title: Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy.
abstract: 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.
authors.affiliation: Department of Digital Systems, University of Piraeus, Greece. Electronic address: imaglo@unipi.gr.
authors.forename: Ilias
authors.initials: I
authors.lastname: Maglogiannis
authors.affiliation: University of Thessaly, Department of Computer Science and Biomedical Informatics, Greece.
authors.forename: Konstantinos K
authors.initials: KK
authors.lastname: Delibasis
mesh_terms: D000465:Algorithms; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D012871:Skin Diseases / Q000175:diagnosis*
keywords: Dark dot segmentation; Dermoscopy images; Globule segmentation; Image classification; Melanoma detection; Skin lesions