Abstract
Because of the flexibility and availability of high-resolution digital cameras, dermatological photography is considered as a good alternative to dermoscopy. However, uneven background illumination on the dermatological photographs makes their automated analysis troublesome. Equalization of the uneven background illumination is helpful to make the automated analysis of the dermatological photographs more sensitive and specific. A customized algorithm for equalizing the uneven background illumination on dermatological photographs is proposed in this paper. The illumination-corrected image is reconstructed from the gamma-corrected illumination component in Hue Value Saturation (HSV) color space. The Retinex decomposition of the value component is formulated as a non-convex optimization problem. Constraints within the cost function are derived from the shading and texture priors. The shading and texture priors are computed respectively from the derivatives of the illumination and texture priors. On 137 dermatological photographs, the values of Average Gradient of the Illumination Component, Lightness Order Error, Sparse Feature Fidelity, Visual Saliency-based Index, Visual Information Fidelity and the computational time exhibited by the proposed devignetting algorithm are 0.1895 ± 0.0386, 232.9553 ± 140.7912, 0.9783 ± 0.0106, 0.9903 ± 0.0021, 0.7063 ± 0.0396 and 2.0272 ± 0.4319 (sec). The proposed algorithm is able to equalize the uneven background illumination without scaling or boosting it intolerably. It produces output images that are natural in appearance and free from structural/color artefacts. The loss of salient information is negligible in the proposed algorithm. It is computationally fast, as well.
Similar content being viewed by others
Availability of data and material
Data may be provided on request.
References
Salah, K.B., Othmani, M., Kherallah, M.: A novel approach for human skin detection using convolutional neural network. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02108-3
García, B.G., Pariente, J.A., Calzada, P.M.: Development of a clinical-dermoscopic model for the diagnosis of urticarial vasculitis. Sci. Rep. (2020). https://doi.org/10.1038/s41598-020-63146-w
MacLellan, A.N., Price, E.L., Brouwer, P.B., Matheson, K., Ly, T.Y., Pasternak, S., Walsh, N.M., Gallant, C.J., Oakley, A., Hull, P.R., Langley, R.G.: The use of non-invasive imaging techniques in the diagnosis of melanoma: a prospective diagnostic accuracy study. J. Am. Acad. Dermatol. (2020). https://doi.org/10.1016/j.jaad.2020.04.019
Glaister, J., Amelard, R., Wong, A., Clausi, D.A.: MSIM: multistage illumination modeling of dermatological photographs for illumination-corrected skin lesion analysis. IEEE Trans. Biomed. Eng. 60(7), 1873–1883 (2013)
Yang, Y., Jia, W., Wu, B.: Simultaneous segmentation and correction model for color medical and natural images with intensity inhomogeneity. Vis. Comput. 36, 717–731 (2020). https://doi.org/10.1007/s00371-019-01651-4
Norton, K.A., Iyatomi, H., Celebi, M.E., Ishizaki, S., Sawada, M., Suzaki, R., Kobayashi, K., Tanaka, M., Ogawa, K.: Three-phase general border detection method for dermoscopy images using non-uniform illumination correction. Skin Res. Technol. 18(3), 290–300 (2012)
Ren, X., Li, M., Cheng, W., Liu, J.: Joint enhancement and denoising method via sequential decomposition. In: Proc. IEEE International Symposium on Circuits and Systems (ISCAS), Florence, pp. 1–5 (2018)
Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27(6), 2828–2841 (2018)
Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X., Ding, X.: A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans. Image Process. 24(12), 4965–4977 (2015)
Agudo, A., Lepetit, V., Moreno-Noguer, F.: Simultaneous completion and spatiotemporal grouping of corrupted motion tracks. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02238-8
Zheng, Y., Lin, S., Kambhamettu, C., Yu, J., Kang, S.B.: Single-image vignetting correction. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2243–2256 (2009)
Kang, S., Weiss, R.: Can we calibrate a camera using an image of a flat textureless lambertian surface? In: ECCV, vol. 2, pp. 640–653 (2000)
Huang, S., Cheng, F., Chiu, Y.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. 22(3), 1032–1041 (2013)
Wang, S., Zheng, J., Hu, H., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013)
Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., Paisley, J.: A fusion-based enhancing method for weakly illuminated images. Signal Process. 129, 82–96 (2016)
Tian, Q.C., Cohen, L.D.: A variational-based fusion model for non-uniform illumination image enhancement via contrast optimization and color correction. Signal Process. 153, 210–220 (2018)
Zhou, M., Jin, K., Wang, S., Ye, J., Qian, D.: color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans. Biomed. Eng. 65(3), 521–527 (2018)
Srinivas, K., Bhandari, A.K.: Low light image enhancement with adaptive sigmoid transfer function. IET Image Process. 14(4), 668–678 (2020)
Shamsudeen, F.M., Raju, G.: An objective function based technique for devignetting fundus imagery using MST. Inf. Med. Unlocked 14, 82–91 (2019)
Bulut, F.: Low dynamic range histogram equalization (LDR-HE) via quantized Haar wavelet transform. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02281-5
Gupta, N., Garg, H., Agarwal, R.: A robust framework for glaucoma detection using CLAHE and EfficientNet. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02114-5
Lu, Y., Xie, F., Wu, Y., Jiang, Z., Meng, R.: No Reference uneven illumination assessment for dermoscopy images. IEEE Signal Process. Lett. 22(5), 534–538 (2015)
Xie, F., Lu, Y., Bovik, A.C., Jiang, Z., Meng, R.: Application-driven no-reference quality assessment for dermoscopy images with multiple distortions. IEEE Trans. Biomed. Eng. 63(6), 1248–1256 (2016)
Zhang, Y., Liu, H., Huang, N., Wang, Z.: Dynamical stochastic resonance for non-uniform illumination image enhancement. IET Image Process. 12(12), 2147–2152 (2018)
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)
Chang, H., Yang, H., Gan, Y., Wang, M.: Sparse feature fidelity for perceptual image quality assessment. IEEE Trans. Image Process. 22(10), 4007–4018 (2013)
Zhang, L., Shen, Y., Li, H.: VSI: a visual saliency-induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23(10), 4270–4281 (2014)
DermQuest.: Available: https://uwaterloo.ca/vision-image-processing-lab/research-demos/skin-cancer-detection (2020)
Hameed, N., Shabut, A.M., Ghosh, M.K., Hossain, M.A.: Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Syst. Appl. 141 (2020)
Zhang, N., Cai, Y.X., Wang, Y.Y., Tian, Y.T., Wang, X.L., Badami, B.: Skin cancer diagnosis based on optimized convolutional neural network. Artif. Intell. Med. 102 (2020)
Hajabdollahi, M., Esfandiarpoor, R., Khadivi, P., Soroushmehr, S.M.R., Karimi, N., Samavi, S.: Simplification of neural networks for skin lesion image segmentation using color channel pruning. Comput. Med. Imaging Graph. 82 (2020)
Funding
This study received no funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Code availability
Code can be made available on request.
Human or animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sathish, S., Sumithra, M.G. & Mohanasundaram, K. Shading and texture constrained retinex for correcting vignetting on dermatological macro images. Vis Comput 39, 693–709 (2023). https://doi.org/10.1007/s00371-021-02368-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-021-02368-z