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. 2022 Jan 29;12(2):344. doi: 10.3390/diagnostics12020344

Table 1.

Image preprocessing methods used for segmentation of skin lesions.

Study Method
[2] Artifact removal using morphological operations and image enhancement with unsharp filtering.
[8] Artifact removal using thresholding and image enhancement with a median filter.
[9] Artifact removal using the bottom-hat filter, dark corner removal with thresholding, and color enhancement by the intensity with saturation features of the HSV color model.
[12] Artifact removal using DullRazor.
[17] Artifact removal using DullRazor and image enhancement by noise filtering with intensity adjustment.
[19] Artifact removal using improved DullRazor and image enhancement with top–bottom filtering, contrast stretching, and log transformation.
[20] Artifact removal using averaging filter and image enhancement with contrast enhancement.
[21] Artifact removal using multiscale decomposition.
[22] Image enhancement using contrast enhancement method.
[23] Artifact removal using a fast line detector and image enhancement with gamma correction.
[25] Artifact removal using DullRazor.
[26] Artifact removal using threshold decomposition and image enhancement for illumination correction with homomorphic filtering.
[27] Image enhancement using adaptive gamma correction.
[28] Artifact removal using DullRazor.
[31] Image enhancement using mean subtraction and standard deviation-based normalization.
[32] Artifact removal and image enhancement using color constancy with shades of gray.
[33] Artifact removal and image enhancement using histogram-based preprocessing.
[34] Artifact removal using a deep learning method.
[36] Artifact removal using DullRazor.
[37] Artifact removal using morphological operations and image enhancement with histogram equalization.
[38] Artifact removal using DullRazor.
[39] Artifact removal using DullRazor and image enhancement with global-local contrast stretching.
[40] Artifact removal using median filter and image enhancement with contrast-limited adaptive histogram equalization.
[41] Artifact removal using Frangi Vesselness filter and image enhancement with contrast-limited adaptive histogram equalization.
[47] Artifact removal using DullRazor and image enhancement with adaptive histogram equalization.
[48] Artifact removal using DullRazor with a median filter.
[49] Image enhancement using adaptive histogram equalization.
[50] Image enhancement using contrast limited adaptive histogram equalization.
[51] Image enhancement using contrast limited adaptive histogram equalization.
[52] Image enhancement using Z-score transformation.