighting, methods canbe devised to measure these non-uniformities to enable corrections to be made at the pre-processing stage. The use of a pure white reference object in each scene, or better still a uniformgrayscale, can be of great benefit in correcting for non-linearity between the red, green and bluechannels as well as correcting for the non-linear reproduction of intensity by the system. In fact, Hallfound that It is not sufficient to simply have a reference white and black in the image for calibrationpurposes, as this would assume a linear relationship for all shades of grey in between'.


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Department : ELECTRONICS& COMMUNICATION ENGINEERING Unit : V Date :Topic name : Pseudo & Full Color Image Processing No. of marks allotted by JNTUK :


Such grayscale non-linearity is inherent in all imaging systems. Calibration can also be takenfurther, to ensure the correct reproduction of color as well as intensity. Frey and Palus, othersconsidered the measurement of a color in a digital image processing system and explained a methodfor calibrating such a system. In particular, they state that grayscale linearization of each of the threechannels, R, G and B, is not enough to allow the system to reproduce colors or hues correctly. Afurther step of linearization must be performed over the three channels together. This ensures thata pure red object which is twice as bright as another object of the exact same red hue is representedas being twice as bright in the red channel only, rather than becoming marginally brighter in the red,green and blue channels for example. For this stage, a color look-up table must be created and usedfor each digitization.

Pseudo Color Image Processing: A pseudo-color image is derived from a grayscale image by mapping each pixel value to a

color according to a table or function. A familiar example is the encoding of altitude usinghypsometric tints in physical relief maps, where negative values are usually represented by shades of blue and positive values by greens and browns. Pseudo-coloring can make some details more visible,by increasing the distance in color space between successive gray levels. Pseudo-coloring can beused to store the results of image elaboration; that is, changing the colors in order to ease

understanding the image. Alternatively, depending on the table or function used, pseudo-coloringmay increase the information contents of the original image, for example adding geographicinformation, combining information obtained from infra-red or ultra-violet light, or MRI scans.

Pseudo-color images differ from false-color images in that they are made from only oneoriginal gray-scale image, rather than two or three.

False-color and pseudo-color images are frequently used for viewing satellite images, such asfrom weather satellites, the Hubble Space Telescope, and the Cassini-Huygens space probe's imagesof the rings of Saturn. Infrared cameras used for thermal imaging often show their image in falsecolors.Full Color Image Processing:

There are internally three processes. They are color transformation , color complements andcolor slicing transformation .Color Transformation:

Use to transform colors to colors.Formulation:


[ ]),(),( y x f T y xg =

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Department : ELECTRONICS& COMMU

Unit : V Topic name : Full Color Image Proc

Books referred : 01. Digital Image P02. www.wikipedi03. www.google.c

f(x,y) = input color image,neighborhood of (x,y).

When only data at onetransformation as:

Where, r i = color componesi = color compone

Note: For RGB images, n = 3.Color Complements:

Color complement replacomponent. This operation is analo

Color Slicing Transformation:We can perform “slicing” i

more than threshold distance, wekeep the original color unchanged.

Faculty/Date:

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LTEACHING NOTES

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Department : ELECTRONICS& COMMUNICATION ENGINEERING Unit : V Date :Topic name : RGB Color Model No. of marks allotted by JNTUK :


RGB Color Model:


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Digital Image Processing Unit-5.pdf

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