Automatic Detection of Globules, Streaks and Pigment Network Based on Texture and Color Analysis in Dermoscopic Images

Jiménez, Álvaro; Serrano, Carmen; Acha, Begoña

doi:10.1007/978-3-319-59876-5_54

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Label {2} Dysp NV is characterized by a typical pigment network with irregular grid lines, globules, and streaks that may be found with the symmetrical distribution. Label {3} Mel is characterized by a typical pigment network with irregular grid lines and asymmetrical distribution of globules and streaks, with the spread of melanoma expected [55].…”

Section: Staging Melanocytic Neoplasmsmentioning

confidence: 99%

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Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

et al. 2020

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The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

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Section: Staging Melanocytic Neoplasmsmentioning

confidence: 99%

“…Melanocytic neoplasms have different dermoscopy structures, such as pigment network, globules, and streaks. Detection of streaks, globules, and pigment network is very significant in the assessment of the malignancy of a lesion [55]. Table 2 lists the pseudo-code for the staging melanocytic neoplasms.…”

Section: Staging Melanocytic Neoplasmsmentioning

confidence: 99%