Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Gerger, A; Wiltgen, M; Langsenlehner, U; Richtig, E; Horn, M; Weger, W; Ahlgrimm-Siess, V; Hofmann-Wellenhof, R; Samonigg, H; Smolle, J.
Diagnostic image analysis of malignant melanoma in in vivo confocal laser-scanning microscopy: a preliminary study
SKIN RES TECHNOL. 2008; 14(3): 359-363.
Doi: 10.1111/j.1600-0846.2008.00303.x
Web of Science
PubMed
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- Führende Autor*innen der Med Uni Graz
- Gerger Armin
- Co-Autor*innen der Med Uni Graz
- Ahlgrimm-Siess Verena
- Hofmann-Wellenhof Rainer
- Langsenlehner Uwe
- Richtig Erika
- Samonigg Hellmut
- Smolle Josef
- Weger Wolfgang
- Wiltgen Marco
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- Abstract:
- Background/purpose: In this study we assessed the applicability of image analysis and a machine learning algorithm on diagnostic discrimination of benign and malignant melanocytic skin tumours in in vivo confocal laser-scanning microscopy (CLSM). Methods: A total of 857 CLSM tumour images including 408 benign nevi and 449 melanoma images was evaluated. Image analysis was based on features of the wavelet transform. For classification purposes we used a classification tree software (CART). Moreover, automated image analysis results were compared with the prediction success of an independent human observer. Results: CART analysis of the whole set of CLSM tumour images correctly classified 97.55% and 96.32% of melanoma and nevi images. In contrast, sensitivity and specificity of 85.52% and 80.15% could be reached by the human observer. When the image set was randomly divided into a learning (67% of the images) and a test set (33% of the images), overall 97.31% and 81.03% of the tumour images in the learning and test set could be classified correctly by the CART procedure. Conclusion: Provided automated decisions can be used as a second opinion. This can be valuable in assisting diagnostic decisions in this new and exciting imaging technique.
- Find related publications in this database (using NLM MeSH Indexing)
- Artificial Intelligence -
- Data Interpretation, Statistical -
- Dermoscopy - methods
- Humans -
- Image Enhancement - methods
- Image Interpretation, Computer-Assisted - methods
- Melanoma - pathology
- Microscopy, Confocal - methods
- Pattern Recognition, Automated - methods
- Pilot Projects -
- Reproducibility of Results -
- Sensitivity and Specificity -
- Skin Neoplasms - pathology
- Find related publications in this database (Keywords)
- confocal microscopy
- image analysis
- classification procedure
- melanocytic skin tumours