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Comput Methods Programs Biomed. 2015 Feb;118(2):124-33. doi: 10.1016/j.cmpb.2014.12.001. Epub 2014 Dec 9.

Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy.

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Department of Digital Systems, University of Piraeus, Greece. Electronic address:
University of Thessaly, Department of Computer Science and Biomedical Informatics, Greece.


The interest in image dermoscopy has been significantly increased recently and skin lesion images are nowadays routinely acquired for a number of skin disorders. An important finding in the assessment of a skin lesion severity is the existence of dark dots and globules, which are hard to locate and count using existing image software tools. In this work we present a novel methodology for detecting/segmenting and count dark dots and globules from dermoscopy images. Segmentation is performed using a multi-resolution approach based on inverse non-linear diffusion. Subsequently, a number of features are extracted from the segmented dots/globules and their diagnostic value in automatic classification of dermoscopy images of skin lesions into melanoma and non-malignant nevus is evaluated. The proposed algorithm is applied to a number of images with skin lesions with known histo-pathology. Results show that the proposed algorithm is very effective in automatically segmenting dark dots and globules. Furthermore, it was found that the features extracted from the segmented dots/globules can enhance the performance of classification algorithms that discriminate between malignant and benign skin lesions, when they are combined with other region-based descriptors.


Dark dot segmentation; Dermoscopy images; Globule segmentation; Image classification; Melanoma detection; Skin lesions

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