Border and Asymmetry measuring of skin lesion for diagnostic of melanoma using a perimeter ratio

doi:10.15520/ajcsit.v6i2.41

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…As described by [29], the general approach used by a CAD system consists in describing the skin lesion by means of a set of textural and geometrical shape features known as the ABCD rule (asymmetry, border, color and diameter). Software WEKA was used to apply 13 different techniques and a statistical test K-folds to obtain the classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Skin Lesions Based on Dermoscopic Images Using Image Processing Techniques

Zaqout¹

2016

IJSIP

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Great effort has been put into the development of diagnosis methods for the most dangerous type of skin diseases-melanoma. This paper aims to develop a prototype capable of segment and classify skin lesions in dermoscopy images based on ABCD rule. The proposed work is divided into four distinct stages: (1) preprocessing, consists of filtering and contrast enhancing techniques, (2) segmentation, thresholding, and statistical properties are computed to localize the lesion, (3) features extraction, asymmetry is calculated by averaging the calculated results of the two methods: entropy and bi-fold. Border irregularity is calculated by accumulate the statistical scores of the eight segments of the segmented lesion. Color feature is calculated among the existence of six candidate colors: white, black, red, light-brown, dark-brown, and blue-gray. Diameter is measured by the conversion operation from the total number of pixels in the greatest diameter into millimeter (mm), and (4) classification, the summation of the four extracted feature scores multiplied by their weights to yield a total dermoscopy score (TDS); hence, the lesion is classified into benign, suspicious, or malignant. The prototype is implemented in MATLAB and the dataset used consists of 200 dermoscopic images from Hospital Pedro Hispano, Matosinhos. The achieved results show an acceptable performance rates, an accuracy 90%, sensitivity 85%, and specificity 92.22%.

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Section: Related Workmentioning

confidence: 99%

Diagnosis of Skin Lesions Based on Dermoscopic Images Using Image Processing Techniques

Zaqout¹

2016

IJSIP

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

Sancen-Plaza

Santiago-Montero

Sossa

et al. 2018

BMC Med Inform Decis Mak

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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.

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ANN-based diagnosis method for skin cancers using dermoscopic images

Khezri

Farzaneh

Ghasemishahrestani

et al. 2021

Highlights in BioScience

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Melanoma is one of the most dangerous skin cancers in the world. It accounts for 55% of all deaths associated with skin cancer. Researchers believe that skin cancer increases the risk of other cancers if not diagnosed early. Therefore, prompt and timely diagnosis of this disease is very important for the successful treatment of the patient. This system can detect melanoma lethal carcinoma from other skin lesions without the need for surgery, with a low cost, accuracy of about 98.88% and specificity 99%. In this article, a new, intelligent and accurate software (Delphi) system has been used to diagnose melanoma skin cancer. To detect malignant melanoma, the ABCDT rule, asymmetry (A), boundary (B), color (C), diameter (D) and textural variation (T) of the lesion are calculated and finally, an artificial neural network (ANN) is used to obtain an accurate result. The ANN with Multi-Layer Perceptron (MLP) contains the five extraction Characteristics (ABCDT) of lesions is used as inputs, two hidden layers, and two outputs. Very good results were obtained using this method. It was observed that for a dataset of 180 dermoscopic lesion images including 80 malignant melanomas, 20 benign melanomas and 80 nevus lesions. Due to its automatic recognition and ability to be installed on a computer, this system can be very useful for dermatologists as well as the general public.

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Border and Asymmetry measuring of skin lesion for diagnostic of melanoma using a perimeter ratio

Cited by 4 publications

References 11 publications

Diagnosis of Skin Lesions Based on Dermoscopic Images Using Image Processing Techniques

Diagnosis of Skin Lesions Based on Dermoscopic Images Using Image Processing Techniques

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

ANN-based diagnosis method for skin cancers using dermoscopic images

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