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. 2017 Oct 30;5(1):10.
doi: 10.1007/s13755-017-0033-x. eCollection 2017 Dec.

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine

Maram A Wahba  1 , Amira S Ashour  1 , Sameh A Napoleon  1 , Mustafa M Abd Elnaby  1 , Yanhui Guo  2
Affiliations

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine

Maram A Wahba et al. Health Inf Sci Syst. .

Abstract

Purpose: Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors.

Methods: In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM).

Results: The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features.

Conclusion: Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

Keywords: Basal cell carcinoma; Empirical mode decomposition; Gray-level difference method; Riesz; Skin cancer classification; Support vector machine.

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Figures

Fig. 1
Fig. 1
Sample images from the dataset: a Basal cell carcinoma; b nevus
Fig. 2
Fig. 2
One level of DWT decomposition system
Fig. 3
Fig. 3
Wavelet output components
Fig. 4
Fig. 4
BEMD sifting process flowchart
Fig. 5
Fig. 5
SVM two-dimensional feature space for two data sample classes represented by blue and red points
Fig. 6
Fig. 6
Proposed system diagram
Fig. 7
Fig. 7
a Original image; b image after DullRazor hair removal
Fig. 8
Fig. 8
Sample skin lesion segmentation results: a input image, b segmentation mask, c output segmentation contour, and d segmented lesion
Fig. 9
Fig. 9
The ROC curves of Q-SVM for different features cases: a BEMD only; b BEMD-Riesz; c GLDM; and d proposed combined BEMD-Riesz and GLDM
Fig. 10
Fig. 10
Accuracy of SVMs against different features combinations
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