Dullrazor®: A software approach to hair removal from images

Lee, Tim; Ng, Vincent; Gallagher, Richard P.; Coldman, Andrew J.; McLean, David I.

doi:10.1016/s0010-4825(97)00020-6

Cited by 388 publications

(250 citation statements)

References 7 publications

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“…The luminance component L p made the difference between hair and pigmented structures more pronounced, and it was not necessary to add any further processing step, as is the case in Ref. [22]. Figs.…”

Section: Hair Removalmentioning

confidence: 95%

“…A scheme to remove hair from images was proposed by Lee et al [22]. They argued that a software solution to the hair problem is better than shaving the lesion area before the acquisition.…”

Section: Hair Removalmentioning

confidence: 99%

“…Lesions that are almost entirely covered with hair, as some of those shown in Ref. [22], are useless for diagnosis and should be rejected. The presence of some hair, however, is difficult to avoid, and the scheme we suggest is intended to solve the hair problem in those situations.…”

Section: Hair Removalmentioning

confidence: 99%

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Towards a computer-aided diagnosis system for pigmented skin lesions

Schmid-Saugeona

Guillodb

Thiran

2003

Computerized Medical Imaging and Graphics

192

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This paper presents a computer-aided diagnosis system for pigmented skin lesions, with solutions for the lesion boundary detection and for the quantification of the degree of symmetry. Lesion detection results were validated by expert dermatologists, who also provided handdrawn boundaries of the lesions. These reference boundaries were not used as a gold standard, but were allowed to statistically determine the accuracy of the boundaries provided by computerized techniques. We could show that the dermatologists were not able to reproduce their results, and that the boundaries of any expert taken alone showed higher divergence from those of the set of remaining experts than the automatic techniques we developed. Feature extraction is restricted in this paper to the quantification of degree of symmetry, even though it is clear that many other features will be necessary for a complete diagnosis system. The symmetry quantification step provides a sixdimensional feature vector that can be used to classify pigmented skin lesions as being benign or malignant. We demonstrate that our scheme outperforms methods based on the principal component decomposition, which is widely used for this kind of application. q

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Section: Hair Removalmentioning

confidence: 95%

“…A scheme to remove hair from images was proposed by Lee et al [22]. They argued that a software solution to the hair problem is better than shaving the lesion area before the acquisition.…”

Section: Hair Removalmentioning

confidence: 99%

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Towards a computer-aided diagnosis system for pigmented skin lesions

Schmid-Saugeona

Guillodb

Thiran

2003

Computerized Medical Imaging and Graphics

192

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“…DullRazor, the first and arguably best known method, finds dark hairs on light skin by a generalized morphological closing using three structuring elements that model three line orientations [19]. Different morphological operators were used in [23,20].…”

Section: Related Workmentioning

confidence: 99%

“…Several digital hair removal (DHR) methods address this by finding and replacing such hairs by plausible colors based on surrounding skin. Despite much work in this area [19,30,15,12,2,13], DHR methods are challenged by hairs which are thin, entangled, of similar contrast or color to underlying skin, or overlaid on a highly-textured skin structure.…”

Section: Introductionmentioning

confidence: 99%

Automated Digital Hair Removal by Threshold Decomposition and Morphological Analysis

Koehoorn

Sobiecki

Boda

et al. 2015

Lecture Notes in Computer Science

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Abstract. We propose a new approach to digital hair removal from dermoscopic images, based on a threshold-set model. For every threshold, we adapt a recent gapdetection algorithm to find hairs, and merge results in a single mask image. Next, we find hairs in this mask by combining morphological filters and medial descriptors. We derive robust parameter settings for our method based on its application on over 300 skin images. We detail a GPU implementation of our method and show how it compares favorably with five existing digital hair removal methods.

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Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures

Wahba¹,

Ashour²

2020

Engineering and Technology for Healthcare

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Dullrazor®: A software approach to hair removal from images

Cited by 388 publications

References 7 publications

Towards a computer-aided diagnosis system for pigmented skin lesions

Towards a computer-aided diagnosis system for pigmented skin lesions

Automated Digital Hair Removal by Threshold Decomposition and Morphological Analysis

Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures

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