Tissue counter analysis of benign common nevi and malignant melanoma

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Abstract

Objective: The aim of this study was to evaluate the applicability of tissue counter analysis to the interpretation of skin images. Method: Digital images from microscopic views of benign common nevi and malignant melanoma were classified by the use of features extracted from histogram and co-occurrence matrix. Eighty cases were sampled and split into a training set and a test set. The images were dissected in square elements and the different features were calculated for each element. The classification was done by classification and regression trees (CART) analysis. In the CART procedure, the square elements were split into disjunctive nodes, which were characterized by a relevant subset of the features. The classification results were indicated in the original image in order to evaluate the performance of the procedure. Results: For the learning set and the test set there is a significant difference between benign nevi and malignant melanoma without overlap. Discriminant analysis based on the percentage of ‘malignant elements’ facilitated a correct classification of all cases. Discussion: Since no image segmentation was needed, problems related to this task were avoided. Though wrong classification of individual elements is unavoidable to some degree, tissue counter analysis shows a good discrimination between benign common nevi and malignant melanoma. Conclusion: In conclusion, tissue counter analysis may be a useful method for the interpretation of melanocytic skin tumors.

Introduction

Automatic medical image analysis is successful when the structures, e.g. blood cells, are well separated and can be clearly defined [1], [2], [3]. In histological tissue, the structures are mostly arranged in a variety of patterns and the segmentation of different structures, such as cells, nuclei, cytoplasm, vessels etc., is difficult, case dependent and cannot be done in a general approach. This is one reason why compact tissue structures generally resist a fully automatic analysis [4].

We concentrated our attention on the automatic analysis of benign common nevi and malignant melanoma lesions. In this study, we tested the applicability of tissue counter analysis to the diagnostic discrimination of a set of melanoma and nevi [5]. The images of the test set show a broad variation in patterns in several cases. To enable the analysis of tissue structures, without being limited by the selection and detection of structures of interest, the images are divided into square elements of equal size. In this way, a priori definition and segmentation of the structures, which is the crucial point in automatic classification, was avoided. Features are calculated by extracting the digital information in each square element. The features from all the elements in the set of images were submitted as data set to a classification and regression trees (CART) analysis [6]. By the CART analysis the elements were automatically separated into more or less homogeneous nodes corresponding to different classes of structures.

Section snippets

Materials

All biopsies were routinely embedded in paraffin and cut at a section thickness of 4 μm. The slides were routinely stained with hematoxylin and eosin using a fully automated staining device (DRF, Fakura, Japan). The specimens were randomly sampled from the Dermatopathology files of the Department of Dermatology, University of Graz, Austria. All cases were diagnosed by two pathologists. In total, we sampled 40 biopsies of benign common nevi and 40 biopsies of malignant melanoma. For the study

Method

The tissue counter analysis consists of three steps: the feature extraction, the classification and the verification.

Results

The classification results of the CART analysis are given in Table 3. When all nodes were taken into account for the 40 cases of the learning set, the percentage of tissue elements classified as ‘malignant elements was 7.3±4.9% (range: 1.6–22.7%) for the cases of benign common nevi and 92.1±6.6% (range: 74.6–99.82%) for the cases of malignant melanoma. For the 40 cases of the test set (by considering again all the nodes from the CART tree), the percentage of tissue elements classified as

Discussion

There is an increasing number of melanocytic tumors. In the fight against skin cancer, researchers have high hopes in improved provisional screening methods, such as optimized computer aided diagnostic methods.

The texture features obtained from histogram and co-occurrence matrix for each square element contain sufficient information to facilitate the differentiation between malignant melanoma and benign common nevi elements. In contrast to other methods in medical image analysis no previous

Acknowledgements

This study was supported by the ‘Jubiläumsfond der Österreichischen Nationalbank’, project number 9297.

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