Automatic Quantitative Analysis of Healing Skin Wounds using Colour Digital Image Processing

Abstract

This article reviews the recently published research of image processing laboratories involved in colour image processing applied to skin wounds and lesions. The lack of non-invasive methods to evaluate wound repair is a major obstacle in acquiring quantitative data in clinical trials. The role of colour image processing as the most acceptable automatic method of objectively and reproducibly analysing skin lesions and wounds is explored. Image acquisition, digitisation and the use of various image processing algorithms in the analysis of wounds and lesions have been studied in this review. In conclusion, there is considerable scope for further research and development in this field.

Introduction

Colour image processing has many advantages over human assessment of wounds and skin lessions; digital image processing techniques are objective and reproducible. Colour image processing has significant potential, since the analysis and comparison of colour images is a task which humans find particularly difficult. With the current technological trends in computer hardware and scanners, computerised systems are becoming increasingly affordable.

There are two main applications of colour 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 colour 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 colours 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 colour image processing could provide additional information to aid such decisions. A very general review on digital imaging has been written by Perednia et al. [1] . 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.

Colour Image Processing

There are relatively few research groups around the world involved in colour image processing of wounds or lesions. Fewer still have experimented with techniques for assessing skin wounds using colour image processing. Herbin et al. [2] [3] at the Department de Biostatistiques et Informatique Medicale, Hopital Cochin, Paris, France analysed RGB colour images digitised from Kodachrome colour 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 colour and geometric reference. Each digitised slide image was corrected, using the white reference patch. They evaluated a simple colour index of healing for these uniformly coloured wounds and used an automated approach to determine the wound area. 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 colouring. Another group, Arnqvist et al.[4] 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 digitised 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 eschar, yellow necrosis/fibrin (or slough), 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 (ROI). 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 [5] [6], 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 colour-coded structured light. This enabled phvolume measurements to be made. A colour 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 have experimented with algorithms that use colour 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'. 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 colour histogram clustering technique. Mekkes et al.[7] at the Department of Dermatology, University of Amsterdam, The Netherlands, have been using colour 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 debriding product (Intrasite Gel) with an old form of treatment using saline soaked gauzes [8]. They found that for a proper evaluation of the cleansing effect of both treatments, colour 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 computerised method which can be used as a reference standard or `gold-standard' for colour wound analysis. In their system, images were acquired directly with an RGB video camera and framegrabber. They used two polarised filters to reduce unwanted reflections. A clinicians knowledge of the colours in secondary healing ulcers was used for calibration of the system. The computer had to be instructed in advance as to which colours 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 colours 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 colour 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 colour image processing of wounds. El Gammal et al. [9] 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 et al. [10] at the University of Otago Medical School, New Zealand developed a simple and rapid technique to measure the size of skin wounds and ulceration using two-dimensional colour video images of ulcers. The images were stored on video cassette, thus rendering low image quality. The work did not involve the development of colour image processing algorithms, but a novel method to correct for limb convexity was presented. Smith et al.[11] 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 colour image processing techniques were applied to the digitised images.

Image Acquisition, Digitisation and Calibration

Some groups used photography to capture the original wound image, others decided to use a video camera and framegrabber for direct image capture and digitisation. 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 digitised and for this a colour 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 colour 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 greyscale images, Hall et al. [12] 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.

Framegrabbers, the digitiser boards in computers that connect to the video camera, do not have such high resolution. Typically framegrabbers digitise images to only 512 pixels by 512 pixels, and resolution does not meet up to the standards of photography or colour slide scanners. Colour resolution is also inferior for framegrabbers, typically a colour framegrabber has a colour resolution of 24-bits, corresponding to 16 million colours. Their advantage is that digitisation 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, digitisation 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 greyscale, can be of great benefit in correcting for non-linearities between the red, green and blue channels as well as correcting for the non-linear reproduction of intensity by the system. In fact, Hall et al. [12] 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'. Such greyscale non-linearities are inherent in all imaging systems. Calibration can also be taken further, to ensure the correct reproduction of colour as well as intensity. Frey and Palus [13]. Others considered the measurement of a colour in a digital image processing system and explained a method for calibrating such a system. In particular, they state that greyscale linearisation of each of the three channels, R, G and B, is not enough to allow the system to reproduce colours or hues correctly. A further step of linearisation 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 colour look-up table (LUT) must be created and used for each digitisation.

