An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection
In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using R...
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| Veröffentlicht in: | Medical & biological engineering & computing 2023-11, Vol.61 (11), p.2921-2938 |
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| creator | Jeba Derwin, D. Jeba Singh, O. Priestly Shan, B. Uma Maheswari, K. Lavanya, D. |
| description | In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency–based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.
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| doi_str_mv | 10.1007/s11517-023-02897-w |
| format | Article |
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Graphical Abstract</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-023-02897-w</identifier><identifier>PMID: 37530886</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial neural networks ; automation ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Computer Applications ; Dermoscopy - methods ; Human Physiology ; Humans ; Illumination ; Image contrast ; Image enhancement ; Image quality ; Image segmentation ; Imaging ; Learning algorithms ; Lesions ; lighting ; Machine learning ; Melanoma ; Melanoma - diagnosis ; Original Article ; Pattern Recognition, Automated - methods ; Preservation ; Radiology ; Reproducibility of Results ; Segmentation ; Sharpening ; Shaving ; Skin Diseases ; Skin lesions ; Skin Neoplasms - diagnosis</subject><ispartof>Medical & biological engineering & computing, 2023-11, Vol.61 (11), p.2921-2938</ispartof><rights>International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. International Federation for Medical and Biological Engineering.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-adb19ee64a968fc84c5e527d4ce17a237e87a4f28ce03c7f534b5315b63dee273</citedby><cites>FETCH-LOGICAL-c452t-adb19ee64a968fc84c5e527d4ce17a237e87a4f28ce03c7f534b5315b63dee273</cites><orcidid>0000-0001-7631-5680</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-023-02897-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-023-02897-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37530886$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jeba Derwin, D.</creatorcontrib><creatorcontrib>Jeba Singh, O.</creatorcontrib><creatorcontrib>Priestly Shan, B.</creatorcontrib><creatorcontrib>Uma Maheswari, K.</creatorcontrib><creatorcontrib>Lavanya, D.</creatorcontrib><title>An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency–based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.
Graphical Abstract</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>automation</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Computer Applications</subject><subject>Dermoscopy - methods</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Illumination</subject><subject>Image contrast</subject><subject>Image enhancement</subject><subject>Image quality</subject><subject>Image segmentation</subject><subject>Imaging</subject><subject>Learning algorithms</subject><subject>Lesions</subject><subject>lighting</subject><subject>Machine learning</subject><subject>Melanoma</subject><subject>Melanoma - diagnosis</subject><subject>Original Article</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Preservation</subject><subject>Radiology</subject><subject>Reproducibility of 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Comput</addtitle><date>2023-11-01</date><risdate>2023</risdate><volume>61</volume><issue>11</issue><spage>2921</spage><epage>2938</epage><pages>2921-2938</pages><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency–based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.
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| subjects | Algorithms Artificial neural networks automation Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Computer Applications Dermoscopy - methods Human Physiology Humans Illumination Image contrast Image enhancement Image quality Image segmentation Imaging Learning algorithms Lesions lighting Machine learning Melanoma Melanoma - diagnosis Original Article Pattern Recognition, Automated - methods Preservation Radiology Reproducibility of Results Segmentation Sharpening Shaving Skin Diseases Skin lesions Skin Neoplasms - diagnosis |
| title | An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection |
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