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. 2022 Jun 2;24(6):783.
doi: 10.3390/e24060783.

NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images

Xiaofei Bian  1 , Haiwei Pan  1 , Kejia Zhang  1 , Chunling Chen  1 , Peng Liu  1 , Kun Shi  1
Affiliations

NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images

Xiaofei Bian et al. Entropy (Basel). .

Abstract

Skin lesion segmentation is the first and indispensable step of malignant melanoma recognition and diagnosis. At present, most of the existing skin lesions segmentation techniques often used traditional methods like optimum thresholding, etc., and deep learning methods like U-net, etc. However, the edges of skin lesions in malignant melanoma images are gradually changed in color, and this change is nonlinear. The existing methods can not effectively distinguish banded edges between lesion areas and healthy skin areas well. Aiming at the uncertainty and fuzziness of banded edges, the neutrosophic set theory is used in this paper which is better than fuzzy theory to deal with banded edge segmentation. Therefore, we proposed a neutrosophy domain-based segmentation method that contains six steps. Firstly, an image is converted into three channels and the pixel matrix of each channel is obtained. Secondly, the pixel matrixes are converted into Neutrosophic Set domain by using the neutrosophic set conversion method to express the uncertainty and fuzziness of banded edges of malignant melanoma images. Thirdly, a new Neutrosophic Entropy model is proposed to combine the three memberships according to some rules by using the transformations in the neutrosophic space to comprehensively express three memberships and highlight the banded edges of the images. Fourthly, the feature augment method is established by the difference of three components. Fifthly, the dilation is used on the neutrosophic entropy matrixes to fill in the noise region. Finally, the image that is represented by transformed matrix is segmented by the Hierarchical Gaussian Mixture Model clustering method to obtain the banded edge of the image. Qualitative and quantitative experiments are performed on malignant melanoma image dataset to evaluate the performance of the NeDSeM method. Compared with some state-of-the-art methods, our method has achieved good results in terms of performance and accuracy.

Keywords: HGMM; image segmentation; malignant melanoma image; morphology; neutrosophic entropy; neutrosophic set.

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Conflict of interest statement

The authors declare that they have no competing interest.

Figures

Figure 1
Figure 1
The framework of the NeDSeM method.
Figure 2
Figure 2
Examples of corrosion and expansion processes.
Figure 3
Figure 3
The edge of malignant melanoma image. The malignant melanoma image can be divided into three areas. Outside the outer edge is the background area; Between innter edge and outer edge is the fuzzy edge of the skin lesion, that is, the banded edge; Inside the inner edge is the skin lesion area.
Figure 4
Figure 4
(a) The position of point xi in the cube (b) The key points around point xi.
Figure 5
Figure 5
The position relationship between point xi and surrounding key points projected on plane ACDF.
Figure 6
Figure 6
The geometric meaning of single-valued neutrosophic entropy.
Figure 7
Figure 7
The sample graph of Rb.
Figure 8
Figure 8
Results of neutrosophic entropy conversion. The first line is the unprocessed conversion results of each channel after the neutrosophic entropy conversion, and the second line is the conversion results of each channel with morphological processing after the neutrosophic entropy conversion.
Figure 9
Figure 9
The segmentation results of different clustering methods.
Figure 10
Figure 10
The results after adding morphology in different steps. (a) Raw malignant melanoma images (b) The results of the only Rb processing. (c) The results of firstly morphological processing followed by Rb processing. (d) The results of firstly Rb processing followed by morphological processing.
Figure 11
Figure 11
Visualization of the segmentation results of our dataset and ISIC2018 dataset.
See this image and copyright information in PMC

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