Conclusion

In conclusion it is found that for wound imaging in particular, the image processing functions and algorithms needed are not simple compared with the functions provided by the majority of image processing packages used by research laboratories and industry. Wound imaging requires the use of colour; effective results cannot be achieved with grey-scale images. This is not so true for lesion imaging though, and the use of greyscale processing algorithms is sometimes sufficient. Industry has yet to catch up with the latest colour image processing algorithms currently being developed by researchers. A lot of commercial image processing software packages have colour image processing operations/algorithms that are simply greyscale algorithms applied to the Red, Green and Blue signals separately. However, it is important to treat a colour pixel as a whole, and not to separate it into its three constituent components, in order to make full use of all the information available in a colour image resulting in a more successful algorithm.

Acknowledgement

The authors gratefully acknowledge the supply of the wound images by, and helpful discussions with, P.J.Phillips and Dr S.Thomas of the Surgical Materials Testing Laboratory, Bridgend General Hospital, Wales.

References

1. D A Perednia, What dermatologists should know about digital imaging, Journal of the American Academy of Dermatology, vol. 25, no. 1, pp. 89-108, 1991. PubMed Abstract:http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=1880259&form=6&db=m&Dopt=r
2. M Herbin, F X Bon, A Venot, F Jeanlouis, M L Dubertret, L Dubertret, and G Strauch, Assessment of healing kinetics through true color image processing, IEEE Transactions on Medical Imaging, vol. 12, no. 1, pp. 39-43, March 1993.
3. M Herbin, A Venot, J Y Devaux, and C Piette, Colour quantitation through image processing in skin, IEEE Transactions on Medical Imaging, vol. 9, no. 3, pp. 262-269, 1990.
4. J Arnqvist, L Hellgren, and J Vincent, Semiautomatic classification of secondary healing ulcers in multispectral images, in Proceedings of 9th International Conference on Pattern Recognition, Rome, November 1988, pp. 459-461.
5. B F Jones and P Plassman, An instrument to measure the dimensions of skin wounds, IEEE Transactions on Biomedical Engineering, vol. 42, no. 5, pp. 464-470, 1995.
6. T D Jones, Semi-automatic segmentation algorithms for measuring the area of skin wounds, University of Glamorgan, Department of Computer Studies, Computer Studies Technical Report CS-94-3, 1994.
7. J R Mekkes and W Westerhof, Image processing in the study of wound healing, Clinics in Dermatology, vol. 13, no. 4, pp. 401-407, 1995. PubMed Abstract: http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=8665449&form=6&db=m&Dopt=r
8. van Riet Paap, J R Mekkes, O Estervez, and W Westerhof, A new color video image analysis system for the objective assessment of wound healing in secondary healing ulcers, Wounds, vol. 3, no. 1, pp. 41-41, 1991.
9. S el Gammal and R Popp, A color image analysis system (cd-cwa) to quantify wound healing of ulcers, Skin Research and Technology, vol. 1, no. 3, pp. 158, 1995.
10. C Solomon, A R Munro, A M van Rij, and R Christie, The use of video image analysis for the measurement of venous ulcers, British Journal of Dermatology, vol. 133, pp. 565-570, 1995. PubMed Abstract:http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=7577585&form=6&db=m&Dopt=r
11. D J Smith, S Bhat, and J P Bulgrin, Video image analysis of wound repair, Wounds, vol. 4, no. 1, pp. 6-15, 1992.
12. P N Hall, E Claridge, and J D M Smith, Computer screening for early detection of melanoma - is there a future?, British Journal of Dermatology, vol. 132, pp. 325-338, 1995. PubMed Abstract:http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=7718448&form=6&db=m&Dopt=r
13. H Frey and H Palus, Sensor calibration for video-colorimetry, in Proceedings on Workshop on Design Methodologies for Microelectronics and Signal Processing, Gliwice, Cracow, Poland, October 1993, pp. 109-113.

