An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection.
Dermoscopy
Non-Local Means Filter
Phase congruency
Robust Image Contrast Enhancement
Unsharp masking
Journal
Medical & biological engineering & computing
ISSN: 1741-0444
Titre abrégé: Med Biol Eng Comput
Pays: United States
ID NLM: 7704869
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
23
01
2022
accepted:
13
04
2023
medline:
23
10
2023
pubmed:
2
8
2023
entrez:
2
8
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37530886
doi: 10.1007/s11517-023-02897-w
pii: 10.1007/s11517-023-02897-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2921-2938Informations de copyright
© 2023. International Federation for Medical and Biological Engineering.
Références
Siegel RL, Miller KD, Jemal A (2018) Cancer statistics. CA A Cancer J Clin 68(1):7–30
doi: 10.3322/caac.21442
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin 68(6):394–424
doi: 10.3322/caac.21492
Bafounta ML, Beauchet A, Aegerter P, Saiag P (2001) Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Arch Dermatol 137(10):1343–1350
doi: 10.1001/archderm.137.10.1343
pubmed: 11594860
Madhankumar K, Kumar P (2012) Characterization of skin lesions. In: International Conference on Pattern Recognition, Informatics and Medical Engineering. IEEE, pp 302–306
Jaworek-Korjakowska J (2015) Novel method for border irregularity assessment in dermoscopic color images. Comput Math Methods Med 2015:1–11
doi: 10.1155/2015/496202
Ocampo-Blandón CF, Restrepo-Parra E, Riaño-Rojas JC, Jaramillo-Ayerbe PF (2016) Contrast enhancement by searching discriminant color projections in dermoscopy images. Revista Facultad Ingenieria, Univ Antioquia 79:192–200
Mishra NK, Celebi ME (2016) An overview of melanoma detection in dermoscopy images using image processing and machine learning. arXiv preprint arXiv:1601.07843
O Cherepkova, & JY Hardeberg (2018) Enhancing dermoscopy images to improve melanoma detection, 2018 Colour and Visual Computing Symposium (CVCS), 1–6
Jayalakshmi D, Dheeba J (2020) Border detection in skin lesion images using an improved clustering algorithm. Int J e-Collab 16(4):15–29
Zghal NS, Derbel N (2020) Melanoma skin cancer detection based on image processing. Curr Med Imaging 16(1):50–58
doi: 10.2174/1573405614666180911120546
Kandhway P, Bhandari AK, Singh A (2020) A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization. Biomed Signal Process Control 56:101677
doi: 10.1016/j.bspc.2019.101677
Jeevakala S, Brintha A (2018) Therese, Sharpening enhancement technique for MR images to enhance the segmentation. Biomed Signal Process Control 41:21–30
doi: 10.1016/j.bspc.2017.11.007
Heo Y-C, Kim K, Lee Y (2020) Image de-noising using Non-Local Means (NLM) approach in magnetic resonance (MR) imaging: a systematic review. Appl Sci 10(7028):1–16
Duan X (2019) A multiscale contrast enhancement for mammogram using dynamic unsharp masking in Laplacian Pyramid. IEEE Trans Radiat Plasma Med Sci 3(5):557–564
doi: 10.1109/TRPMS.2018.2876873
Gu K, Zhai G, Yang X, Zhang W, Chen CW (2015) Automatic contrast enhancement technology with saliency preservation. IEEE Trans Circuits Syst Video Technol 25(9):1480–1494
doi: 10.1109/TCSVT.2014.2372392
Rajchel M, Oszust M (2021) No-reference image quality assessment of authentically distorted images with global and local statistics. SIViP 15:83–91
doi: 10.1007/s11760-020-01725-0
Arvanitopoulos N, Achanta R, Susstrunk S (2017) Single image reflection suppression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4498–4506
Li J, Hu Q, Ai M (2020) RIFT: multi-modal image matching based on radiation-variation insensitive feature transform. IEEE Trans Image Process 29:3296–3310
doi: 10.1109/TIP.2019.2959244
Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th international symposium on biomedical imaging. IEEE, pp 168–172
Mishra D, Chaudhury S, Sarkar M, Soin AS, Sharma V (2018) Edge probability and pixel relativity-based speckle reducing anisotropic diffusion. IEEE Trans Image Process 27(2):649–664
doi: 10.1109/TIP.2017.2762590
pubmed: 29028196
Gavaskar RG, Chaudhury KN (2019) Fast adaptive bilateral filtering. IEEE Trans Image Process 28(2):779–790
doi: 10.1109/TIP.2018.2871597
pubmed: 30235131
Zhou M, Jin K, Wang S, Ye J, Qian D (2018) Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans Biomed Eng 65(3):521–527
doi: 10.1109/TBME.2017.2700627
pubmed: 28475043
Shamsudeen FM, Raju G (2019) An objective function based technique for devignetting fundus imagery using MST. Inform Med Unlocked 14:82–91
doi: 10.1016/j.imu.2018.10.001
Srinivas K, Bhandari AK (2020) Low light image enhancement with adaptive sigmoid transfer function. IET Image Proc 14(4):668–678
doi: 10.1049/iet-ipr.2019.0781
Celik T, Tjahjadi T (2011) Contextual and variational contrast enhancement. IEEE Trans Image Process 20(12):3431–3441
doi: 10.1109/TIP.2011.2157513
pubmed: 21609884
Liu J (2018) A cascaded deep convolutional neural network for joint segmentation and genotype prediction of brainstem gliomas. IEEE Trans Biomed Eng 65(9):1943–1952
doi: 10.1109/TBME.2018.2845706
pubmed: 29993462
Bai X et al (2019) Intuitionistic center-free FCM clustering for MR brain image segmentation. IEEE J Biomed Health Inform 23(5):2039–2051
doi: 10.1109/JBHI.2018.2884208
pubmed: 30507540
Quan R et al (2019) A novel IGBT health evaluation method based on multi-label classification. IEEE Access 7:47294–47302
doi: 10.1109/ACCESS.2019.2909741
Jaisakthi SM et al (2018) Automated skin lesion segmentation of dermoscopic images using GrabCut and K-means algorithms. IET Comput Vision 12(8):1088–1095
doi: 10.1049/iet-cvi.2018.5289
Lee C, Lee C, Kim C (2013) Contrast enhancement based on layered difference representation of 2D histograms. IEEE Trans Image Process 22(12):5372–5384
doi: 10.1109/TIP.2013.2284059
pubmed: 24108715