Applicability of Facial De-Identification Methods for Privacy Protection in Dermatology: Systematic Review (Preprint)

BACKGROUND De-identifying facial images is critical for protecting patient anonymity in the era of increasing tools for automatic image analysis in dermatology. OBJECTIVE The purpose of this paper was to review the current literature in the field of automatic facial de-identification algorithms. METHODS We conducted a systematic search using a combination of headings and keywords to encompass the concepts of facial de-identification and privacy preservation. The databases MEDLINE (via Pubmed), Embase (via Elsevier) and Web of Science (via Clarivate) were queried from inception to 5/1/2021. Studies of wrong design and outcomes were excluded during the screening and review process. RESULTS A total of 18 studies were included in the final review reporting various methodologies of facial de-identification algorithms. The study methods were rated individually for their utility for use cases in dermatology pertaining to skin color/pigmentation and texture preservation, data utility, and human detection. Most studies notable in the literature address feature preservation while sacrificing skin color and texture. CONCLUSIONS Facial de-identification algorithms are sparse and inadequate to preserve both facial features and skin pigmentation/texture quality in facial photographs. A novel approach is needed to ensure greater patient anonymity, while increasing data access for automated image analysis in dermatology for improved patient care.

A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging

Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution mounted cameras, presents a challenging issue when performing different image processing tasks related to urban areas monitoring. Accordingly, the state-of-the-art reported works can correct the shadow regions, but the heterogeneity between the corrected shadow and non-shadow areas is still evident and especially noticeable in concrete and asphalt regions. The present work introduces a local color transfer methodology to shadow removal which is based on the CIE L*a*b (Lightness, a and b) color space that considers chromatic differences in urban regions, and it is followed by a color tuning using the HSV color space. The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. The qualitative comparison was visually realized and proved that the proposed method enhances the color correspondence without losing texture information. Quantitative and qualitative results validate the results of color correction and texture preservation accuracy of the proposed method against other published methodologies.

Adaptive Directional Cubic Convolution for Integrated Circuit Chip Defect Image Interpolation

An adaptive directional cubic convolution interpolation method for integrated circuit (IC) chip defect images is proposed in this paper, to meet the challenge of preserving edge and texture information. In the proposed method, Otsu thresholding technique is employed to distinguish strong edge pixels from weak ones and texture regions, and estimate the direction of strong edges, adaptively. Boundary pixels are pre-interpolated using the original bicubic interpolation method to help improve the interpolation accuracy of the interior pixels. The experimental results of both classic test images and IC chip defect images demonstrate that the proposed method outperforms the competing methods with better edge and texture preservation, interpolation quality, more natural visual effect of the interpolated images and reasonable computational time. The proposed method can provide high quality IC chip images for defect detection and has been successfully applied on practical vision inspection for IC chips

A Novel Method of Image Denoising: New Variant of Block Matching and 3D

The demand of accurate and visually fair images is increasing with the passage of time and bang of the number of digital images especially in the domain of medical and healthcare systems. The visual image quality of modern cameras affected due to edges, textures and sharp structures noise. Though research community has introduced several techniques such as BM3D (Block Matching and 3D) for image denoising. However, edges and texture preservation capabilities remain issues due to hard thresholds values and captured image diversity. In order to address these issues, we propose a new variant of BM3D namely BM3DMA (Block Matching and 3D with Mahalanobis and Adaptive filter) which is employed through the use of Mahalanobis distance measure (for diversity coverage) and adaptive filter (for soft thresholds). We used two widely known datasets consist of set of standard and medical images. We observe 5% to 10% enhancement in the performance of BM3DMA as compared to BM3D in terms of improving the PSNR (Peak Signal to Noise Ratio) value. The promising experimental result indicates the effectiveness of BM3DMA in terms preserving the edge and texture image noise.