NEW WITA WEBSITE LAUNCHED

Wound Assessment and Care Management Software launch October 1^st 2009. Please visit www.woundassessment.co.uk for more information and sign up to the website to receive of latest offers.

To celebrate our new WITA website launch we have decided to share the joy with you! Simply sign up to our website, register your interest receive up to 50% off if you decide to purchase WITA! Our little way of saying thank you!

Wound Image Analysis Article

Researching the internet I came across avery interesting article that reviews the recently published research of image processing laboratories involved in colour image processing applied to skin wounds and lesions. The lack of non-invasive methods to evaluate wound repair is a major obstacle in acquiring quantitative data in clinical trials. The role of colour image processing as the most acceptable automatic method of objectively and reproducibly analysing skin lesions and wounds is explored. Image acquisition, digitisation and the use of various image processing algorithms in the analysis of wounds and lesions have been studied in this review. In conclusion, there is considerable scope for further research and development in this field.

Our wound care solution is next generation in wound management. WITA (www.wita-imagocare.co.uk)

Introduction

Colour image processing has many advantages over human assessment of wounds and skin lessions; digital image processing techniques are objective and reproducible. Colour image processing has significant potential, since the analysis and comparison of colour images is a task which humans find particularly difficult. With the current technological trends in computer hardware and scanners, computerised systems are becoming increasingly affordable.

There are two main applications of colour 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 colour 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 colours 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 colour image processing could provide additional information to aid such decisions. A very general review on digital imaging has been written by Perednia et al. [1] . 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.

Colour Image Processing

There are relatively few research groups around the world involved in colour image processing of wounds or lesions. Fewer still have experimented with techniques for assessing skin wounds using colour image processing. Herbin et al. [2] [3] at the Department de Biostatistiques et Informatique Medicale, Hopital Cochin, Paris, France analysed RGB colour images digitised from Kodachrome colour 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 colour and geometric reference. Each digitised slide image was corrected, using the white reference patch. They evaluated a simple colour index of healing for these uniformly coloured wounds and used an automated approach to determine the wound area. 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 colouring. Another group, Arnqvist et al.[4] 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 digitised 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 eschar, yellow necrosis/fibrin (or slough), 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 (ROI). 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 [5] [6], 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 colour-coded structured light. This enabled phvolume measurements to be made. A colour 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 have experimented with algorithms that use colour 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'. 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 colour histogram clustering technique. Mekkes et al.[7] at the Department of Dermatology, University of Amsterdam, The Netherlands, have been using colour 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 debriding product (Intrasite Gel) with an old form of treatment using saline soaked gauzes [8]. They found that for a proper evaluation of the cleansing effect of both treatments, colour 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 computerised method which can be used as a reference standard or `gold-standard' for colour wound analysis. In their system, images were acquired directly with an RGB video camera and framegrabber. They used two polarised filters to reduce unwanted reflections. A clinicians knowledge of the colours in secondary healing ulcers was used for calibration of the system. The computer had to be instructed in advance as to which colours 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 colours 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 colour 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 colour image processing of wounds. El Gammal et al. [9] 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 et al. [10] at the University of Otago Medical School, New Zealand developed a simple and rapid technique to measure the size of skin wounds and ulceration using two-dimensional colour video images of ulcers. The images were stored on video cassette, thus rendering low image quality. The work did not involve the development of colour image processing algorithms, but a novel method to correct for limb convexity was presented. Smith et al.[11] 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 colour image processing techniques were applied to the digitised images.

