Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
Wiltgen, M; Gerger, A; Wagner, C; Bergthaler, P; Smolle, J.
Discrimination of benign common nevi from malignant melanoma lesions by use of features based on spectral properties of the wavelet transform.
ANAL QUANT CYTOL HISTOL 2003 25: 243-253.
Web of Science
PubMed
Google Scholar
- Führende Autor*innen der Med Uni Graz
- Wiltgen Marco
- Co-Autor*innen der Med Uni Graz
- Gerger Armin
- Smolle Josef
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- Abstract:
- OBJECTIVE: To evaluate the possibilities of describing and discriminating common nevi and malignant melanoma tissue with features based on spectral properties of the Daubechies 4 wavelet transform. STUDY DESIGN: Images of common nevi and malignant melanoma were dissected in square elements. The wavelet coefficients were calculated inside the square elements. The diagonal coefficients and related power spectra were used for further analysis. The analysis results served as guide for the selection of features, including standard deviations of wavelet coefficients inside the frequency bands and the energy of the frequency bands. These features describe properties of the frequency bands, representing information on different scales. To test the usefulness of the features for discrimination, a study set of 80 cases was classified by classification and regression trees analysis. The set was divided into a training set and a test set. RESULTS: In the case of benign common nevi, the energies of the lower frequency bands and higher, whereas malignant melanoma tissue shows more variability of the coefficients in higher-frequency bands. The influence on the detail properties of the images was studied by suppression of coefficients with low values, which are concentrated mainly in higher-frequency bands. In the case of benign common nevi the main information is contained in 15% of the coefficients and in the case of malignant melanoma, in 39%. The results of classification show a clear-cut difference between the cases. The classification correctly classified 95.78% of nevi elements and 94.22% of melanoma elements in the training set and 100% of cases of benign nevi and 80% of cases of malignant melanoma in the test set. CONCLUSION: Features based on the wavelet power spectrum contain sufficient information for differentiation between common nevi and malignant melanomas.
- Find related publications in this database (using NLM MeSH Indexing)
- Artificial Intelligence -
- Diagnosis, Computer-Assisted - methods
- Diagnosis, Differential - methods
- Humans - methods
- Image Processing, Computer-Assisted - methods
- Melanoma - diagnosis
- Microscopy - methods
- Nevus - diagnosis
- Predictive Value of Tests - diagnosis
- Sensitivity and Specificity - diagnosis
- Find related publications in this database (Keywords)
- computer-assisted diagnosis
- melanomas and nevi
- tissue counter analysis
- wavelet textural analysis