Digital Image Processing Unit-5

Page 1 of 7

LCE/7.5.1/RC 01

TEACHING 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 Woods

02. 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 processing

has significant potential, since the analysis and comparison of color images is a task which humans

find 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. They

are the assessment of the healing of skin wounds or ulcers, and the diagnosis of pigmented skin

lesions such as melanomas. The analysis of lesions involves more traditional image processing

techniques 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 is

necessary to detect the wound border and to calculate its area, analysis of the colors within the

wound 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 an

important 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 a

skin lesion should be tested further, and analysis using color image processing could provide

additional 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 storage

on computer. One problem of storage is that image files can be very large. However this can be

reduced to some extent by use of data compression techniques without significantly reducing the

information content or quality of an image. One of the groups reviewed found that dermatologists

were able to diagnose lesions with compressed digital images without significant change from their

performance with the original digital image.

Color Image Processing:

There are relatively few research groups around the world involved in color image

processing of wounds or lesions. Fewer still have experimented with techniques for assessing skin

wounds using color image processing. Herbin at the Department de Biostatistiques et Informatique

Medicale, Hopital Cochin, Paris, France analyzed RGB color images digitized from Kodachrome color

slides of wounds, in order to quantitatively assess wound healing kinetics. They studied artificially

created blister wounds on the forearms of eight volunteers over twelve days. The wounds were

photographed with a 2mm white paper disk placed adjacent to the wound site, which served as both

a color and geometric reference. Each digitized slide image was corrected, using the white reference

patch. They evaluated a simple color index of healing for these uniformly colored wounds and used

an automated approach to determine the wound area.

Faculty/Date: HOD/Date:

www.jntuworld.com

Page 2 of 7

LCE/7.5.1/RC 01

TEACHING NOTES


Unit: V Date:




03. www.google.com

Although they have tackled the problem of automating wound analysis, their method was

not as complex as would be necessary for the analysis of natural wounds which have a highly

variegated coloring. Another group, Arnqvist at the Department of Scientific Computing, University

of Uppsala, Sweden, experimented with a method for the semi-automatic classification of secondary

healing ulcers. Color photographs were acquired with a 35mm still camera with ring flash. The

photographs were then digitized into a 24-bit RGB image. Photographs were taken at an optimal

angle of thirty degrees to the wound plane normal in order to reduce reflections from the flash. In

each 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, red

granulation 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 a

skilled 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 been

created 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 segmentation

depending 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 an

overall 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. This

enabled phvolume measurements to be made. A color CCD video camera with a 250 W tungsten

halogen bulb was used for imaging the skin directly. MAVIS is capable of measuring the area phand

volume of deep three-dimensional wounds. For each acquisition, a magnesium oxide chip, placed

alongside the wound, was used as a white standard. The group has experimented with algorithms

that use color to segment an image into one of three tissue types: healthy skin, wound tissue and

epithelialisation tissue. They found that epithelialisation tissue is often a darkened band around the

wound, separating skin from wound. In all, they tried six measurement parameters: the R, G, and B

intensities; Hue; Saturation; and gray-level intensity. The R, G and B intensities were only examined

in isolation and they concluded that, `It is clear from inspection of Red, Green and Blue plane

intensity-level histograms for the different tissue types that straightforward thresholding of these

planes cannot produce a good segmentation which distinguishes between wound and skin or wound

and surrounding connected tissue'.


www.jntuworld.com

Page 3 of 7

LCE/7.5.1/RC 01

TEACHING NOTES


Unit: V Date:




03. www.google.com

They conclude that in looking at such 1D histograms, segmentation is only partially

achievable, 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 clustering

technique. 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 the

enormous number of wound care materials that have been introduced into the market have not

been properly tested in randomized, double-blind clinical trials. They pointed out that such trials are

desperately needed to supply clinicians with information to guide them in their choice of wound

care products. They compared a degrading product with an old form of treatment using saline

soaked 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 black

to yellow necrotic tissue to red granulation tissue. Their aim is to create an automatic computerized

method which can be used as a reference standard or `gold-standard' for color wound analysis. In

their system, images were acquired directly with an RGB video camera and frame grabber. They

used two polarized filters to reduce unwanted reflections. A clinician’s knowledge of the colors in

secondary healing ulcers was used for calibration of the system. The computer had to be instructed

in advance as to which colors can be encountered in the granulation region and which in the necrotic

region 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 to

segment the image into these three tissue types. For this reason, large classification tables, of the

colors 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 wound

margins automatically, the color differences between granulation tissue, surrounding skin and the

thin partly transparent layer of newly formed epithelium were too small to allow automatic

detection.