Image Acquisition, Digitisation and Calibration

Some groups used photography to capture the original wound image, others decided to use a video camera and framegrabber for direct image capture and digitisation. 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 digitised and for this a colour 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 colour 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 greyscale images, Hall et al. [12] 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.

Framegrabbers, the digitiser boards in computers that connect to the video camera, do not have such high resolution. Typically framegrabbers digitise images to only 512 pixels by 512 pixels, and resolution does not meet up to the standards of photography or colour slide scanners. Colour resolution is also inferior for framegrabbers, typically a colour framegrabber has a colour resolution of 24-bits, corresponding to 16 million colours. Their advantage is that digitisation 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, digitisation 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 greyscale, can be of great benefit in correcting for non-linearities between the red, green and blue channels as well as correcting for the non-linear reproduction of intensity by the system. In fact, Hall et al. [12] 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'. Such greyscale non-linearities are inherent in all imaging systems. Calibration can also be taken further, to ensure the correct reproduction of colour as well as intensity. Frey and Palus [13]. Others considered the measurement of a colour in a digital image processing system and explained a method for calibrating such a system. In particular, they state that greyscale linearisation of each of the three channels, R, G and B, is not enough to allow the system to reproduce colours or hues correctly. A further step of linearisation 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 colour look-up table (LUT) must be created and used for each digitisation.

Conclusion

In conclusion it is found that for wound imaging in particular, the image processing functions and algorithms needed are not simple compared with the functions provided by the majority of image processing packages used by research laboratories and industry. Wound imaging requires the use of colour; effective results cannot be achieved with grey-scale images. This is not so true for lesion imaging though, and the use of greyscale processing algorithms is sometimes sufficient. Industry has yet to catch up with the latest colour image processing algorithms currently being developed by researchers. A lot of commercial image processing software packages have colour image processing operations/algorithms that are simply greyscale algorithms applied to the Red, Green and Blue signals separately. However, it is important to treat a colour pixel as a whole, and not to separate it into its three constituent components, in order to make full use of all the information available in a colour image resulting in a more successful algorithm.

Acknowledgement

The authors gratefully acknowledge the supply of the wound images by, and helpful discussions with, P.J.Phillips and Dr S.Thomas of the Surgical Materials Testing Laboratory, Bridgend General Hospital, Wales.

References

1. D A Perednia, What dermatologists should know about digital imaging, Journal of the American Academy of Dermatology, vol. 25, no. 1, pp. 89-108, 1991. PubMed Abstract: http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=1880259&form=6&db=m&Dopt=r
2. M Herbin, F X Bon, A Venot, F Jeanlouis, M L Dubertret, L Dubertret, and G Strauch, Assessment of healing kinetics through true color image processing, IEEE Transactions on Medical Imaging, vol. 12, no. 1, pp. 39-43, March 1993.
3. M Herbin, A Venot, J Y Devaux, and C Piette, Colour quantitation through image processing in skin, IEEE Transactions on Medical Imaging, vol. 9, no. 3, pp. 262-269, 1990.
4. J Arnqvist, L Hellgren, and J Vincent, Semiautomatic classification of secondary healing ulcers in multispectral images, in Proceedings of 9th International Conference on Pattern Recognition, Rome, November 1988, pp. 459-461.
5. B F Jones and P Plassman, An instrument to measure the dimensions of skin wounds, IEEE Transactions on Biomedical Engineering, vol. 42, no. 5, pp. 464-470, 1995.
6. T D Jones, Semi-automatic segmentation algorithms for measuring the area of skin wounds, University of Glamorgan, Department of Computer Studies, Computer Studies Technical Report CS-94-3, 1994.
7. J R Mekkes and W Westerhof, Image processing in the study of wound healing, Clinics in Dermatology, vol. 13, no. 4, pp. 401-407, 1995. PubMed Abstract: http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=8665449&form=6&db=m&Dopt=r
8. van Riet Paap, J R Mekkes, O Estervez, and W Westerhof, A new color video image analysis system for the objective assessment of wound healing in secondary healing ulcers, Wounds, vol. 3, no. 1, pp. 41-41, 1991.
9. S el Gammal and R Popp, A color image analysis system (cd-cwa) to quantify wound healing of ulcers, Skin Research and Technology, vol. 1, no. 3, pp. 158, 1995.
10. C Solomon, A R Munro, A M van Rij, and R Christie, The use of video image analysis for the measurement of venous ulcers, British Journal of Dermatology, vol. 133, pp. 565-570, 1995. PubMed Abstract: http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=7577585&form=6&db=m&Dopt=r
11. D J Smith, S Bhat, and J P Bulgrin, Video image analysis of wound repair, Wounds, vol. 4, no. 1, pp. 6-15, 1992.
12. P N Hall, E Claridge, and J D M Smith, Computer screening for early detection of melanoma - is there a future?, British Journal of Dermatology, vol. 132, pp. 325-338, 1995. PubMed Abstract: http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=7718448&form=6&db=m&Dopt=r
13. H Frey and H Palus, Sensor calibration for video-colorimetry, in Proceedings on Workshop on Design Methodologies for Microelectronics and Signal Processing, Gliwice, Cracow, Poland, October 1993, pp. 109-113.

