Saliency-based segmentation of dermoscopic images using color information

Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma. This paper investigates how color information, besides saliency, can be used to determine the pigmented lesion region automatically. Unlike most existing segmentation methods using only the saliency in order to discriminate against the skin lesion from the surrounding regions, we propose a novel method employing a binarization process coupled with new perceptual criteria, inspired by the human visual perception, related to the properties of saliency and color of the input image data distribution. As a means of refining the accuracy of the proposed method, the segmentation step is preceded by a pre-processing aimed at reducing the computation burden, removing artifacts, and improving contrast. We have assessed the method on two public databases, including 1497 dermoscopic images. We have also compared its performance with classical and recent saliency-based methods designed explicitly for dermoscopic images. The qualitative and quantitative evaluation indicates that the proposed method is promising since it produces an accurate skin lesion segmentation and performs satisfactorily compared to other existing saliency-based segmentation methods.Comment: Preprin

Evaluation of quality measures for color quantization

Visual quality evaluation is one of the challenging basic problems in image processing. It also plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods focused on images corrupted by common degradation types while little attention was paid to color quantization. This in spite there is a wide range of applications requiring color quantization assessment being used as a preprocessing step when color-based tasks are more efficiently accomplished on a reduced number of colors. In this paper, we propose and carry-out a quantitative performance evaluation of nine well-known and commonly used full-reference image quality assessment measures. The evaluation is done by using two publicly available and subjectively rated image quality databases for color quantization degradation and by considering suitable combinations or subparts of them. The results indicate the quality measures that have closer performances in terms of their correlation to the subjective human rating and show that the evaluation of the statistical performance of the quality measures for color quantization is significantly impacted by the selected image quality database while maintaining a similar trend on each database. The detected strong similarity both on individual databases and on databases obtained by integration provides the ability to validate the integration process and to consider the quantitative performance evaluation on each database as an indicator for performance on the other databases. The experimental results are useful to address the choice of suitable quality measures for color quantization and to improve their future employment.Comment: Preprin