Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
Gerger, A; Pompl, R; Smolle, J; Wilhelm Stolz.
Automated epiluminescence microscopy--tissue counter analysis using CART and 1-NN in the diagnosis of Melanoma.
SKIN RES TECHNOL 2003 9: 105-110.
Doi: 10.1034/j.1600-0846.2003.00028.x
Web of Science
PubMed
FullText
FullText_MUG
- Führende Autor*innen der Med Uni Graz
- Gerger Armin
- Smolle Josef
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- Abstract:
- BACKGROUND/PURPOSE: In tissue counter analysis, digital images are overlayed with regularly distributed measuring masks (elements) of equal size and shape, and the digital contents (grey level, colour and texture parameters) of each element are used for statistical analysis. In this study we assessed the applicability of tissue counter analysis and machine learning algorithms on tumour segmentation and diagnostic discrimination of benign and malignant melanocytic skin lesions. METHODS: A total of 369 standardised dermatoscopic images (93 melanomas, 276 benign nevi) were evaluated. The Classification and Regression Tree (CART) analysis was performed in order to differentiate between melanocytic skin lesions and surrounding skin. Instance-based learning (1-NN) was tested for differentiating between benign and malignant tumour elements. For diagnostic assessment, only the percentage of elements suggestive for malignancy in each lesion was used. RESULTS: Evaluation of a total of 369 melanocytic skin lesions showed a suitable segmentation of the tumour portion in 97.6%. When instance-based learning was applied to an independent test set, a threshold value of 27.4% of elements suggestive for malignancy recognised 35 out of 35 melanomas and 100 out of 101 nevi (sensitivity 100%, specificity 99%, positive predictive value 97.2%, negative predictive value 100%). CONCLUSION: Tissue counter analysis combined with machine learning algorithms turned out to be a useful method for diagnostic purposes in epiluminescence microscopy.
- Find related publications in this database (using NLM MeSH Indexing)
- Algorithms -
- Artificial Intelligence -
- Diagnosis, Differential -
- Humans -
- Image Interpretation, Computer-Assisted -
- Melanoma - pathology
- Microscopy, Fluorescence - methods
- Microscopy, Video - methods
- Nevus, Pigmented - pathology
- Regression Analysis - pathology
- Skin Neoplasms - pathology
- Find related publications in this database (Keywords)
- tissue counter analysis
- automated epiluminescence microscopy
- segmentation
- classification
- machine learning
- melanoma