A meeting with Judy Waterlow

I had the absolute pleasure of meeting and spending an afternoon with Judy Waterlow, the constructor of the Waterlow Pressure Ulcer Risk Assessment Scale.

I started my journey at 6 o'clock in the morning and arrived in Taunton just after midday, where a smiling Judy Waterlow picked me up and took me to her beautiful house. Over lunch Judy, Rupert (and Ranger) and I discussed various different topics before starting our discussion regarding WITA and pressure ulcer risk assessments. It was clear from the start that both Judy and I were on the same wavelength and have similiar goals to achieve.

When I started the process of identifying and procuring a wound management system out on the market I struggled to find one that ticket all the boxes that I would want to use. When I came across Antonic's wound image based tissue analysis tool, I knew I had found something special and could help develop it into a very attractive and compelling wound management solution.

The idea behind WITA is to provide a standardised methodology to wound care, maintain the integrity and accountability of the wound diagnosis and care plan, whilst providing a platform to educate younger nurses and carers around the correct methods to check for pressure ulceration and how to treat specific types ofwounds.

It was a very educational trip for me, as Judy talked me through some of her presentations regarding pressure ulcers and the ways they can be prevented. I feel we both came away thinking we have an ally in each other as we fight for the same goals to improve education and provide a standardise platform for nurse, carer and public development.

Light is Light

After reading about Niels Finsen's 1886 research into the cure of lupus and smallpox through the use of light, I was intrigued to find out how this actually works. I have found an interesting article I would like to share with you regarding the use of red light to help heal chronic wounds and pressure ulcers. To find out more products that can help you with chronic hard to heal wounds please visit www.imagocare.com . For information on how to manage your wound please visit www.wita-imagocare.co.uk.

