Skin lesion segmentation is one of the most important steps in automated early skin cancer detection, since the accuracy of the following steps significantly depends on it. In this paper, a two-stage approach based on Mean Shift and spectral graph partitioning algorithms is proposed. This method effectively extracts lesion borders. Moreover, a distinctive advantage of this approach is extracting the region of interest levels that is not addressed in pervious state of the art methods. In the first stage, the image is segmented to regions using Mean Shift algorithm. In the second stage, a graph-based representation is used to demonstrate the structure of the extracted regions and their relationships. Afterwards a clustering process is applied, considering the neighborhood system and analyzing the color and texture distance between regions. The proposed method is applied to 170 dermoscopic images and evaluated with two different metrics. This evaluation has performed by means of the segmentation results provided by an experienced dermatologist as the ground truth. Experiments demonstrate that in this method, challenging features of skin lesions are handled as might be expected when compared to five state of the art methods.
Skin lesion segmentation is one of the most important steps for automated early skin cancer detection since the accuracy of the following steps significantly depends on it. In this paper we present a novel approach based on spectral clustering that provides accurate and effective segmentation for dermoscopy images. In the proposed method, an optimized clustering algorithm has been provided which effectively extracts lesion borders using spectral graph partitioning algorithm in an appropriate color space, considering special characteristics of dermoscopy images. The proposed segmentation method has been applied to 170 dermoscopic images and evaluated with two metrics, by means of the segmentation results provided by an experienced dermatologist as the ground truth. The experiment results of this approach demonstrate that, complex contours are distinguished correctly while challenging features of skin lesions such as topological changes, weak or false contours, and asymmetry in color and shape are handled as might be expected when compared to four state of the art methods.
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