An effective hair removal algorithm for dermoscopy images

Toossi, Mohammad Taghi Bahreyni; Pourreza, Hamid Reza; Zare, Hoda; Sigari, Mohamad Hoseyn; Layegh, Pouran; Azimi, Abbas

doi:10.1111/srt.12015

Cited by 68 publications

(35 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Abbas et al [8] proposed the hair lines detection scheme based on the 2-D derivatives of Gaussian function in the CIE L n a n b n color space and then used the morphological operator to obtain smooth hair lines which were inpainted by the fast marching inpainting method. A similar approach was described in [9], which improved the canny method to roughly detect and remove hairs from dermoscopy images through a multiresolution coherence transport inpainting method.…”

Section: Introductionmentioning

confidence: 89%

See 1 more Smart Citation

No-reference hair occlusion assessment for dermoscopy images based on distribution feature

Xie

Meng

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 89%

“…For each image, the objective metric C is calculated by Eq. (9), and the quality ground truth is obtained through subjective experiments. The evaluation criteria includes Pearson linear correlation coefficient (LCC) and Spearman rank-order correlation coefficient (SROCC).…”

Section: The Effectiveness Of Hair Occlusion Assessment For Real Imagesmentioning

confidence: 99%

No-reference hair occlusion assessment for dermoscopy images based on distribution feature

Xie

Meng

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…A wide range of algorithms has been used for image segmentation, broadly categorized as pixel-based segmentation, region-based segmentation and edge detection (Joel et al, 2002;Mendonca et al, 2007;Sadri et al, 2013;Toossi et al, 2013;Wang et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

et al. 2016

View full text Add to dashboard Cite

Introduction: Early detection of suspicious skin lesions is critical to prevent skin malignancies, particularly the melanoma, which is the most dangerous form of human skin cancer. In the last decade, image processing techniques have been an increasingly important tool for early detection and mathematical models play a relevant role in mapping the progression of lesions. Methods: This work presents an algorithm to describe the evolution of the border of the skin lesion based on two main measurable markers: the symmetry and the geometric growth path of the lesion. The proposed methodology involves two dermoscopic images of the same melanocytic lesion obtained at different moments in time. By applying a mathematical model based on planar linear transformations, measurable parameters related to symmetry and growth are extracted. Results: With this information one may compare the actual evolution in the lesion with the outcomes from the geometric model. First, this method was tested on predefined images whose growth was controlled and the symmetry known which were used for validation. Then the methodology was tested in real dermoscopic melanoma images in which the parameters of the mathematical model revealed symmetry and growth rates consistent with a typical melanoma behavior. Conclusions: The method developed proved to show very accurate information about the target growth markers (variation on the growth along the border, the deformation and the symmetry of the lesion trough the time). All the results, validated by the expected phantom outputs, were similar to the ones on the real images.

show abstract

“…used an adaptive Canny edge detector and Sultana et al. proposed a top‐hat morphological technique with patch‐based inpainting . It is particularly important that routine dermatoscopic examination of lesions by dermatologists may be significantly hampered by the presence of hair when using examination techniques such as ‘immediate pattern recognition', which enable assessment of potential malignancy in ‘the blink of an eye’ .…”

Section: Introductionmentioning

confidence: 99%

“…Even after a flawless physical shave, the intraepidermal parts of the hair will remain dermatoscopically visible. In order to address this issue, multiple hair detection and removal algorithms were designed for application in automated digital image analysis [10][11][12][13][14][15]. Lee at al.…”

Section: Introductionmentioning

confidence: 99%

Physicians' level of hindrance by body hair in dermatoscopy and clinical benefit of an automated hair removal algorithm

Fink

Uhlmann

Vogt

et al. 2019

J Deutsche Derma Gesell

View full text Add to dashboard Cite

Background and objectives: Dermatoscopy may be hindered by body hair, and the development of an automated hair removal algorithm (AuHRA) might improve the diagnostic accuracy. However, the physicians' exact level of hindrance and the clinical benefit attained by AuHRA has not been assessed. The objectives of this study are to quantify the physicians' level of hindrance by body hair and the level of improvement in the visibility of underlying dermatoscopic patterns after application of AuHRA to digital images of hair-covered nevi.Patients and methods: A cross-sectional reader study including 59 sets of dermatoscopic images of benign nevi that were presented to six dermatologists. Each set included three images of one individual nevus (unshaved / physically shaved / digitally shaved with AuHRA), which were compared to each other within each set to assess the level of improvement caused by hair removal.Results: In comparison to unshaved lesions, dermatologists attributed the highest mean level of improvement to a physical shave (+1.36, p < 0.001) followed by AuHRA's digital shave (+0.79, p < 0.001). The majority of dermatologists considered the application of AuHRA as helpful and confirmed a medical need. Conclusions:The dermatologists in our study confirmed a substantial impairment of the dermatoscopic examination by body hair. We demonstrated a clinical benefit attained by AuHRA in comparison to unshaved or physically shaved lesions.

show abstract

An effective hair removal algorithm for dermoscopy images

Abstract: The results demonstrate that the proposed algorithm is highly accurate and able to detect and repair the hair pixels with few errors. In addition, the segmentation veracity of the skin lesion is effectively improved after our proposed hair removal algorithm.

Cited by 68 publications

References 14 publications

No-reference hair occlusion assessment for dermoscopy images based on distribution feature

No-reference hair occlusion assessment for dermoscopy images based on distribution feature

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

Physicians' level of hindrance by body hair in dermatoscopy and clinical benefit of an automated hair removal algorithm

Contact Info

Product

Resources

About