Light is Light Since Endre Mester and his colleagues first documented the therapeutic benefits of monochromatic light,1,2 a plethora of terminologies and acronyms have emerged and continue to evolve in describing light and laser therapy. Intended or not, each term, old or new, seems to advance the notion that therapeutic lasers and related monochromatic light—often with less than 150 mW cm2 irradiance—are innocuous and safe. Consequently, terms such as “cold laser,” “soft laser,” and “low power lasers,” which were used in the 1970s and 1980s to contrast therapeutic lasers from their high power counterparts, have since metamorphosed into low level laser therapy (LLLT),3–5 low energy laser therapy (LELT),6 low-intensity laser activated biostimulation (LILAB),low-power laser irradiation (LPLI), low power laser therapy (LPLT),8 etc. So much confusion has arisen from describing the same treatment in so many ways that some now resort to using the terms low-level laser therapy, low-energy, cold laser, low power and soft laser concurrently to ensure clarity. During the Cold War era of the 1960s and 1970s, lasers were considered destructive and, in military circles, powerful weapons appropriate for gaining technological advantage over potential enemies. The “Star War” mentality of that period only heightened the awe engendered by lasers. Therefore, the idea that the same tool could be therapeutic at low fluences seemed too far-fetched and remote from prevailing thinking that proponents of lasers as a form of therapy coined soothing terms, such as soft laser and cold laser to project a safe image, foster public acceptance, and distinguish therapeutic lasers from their high power counterparts. As terminologies have evolved, so therapeutic light technology has changed with time. Beginning from the mid-1990s bulky laser devices—mainly gas and dye lasers—were rendered obsolete as semiconductor- and diode-based lasers gained popularity. The evolution continues. Today’s therapeutic light source is as likely to be a superluminous diode (SLD), light emitting diode (LED) or polarized light (PL) as a semiconductor or diode laser, given the increasing number of papers reporting the beneficial effects of SLDs, LEDs and polarized light. This development renders the continued use of LLLT, LELP, LILAB, LPLI, and other terms and acronyms obsolete, and in some instances, inappropriate. A common term or terms recognizing lasers and emerging monochromatic or polarized light sources would seem appropriate to streamline things and minimize the on-going confusion. Therefore, it is recommended that the term “light therapy” or “phototherapy” be used to describe noninvasive interventions involving therapeutic light, as for example, situations involving cutaneous and subcutaneous tissue irradiation for tissue repair, light-based acupuncture, and transcutaneous irradiation for pain relief. The term photomedicine may be appropriate to describe these interventions. However, it broadly covers invasive light therapy such as direct irradiation of stomach ulcer, cardiac infarct, or fracture using a catheter. In addition, the wavelength of light used in each situation should be specified, given the implication of wavelength as an important determinant of treatment outcomes. When multiple wavelengths are used, as for example, treatments involving applicators with “multiarray,” “cluster probes,” and other multi-diode devices, details of the average power of each wavelength of light involved should be reported. Similarly, when multicolor or white polarized light is used, the wavelengths of light encompassed by such devices should be specified in the report. There are additional rationales for these recommendations. First, the terms light therapy, phototherapy and photomedicine are simple. Second, they seem appropriate for every form of light-based treatment—lasers, SLDs, LEDs, polarized light, etc. Third, they streamline therapeutic light lingo, stem the proliferation of acronyms, and minimize confusion. Fourth, there has been a 35year history of safe use of therapeutic lasers. The safety record seems excellent given the dearth of untoward effects in the literature. Therefore, it is no longer necessary to sooth public perception that therapeutic light may be unsafe. Indeed, such perception has diminished significantly; lasers have become an integral part of a wide range of consumer products. Fifth, limiting the terminology to “light therapy,” “phototherapy,” or “photomedicine” further emphasis the notion that light is light. The literature seems clear that the therapeutic effects ascribed to monochromatic light relate more to wavelength and doses than the source of light—lasers, LEDs, SLDs, polarized light, etc. In other words, the source does not have to be a laser in order to produce a therapeutic effect,but rather appropriate doses of light in the range of 600–1000 nm as contemporary reports continue to show.5 Sixth, there is nothing really fancy about being “low,” “soft” or “cold.” Indeed, given the volume of research supporting the clinical benefits of photomedicine, it seems demeaning to be considered “low” or “low level.” Chukuka S. Enwemeka, Ph.D., FACSM Co-Editor-in-Chief

Healing Without the Sting by Keith Cutting

Below I found an interesting article written by Keith Cuttings a Principal lecturer of Tissue Viabilityt highly regarded in the industry. It discusses the use of honey in wound management. For more articles regarding the use of honey in wound management visit www.imagocare.com.

