Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection

Abstract

BackgroundThe performance of Computer Aided Diagnosis Systems for early melanoma detection relies mainly on quantitative evaluation of the geometric features corresponding to skin lesions. In these systems, diagnosis is carried out by analyzing four geometric characteristics: asymmetry (A), border (B), color (C) and dimension (D). The main objective of this study is to establish an algorithm for the measurement of asymmetry in biological entities.MethodsBinary digital images corresponding to lesions are divided into 8 segments from their centroid. For each segment, the discrete compactness value is calculated using Normalized E-Factor (NEF). The asymmetry value is obtained from the sum of the square difference of each NEF value and corresponding value of its opposite by the vertex. Two public skin cancer databases were used. 1) Lee’s database with 40 digital regions evaluated by fourteen dermatologists. 2) The PH2 database which consists of 200 images in an 8-bit RGB format. This database provides a pre-classification of asymmetry carried out by experts, and it also indicates if the lesion is a melanoma.ResultsThe measure was applied using two skin lesion image databases. 1) In Lee’s database, Spearman test provided a value of 0.82 between diagnosis of dermatologists and asymmetry values. For the 12 binary images most likely to be melanoma, the correlation between the measurement and dermatologists was 0.98. 2) In the PH2 database a label is provided for each binary image where the type of asymmetry is indicated. Class 0–1 corresponds to symmetry and one axis of symmetry shapes, the completely asymmetrical were assigned to Class 2, the values of sensitivity and specificity were 59.62 and 85.8% respectively between the asymmetry measured by a group of dermatologists and the proposed algorithm.ConclusionsSimple image digital features such as compactness can be used to quantify the asymmetry of a skin lesion using its digital binary image representation. This measure is stable taking into account translations, rotations, scale changes and can be applied to non-convex regions, including areas with holes.

Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification

“…Then, both images were divided into eight parts to assess the shape and color asymmetry of the skin lesion (see Figure 3). Indeed, according to the ABCDE rule [23] and following the parameters extracted by Bhuiyan et al [24], the following image features were collected (see The work by Dalila et al [25] showed that most of the relevant features for skin lesion classification are related to color variation; hence, we paid more attention to that type of features. To extract the colors from the images, the K-means algorithm was applied.…”

An algorithm for characterizing skin moles using image processing and machine learning

“…The second characteristic obtained is the irregularity of the edges that could exist, for this we analyze the edge of the mole. The same procedure as Sancen-Plaza et al in [23] is followed with the area obtained in the process of application (10) shown in Figure 10(b). From this process, 9 descriptors of the degree of irregularity of the border are obtained, according to Santiago-Montero et al [24].…”