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 short

paper on the use of the black-yellow-red classification scheme to evaluate the debridement activity

of wounds. Solomon at the University of Otego Medical School, New Zealand developed a simple

and rapid technique to measure the size of skin wounds and ulceration using two-dimensional color

video 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 method

to correct for limb convexity was presented. Smith at the University of Akron, Ohio, USA, evaluated

wound 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.


www.jntuworld.com

Page 4 of 7

LCE/7.5.1/RC 01

TEACHING NOTES


Unit: V Date:

Topic name: Image Acquisition, Digitization & Calibration No. of marks allotted by JNTUK:



03. www.google.com

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 the

slides need to be digitized and for this a color slide scanner can give a very high spatial resolution, up

to 2,700 dots per inch. Such a scanner can capture 95% of the information in a high resolution color

slide. 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 easily

be used outside the laboratory or clinic, in the patient's home for instance. Care must be taken with

the exposure setting on the camera. In considering just grayscale images, Hall discovered that

different exposures of the film have a significant effect on the histogram of the image. This has many

repercussions 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 not

have 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. Color

resolution 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 takes

place 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 the

system to be portable, such a system tends to be less versatile than using a 35mm still camera. This

renders imaging outside the laboratory less suitable. The best solution would be to use a phdigital

still camera, but these are still fairly new on the market and rather expensive. Still, costs are

gradually falling and so they are becoming a viable option. They are quick, as no photographic

processing 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 often

aim 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 acquisition

system due to the non-linear response of electronic devices and non-uniform lighting, methods can

be 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 uniform

grayscale, can be of great benefit in correcting for non-linearity between the red, green and blue

channels as well as correcting for the non-linear reproduction of intensity by the system. In fact, Hall

found that It is not sufficient to simply have a reference white and black in the image for calibration

purposes, as this would assume a linear relationship for all shades of grey in between'.


www.jntuworld.com

Page 5 of 7

LCE/7.5.1/RC 01

TEACHING NOTES


Unit: V Date:

Topic name: Pseudo & Full Color Image Processing No. of marks allotted by JNTUK:



03. www.google.com

Such grayscale non-linearity is inherent in all imaging systems. Calibration can also be taken

further, to ensure the correct reproduction of color as well as intensity. Frey and Palus, others

considered the measurement of a color in a digital image processing system and explained a method

for calibrating such a system. In particular, they state that grayscale linearization of each of the three

channels, R, G and B, is not enough to allow the system to reproduce colors or hues correctly. A

further step of linearization must be performed over the three channels together. This ensures that

a pure red object which is twice as bright as another object of the exact same red hue is represented

as 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 used

for 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 using

hypsometric 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 be

used 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-coloring

may increase the information contents of the original image, for example adding geographic

information, 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 one

original gray-scale image, rather than two or three.

False-color and pseudo-color images are frequently used for viewing satellite images, such as

from weather satellites, the Hubble Space Telescope, and the Cassini-Huygens space probe's images

of the rings of Saturn. Infrared cameras used for thermal imaging often show their image in false

colors.

Full Color Image Processing:

There are internally three processes. They are color transformation, color complements and

color slicing transformation.

Color Transformation:

Use to transform colors to colors.

Formulation:


[ ]),(),( yxfTyxg =

www.jntuworld.com

Department: ELECTRONICS & COMMUNICATION

Unit: V

Topic name: Full Color Image Proce



03. www.google.com

f(x,y) = input color image, g(x,y) = output color image

neighborhood of (x,y).

When only data at one pixel is used in the transformation, we

transformation as:

Where, ri = color component of f(x,y)

si = color component of g(x,y)

Note: For RGB images, n = 3.

Color Complements:

Color complement replaces each color with its opposite color in the

component. This operation is analogous to

Color Slicing Transformation:

We can perform “slicing” in color space: if the color of each pixel

more than threshold distance, we set that

keep the original color unchanged.

Faculty/Date:

i Ts =

Page 6 of 7

LCE/7.5.1/RC 01

TEACHING NOTES

OMMUNICATION ENGINEERING

Date:

essing No. of marks allotted by JNTUK

: 01. Digital Image Processing by R C Gonzalez and R E Woods

www.wikipedia.org

www.google.com

f(x,y) = input color image, g(x,y) = output color image and T = operation on f over a spatial

When only data at one pixel is used in the transformation, we can express the

i = 1, 2,… n

= color component of f(x,y)

component of g(x,y)

Color complement replaces each color with its opposite color in the color circle of the Hue

component. This operation is analogous to image negative in a gray scale image.

rm “slicing” in color space: if the color of each pixel is far from a desired color

more than threshold distance, we set that color to some specific color such as gray, otherwise we

HOD/Date:

),,,(21 ni rrrT K

LCE/7.5.1/RC 01

No. of marks allotted by JNTUK:

T = operation on f over a spatial

can express the

color circle of the Hue

is far from a desired color

color to some specific color such as gray, otherwise we

www.jntuworld.com

Page 7 of 7

LCE/7.5.1/RC 01

TEACHING NOTES


Unit: V Date:

Topic name: RGB Color Model No. of marks allotted by JNTUK:



03. www.google.com

RGB Color Model:


www.jntuworld.com

Documents

Digital Image Processing Unit-5