Healing Without the Sting
JCN July 2008

Abstract:

Keith Cutting discusses the renaissance of honey as a therapeutic agent in wound management

Keith F Cutting MN, RN, Dip N, Cert Ed. Principal lecturer Tissue Viability. Buckinghamshire New University

The use of honey in wound care has received increasing attention in recent years, mainly as a result of improved formulations, increased availability, supportive research, clinical efficacy reports and numerous positive reports in the national and international press. These have all contributed to the renaissance of honey as a therapeutic agent, in particular, its use as an antimicrobial agent which neatly confronts the increasing number of reports on the problem of antibiotic resistance.

The therapeutic advantages of honey have been recorded by Molan (2001) and tabulated by Cutting (2007) Table 1.

Good practice dictates that before first application of any honey product the patient should always be assessed for risk of reaction/allergy to bee or honey products and asked about known sensitivities. Adverse effects from application of honey are rare and although Molan (2001) records that patients find honey soothing and non-irritating some clinicians report that a small number of patients may experience a stinging sensation (Vandeputte & Van Waeyenberge 2003). It has been stated that not all honeys are the same and for this reason honey should not be considered a generic term (Molan 2002). Consequently, with the introduction of a new honey product it should not be assumed that painful reactions following application may occasionally be expected.

Melladerm® PLUS is intended for use in all types of wounds, including burns and is derived from Bulgarian (BULGARIA H) mountain flower honey. Bulgaria H has been selected on the basis of its excellent wound healing properties. Melladerm® PLUS is a proprietary wound ointment/gel that contains 45% BULGARIA H and a mixture of ingredients including glycerin and polyethylene glycol 4000 (PEG 4000) to make the honey dressing more user friendly. PEG 4000 is a blend of water soluble polymers and its use as an additive to honey has been assessed by Subrahmanyam (1996).

Clinical evaluation reports prepared as part of a submission for CE marking on Melladerm Plus demonstrate healing without the disadvantage of occasionally incurring pain following application (Vandeputte 2007).

In addition to not inducing pain, a number of clinical advantages to Melladerm PLUS can be found. It has a remarkably rapid debridement capability (Vandeputte 2007). Melladerm PLUS also contains phenolic compounds (most frequently reported for antibacterial and antioxidant activities). Melladerm PLUS has a moisture content of 16.8%, possessing a high osmolarity that promotes a moist environment and permits easy change of dressing. Melladerm PLUS has a low pH (3.4) and it is known that harmful protease are more active when the wound pH is alkaline (Schultz et al 2005).

An additional factor related to low wound pH is the faster release of oxygen from oxyhaemoglobin. Ischaemia is a feature of many chronic wounds and the release of oxygen and subsequent availability assists cellular metabolism and healing (Wilson et al 1979).

The Bulgarian Honey used in Melladerm PLUS is not heat processed. It is filtered to remove contaminants such as pollen that may cause allergic reactions. In order to kill possible anaerobic bacteria in the honey a novel patented process using ozone gas to sterilize the honey is used. This process also destroys fungi and yeasts. This processing does not result in loss of activity of glucose oxidase, an enzyme that is naturally present in honey and is very sensitive to heating. Compared to other types of honey, Bulgarian Honey has a high amount of glucose oxidase and is a rich source of phenolic antioxidants. Phenolics are known to possess antibacterial activity (Schramm et al 2003, Taormina et al 2001).

In order to illustrate the positive benefits of Melladerm PLUS a community tissue viability nurse has recorded the following “Medical honey is an effective wound dressing agent being particularly useful for patients with infected wounds. Unfortunately, some patients experience pain when the honey is initially applied and for some the pain can be so severe that treatment has to be discontinued. With this in mind and with patient consent Melladerm PLUS was applied to a patient with a large chronic leg ulcer. The patient reported no pain on application or during the initial post dressing period.” This account perhaps understates the personal experience of the patient, which, with permission is reported here, “I have never been able to tolerate the pain of any honey based dressing and before now it was so bad I looked like I was dancing out of the door. My wound needed cleaning up and Sister suggested this new Melladerm dressing. I didn’t notice it had been applied, there was no sting and no pain afterwards.”

