Solution for professional total body mapping photography and automatic mole mapping analysis
The change of nevi is an important factor for the early detection of melanoma in high risk patients. It is often difficult to identify changes or new moles in follow-up examinations of these patients. Changes in moles on the back are difficult to be discovered by the patients themselves.
MoleExpert macro is one of the world's most advanced mole mapping and screening systems for the detection of changes in nevi. A poster of a first version has been presented at the International Skin Cancer Conference 2003 in Zurich.
Two similar follow-up images of a relevant body site for one patient are compared. MoleExpert macro automatically detects nevi in the first and in the follow-up image and computes the mole mapping. Additionally the software extracts features relevant to the size, shape, and brightness of each lesion and compares these features for all mapped nevi. Extracted moles are shown in full resolution providing a detailed image of the nevus.
The software works best with newer digital cameras and an image resolution of 4 megapixels or higher. It also works with a lower image resolution.
Screenshots
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Analysis results
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Lesion selection
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Program Features
Together with the dermacenter Küssnacht (Switzerland) the image analysis software MoleExpert macro was designed as a practical solution for the clinical monitoring of nevi. The starting points for the analysis are always photographed bodily regions of interest (e.g. the back). The body area to be examined is photographed under as identical as possible conditions (distance, angle, lighting) within a time interval of approximately half a year to one year and saved in an image database. When the original and developed images are loaded, the image analysis software will begin. The detection of skin pigmented lesions and organizing of pairs in the two images is automated. After a few moments, the analysis result appears drawn directly in the images.
MoleExpert macro is very well suited for the detection of changes in patients with a large amount of pigmented lesions. With this software, images of the back, the legs and also from the chest or throat regions can be analyzed and subsequent changes highlighted.
The software is designed so that image information even from high resolution pictures can be used, in which the areas of special interest can be displayed greatly enlarged in both images simultaneously using the magnification function.
Experience with the program has so far shown that size change from approximately 10% can be detected in good quality images.
From a technical viewpoint, the software offers the following features:
- automated detection of pigmented lesions in every image,
- automated organization of these lesions into image pairs,
- detection of new moles and changes in existing,
- determination of size and color changes,
- as accurate as possible correction of differences in photo taking conditions (position, distance, rotation, lighting)
Description of Algorithm
Described below is the principle of analysis.
- Recognition and evaluation (score) of pigmented lesions in every image through the use of a knowledge-based system (artificial intelligence)
- Automated selection of conspicuous pigmented lesions (highest score) in both images
- Calculation of possible transformations, taking into account different conditions (position, rotation, distortion), based on the location of the chosen pigmented lesion
- Mapping of pigmented lesions using the transformation
- Correction of parameters for the transformation by the use of the found pigmented pairs
- Repetition steps 4 and 5 until no further improvement is possible
- Concrete mapping and comparison of pigmented lesions
- Presentation of results
Approach for the Analysis of Images
If there is an image database, two images can be transferred directly into MoleExpert macro. The image analysis then begins automatically. Otherwise, images can be loaded under ‘File’ on the main menu. The automatic processing of the images begins when the ‘Analyze’ button is clicked.
First the software searches for all moles in each image, which fulfil a specific criteria of size and conspicuity. The mapping of the corresponding moles from both images follows through the use of a specially developed algorithm. Unchanged lesions are circled in green and changed lesions circled in red. Lesions in which no corresponding lesion could be found will be circled in
yellow.
Scientific Background
An analysis of age distribution in groups of patients with a different melanoma tumor thickness (Breslow) resulted in an age distribution of 7.8 years after comparing the groups ‘Smaller 0.75 mm’ and ‘Larger 3 mm’ tumor thickness [1]. Based on this data, realistic and relevant changes could be recognized. Tsoa et al found an increased risk of skin pigmented lesions in persons aged 40 and over of possessing malignant melanoma [2].
The pigment lesions for this particular group of patients should therefore be strongly controlled.
According to Liu et al one third of melanomas grew 0.5 mm per month or more. The monthly growth rate for the SSM (superficial spreading melanoma) was 0.12 mm, for the LMM (lentigo maligna melanoma) 0.13 mm and 0.49 mm for the NM (nodular melanoma). The ABCD-rule could not be reliably confirmed for fast-growing melanoma [3]. On the basis of this data an observation time frame of 6 months for high-risk patients seems appropriate – for NM however too short.
Bishop et al describe a progressive change in form, size or color of a lesion as important factors in melanoma detection [4]. These characteristics described should thus be taken into account in clinical monitoring.
Distributors
References
[1] Paul E, Pausch A, Bödeker HR (1989) Speed of Growth of Melanoma: Statistical Analysis of the Average Ages of Patient Groups. Pigment Cell Research 2 (6), 475–477.
[2] Tsao H, Bevona C, Goggins W, Quinn T. (2003) The transformation rate of moles (melanocytic nevi) into cutaneous melanoma: a population-based estimate. Arch Dermatol. 2 003 Mar;139(3):282-8.
[3] Wendy Liu, MBChB, PhD; John P. Dowling, MBBS; William K. Murray, MBBS; Grant A. McArthur, MBBS, PhD; John F. Thompson, MBBS, MD; Rory Wolfe, BSc, PhD; John W. Kelly, MBBS, MD (2006) Rate of Growth in Melanomas. Characteristics and Associations of Rapidly Growing Melanomas. Arch Dermatol. 2006;142:1551-155
[4] Bishop JN, Bataille V, Gavin A, Lens M, Marsden J, Mathews T, Wheelhouse C (2007) The prevention, diagnosis, referral and management of melanoma of the skin: concise guidelines. Clin Med. 2007 Jun;7(3):283-90.