Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Koller, S; Wiltgen, M; Ahlgrimm-Siess, V; Weger, W; Hofmann-Wellenhof, R; Richtig, E; Smolle, J; Gerger, A.
In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours.
J Eur Acad Dermatol Venereol. 2011; 25(5):554-558
Doi: 10.1111/j.1468-3083.2010.03834.x
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- Führende Autor*innen der Med Uni Graz
- Gerger Armin
- Koller Silvia Eleonore
- Co-Autor*innen der Med Uni Graz
- Ahlgrimm-Siess Verena
- Hofmann-Wellenhof Rainer
- Richtig Erika
- Smolle Josef
- Weger Wolfgang
- Wiltgen Marco
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- Abstract:
- Background In vivo reflectance confocal microscopy (RCM) has been shown to be a valuable imaging tool in the diagnosis of melanocytic skin tumours. However, diagnostic image analysis performed by automated systems is to date quite rare. Objectives In this study, we investigated the applicability of an automated image analysis system using a machine learning algorithm on diagnostic discrimination of benign and malignant melanocytic skin tumours in RCM. Methods Overall, 16 269 RCM tumour images were evaluated. Image analysis was based on features of the wavelet transform. A learning set of 6147 images was used to establish a classification tree algorithm and an independent test set of 10 122 images was applied to validate the tree model (grouping method 1). Additionally, randomly generated 'new' learning and test sets, tumour images only and different skin layers were evaluated (grouping method 2, 3 and 4). Results The classification tree analysis correctly classified 93.60% of the melanoma and 90.40% of the nevi images of the learning set. When the classification tree was applied to the independent test set 46.71 +/- 19.97% (range 7.81-83.87%) of the tumour images in benign melanocytic skin lesions were classified as 'malignant', in contrast to 55.68 +/- 14.58% (range 30.65-83.59%; t-test: P < 0.036) in malignant melanocytic skin lesions (grouping method 1). Further investigations could not improve the results significantly (grouping method 2, 3 and 4). Conclusions The automated RCM image analysis procedure holds promise for further investigations. However, to date our system cannot be applied to routine skin tumour screening.
- Find related publications in this database (using NLM MeSH Indexing)
- Algorithms -
- Artificial Intelligence -
- Humans -
- Image Interpretation, Computer-Assisted - methods
- Melanoma - pathology
- Microscopy, Confocal - methods
- Nevus, Pigmented - pathology
- Reproducibility of Results -
- Retrospective Studies -
- Skin Neoplasms - pathology
- Find related publications in this database (Keywords)
- image analysis
- melanocytic skin tumours
- reflectance confocal microscopy