Melladerm PLUS is available in UK on prescription.

References

• Cutting KF. (2007) Honey and contemporary wound care: an overview. OWM 53,11:49-54
• Molan, P. (2001) Honey as a topical antibacterial agent for treatment of infected wounds. World Wide Wounds. Available at: www.worldwidewounds.com/2001/november/Molan/honey-as-topical-agent.html Accessed May 1st 2008.
• Molan P. (2002) Not all honeys are the same for wound healing. European Tissue Repair Society Bulletin 9,1
• Molan PC. (2001) Why honey is effective as a medicine 2. The scientific explanation of its effects. Bee World 82(1):22–40.
• Schramm DD, Karim M, Schrader HR. (2003) Honey with high levels of antioxidants can provide protection to healthy human subjects. J Agric Food Chem 51: 1732-35
• Schultz G, Ladwig G, Wysocki A. (2005) Extracellular matrix: review of its roles in acute and chronic wounds. World Wide Wounds available at www.worldwidewounds.com/2005/august/Schultz/Extrace-Matric-Acute-Chronic-Wounds.html accessed 20 May 2008.
• Subrahmanyam N. (1996) Addition of antioxidants and polyethylene glycol 4000 enhances the healing property of honey in burns. Annals of Burns and Fire Disasters 9(2):93-95.
• Taormina PJ, Niemira BA, Beuchat LR. (2001) Inhibitory activity of honey against foodborne pathogens as influenced by the presence of hydrogen peroxide and level of antioxidant power. Int J Food Microbiol 69: 217-25
• Vandeputte J, Van Waeyenberge PH (2003) Clinical evaluation of L-Mesitran®, a honey-based wound ointment. European Wound Management Association Journal. 3, 2, 8-11.
• Vandeputte JAJ (2007) Clinical evaluation of Melladerm Plus, Technical file for CE marking.
  Wilson I.A.I et al (1979) The pH of varicose ulcer surfaces and its relationship to healing, Vasa (Bern) 8, 339-342.
  

WITA (Wound Image Tissue Analysis) Analysis, Assessment and Managment Software Solution

Welcome to the WITA WOUND MANAGEMENT Blog,

Over the coming months and years, I will be searching the internet and variety of different internet sources to highlight, in my opinion, the most innovative wound care products in the market, wound care best practice and so that you can follow the development, release and my thoughts on our new WITA (Wound Image Tissue Analysis) Software. Judy Waterlow, leads the commentary on WITA.

"When I first saw WITA I thought Eureeka! It is a very impressive solution. It will encourage & motivate patients and staff to improve wound care best practice. WITA will be the most amazing tool for teaching."

We are almost ready for our first WITA release and are currently looking for wound care distributors to advertise on our unique treatment tool and agents to distribute the software in the UK, EU and US . I will keep you all updated with our progress.

What is WITA?

WITA™ is a sophisticated wound analysis, wound assessment and wound management software solution allowing carers, clinicians, business managers and scholars to quickly and intuitively analyse wounds and manage the ongoing care of patients. To add value to this process and enhance patient recovery, WITA™ will also provide SMART treatments aligned to a hospital’s formulary and even scan the market for alternative treatments.

Its unique image analysis tool takes the visual properties of a wound image and applies an advanced statistical pattern recognition algorithm. The result is a quick analysis of a wound providing accurate tissue and dimension data, which is stored in an interactive wound diary.

For more information please visit www.wita-imagocare.co.uk

Who are Imago Care Ltd?

Imago Care Ltd are a UK based distribution company who procure the most effective products in the health care market to help redefine wound care best practice.

For more information please visit www.imagocare.com