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. 2022 Sep 20;17(9):e0274896.
doi: 10.1371/journal.pone.0274896. eCollection 2022.

Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern

Siti Salbiah Samsudin  1 , Hamzah Arof  1 , Sulaiman Wadi Harun  1 , Ainuddin Wahid Abdul Wahab  2 , Mohd Yamani Idna Idris  2
Affiliations

Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern

Siti Salbiah Samsudin et al. PLoS One. .

Abstract

Skin cancer is the most common type of cancer in many parts of the world. As skin cancers start as skin lesions, it is important to identify precancerous skin lesions early. In this paper we propose an image based skin lesion identification to classify seven different classes of skin lesions. First, Multi Resolution Empirical Mode Decomposition (MREMD) is used to decompose each skin lesion image into a few Bidimensional intrinsic mode functions (BIMF). MREMD is a simplified bidimensional empirical mode decomposition (BEMD) that employs downsampling and upsampling (interpolation) in the upper and lower envelope formation to speed up the decomposition process. A few BIMFs are extracted from the image using MREMD. The next step is to locate the lesion or the region of interest (ROI) in the image using active contour. Then Local Binary Pattern (LBP) is applied to the ROI of the image and its first BIMF to extract a total of 512 texture features from the lesion area. In the training phase, texture features of seven different classes of skin lesions are used to train an Artificial Neural Network (ANN) classifier. Altogether, 490 images from HAM10000 dataset are used to train the ANN. Then the accuracy of the approach is evaluated using 315 test images that are different from the training images. The test images are taken from the same dataset and each test image contains one type of lesion from the seven types that are classified. From each test image, 512 texture features are extracted from the lesion area and introduced to the classifier to determine its class. The proposed method achieves an overall classification rate of 98.9%.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of the proposed system.
Fig 2
Fig 2. MREMD pseudocode.
Fig 3
Fig 3. Samples of segmented skin lesions.
Fig 4
Fig 4. The decomposed skin lesion using MREMD.
Fig 5
Fig 5. Performance comparison of different classifiers.
Fig 6
Fig 6. Comparison of execution time for different classifier.
See this image and copyright information in PMC

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