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Background/purpose: In vivo confocal laser scanning microscopy (CLSM) represents a novel imaging tool that allows the non-invasive examination of skin cancer morphology at a quasi histological resolution without biopsy. Previous studies dealt with the search for diagnostic, but subjective visual criteria. In this study we examined the correlation between objectively reproducible image-analysis features und visual morphology in melanocytic skin tumours using CLSM., Methods: Eight hundred and fifty-seven CLSM tumour images including 408 benign nevi and 449 melanoma images were evaluated. Image analysis was based on features of the wavelet transform and classification tree analysis (CART) was used for classification purposes. In a second step, morphologic details of CLSM images, which have turned out to be of diagnostic significance by the classification algorithm were evaluated., Results: CART analysis of the whole set of CLSM images correctly classified 97.55% of all melanoma images and 96.32% of all nevi images. Seven classification tree nodes seemed to indicate benign nevi, whereas six nodes were suggestive for melanoma morphology. The visual examination of selected nodes demonstrated that monomorphic melanocytic cells and melanocytic cell nests are characteristic for benign nevi whereas polymorphic melanocytic cells, disarray of melanocytic architecture and poorly defined or absent keratinocyte cell borders are characteristic for melanoma., Conclusion: Well-known, but subjective CLSM criteria could be objectively reproduced by image analysis features and classification tree analysis. Moreover, features not accessible to the human eye seem to contribute to classification success.

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.

Background: Melanoma and nonmelanoma skin cancer are the most frequent malignant tumors by far among whites. Currently, early diagnosis is the most efficient method for preventing a fatal outcome. In vivo confocal laser-scanning microscopy (CLSM) is a recently developed potential diagnostic tool., Methods: One hundred seventeen melanocytic skin lesions and 45 nonmelanocytic skin lesions (90 benign nevi, 27 malignant melanomas, 15 basal cell carcinomas, and 30 seborrheic keratoses) were sampled consecutively and were examined using proprietary CLSM equipment. Stored images were rated by 4 independent observers., Results: Differentiation between melanoma and all other lesions based solely on CLSM examination was achieved with a positive predictive value of 94.22%. Malignant lesions (melanoma and basal cell carcinoma) as a group were diagnosed with a positive predictive value of 96.34%. Assessment of distinct CLSM features showed a strong interobserver correlation (kappa >0.80 for 11 of 13 criteria). Classification and regression tree analysis yielded a 3-step algorithm based on only 3 criteria, facilitating a correct classification in 96.30% of melanomas, 98.89% of benign nevi, and 100% of basal cell carcinomas and seborrheic keratoses., Conclusions: In vivo CLSM examination appeared to be a promising method for the noninvasive assessment of melanoma and nonmelanoma skin tumors., (Copyright 2006 American Cancer Society.)

Background: Tissue counter analysis is an image analysis tool designed for the detection of structures in complex images at the macroscopic or microscopic scale. As a basic principle, small square or circular measuring masks are randomly placed across the image and image analysis parameters are obtained for each mask. Based on learning sets, statistical classification procedures are generated which facilitate an automated classification of new data sets., Objective: To evaluate the influence of the size and shape of the measuring masks as well as the importance of feature selection, statistical procedures and technical preparation of slides on the performance of tissue counter analysis in microscopic images. As main quality measure of the final classification procedure, the percentage of elements that were correctly classified was used., Study Design: HE-stained slides of 25 primary cutaneous melanomas were evaluated by tissue counter analysis for the recognition of melanoma elements (section area occupied by tumour cells) in contrast to other tissue elements and background elements. Circular and square measuring masks, various subsets of image analysis features and classification and regression trees compared with linear discriminant analysis as statistical alternatives were used. The percentage of elements that were correctly classified by the various classification procedures was assessed. In order to evaluate the applicability to slides obtained from different laboratories, the best procedure was automatically applied in a test set of another 50 cases of primary melanoma derived from the same laboratory as the learning set and two test sets of 20 cases each derived from two different laboratories, and the measurements of melanoma area in these cases were compared with conventional assessment of vertical tumour thickness., Results: Square measuring masks were slightly superior to circular masks, and larger masks (64 or 128 pixels in diameter) were superior to smaller masks (8 to 32 pixels in diameter). As far as the subsets of image analysis features were concerned, colour features were superior to densitometric and Haralick texture features. Statistical moments of the grey level distribution were of least significance. CART (classification and regression tree) analysis turned out to be superior to linear discriminant analysis. In the best setting, 95% of melanoma tissue elements were correctly recognized. Automated measurement of melanoma area in the independent test sets yielded a correlation of r=0.846 with vertical tumour thickness (p<0.001), similar to the relationship reported for manual measurements. The test sets obtained from different laboratories yielded comparable results., Conclusions: Large, square measuring masks, colour features and CART analysis provide a useful setting for the automated measurement of melanoma tissue in tissue counter analysis, which can also be used for slides derived from different laboratories.