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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. [ABSTRACT FROM AUTHOR]

In this work we propose a novel algorithm for foreground segmentation in video sequences that achieves better accuracy, while maintaining low complexity and allows real time execution. The proposed algorithm combines selected features from a number of well-established and state of the art algorithms, such as the Gaussian mixture models, the Self Organizing Maps and the illumination sensitive method. The presented methodology is capable of efficiently handling sudden light changes, both from natural and multiple artificial light sources, which may have caused erroneous segmentation for other algorithms. Comparative results are presented utilizing user-defined ground truth segmentation for two different types of indoor video sequences, one of which was obtained by a hemispheric omnidirectional dioptric (fish-eye) camera, with and without illumination changes and the second by a plain camera. The behavior of the algorithm with respect to its controlling parameters is investigated and its computational burden is studied. [ABSTRACT FROM AUTHOR]