[PDF] Hair removal methods: A comparative study for dermoscopy images | Semantic Scholar (2024)

The preliminary results indicated the proposed method was able to remove more fine hairs and hairs in the shade, and lower false hair detection rate by 58% as compared to the DullRazor's approach.

A hair removal benchmark of six state-of-the-art algorithms, each with a different approach to segment and inpaint the hair pixels, and a series of performance measures that evaluate the similarity between the original hairless image and the one obtained by each of the algorithms.

A new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting is proposed that reports a true positive rate (sensitivity) and a true negative rate (specificity) of 95.75%.

This paper presents, for the first time, a hair detection and removal algorithm for 2D psoriasis images that overcomes the problem of removing skin hair without affecting the intensity or texture features of the lesions.

The proposed algorithm performs well in removing hairs compared to previous studies and improves classification performance and the integration of the suggested algorithm has led to improvements in the performance metrics of the AlexNet architecture.

This work presents a new approach for the task of hair removal on dermoscopic images based on deep learning techniques that relies on an encoder-decoder architecture, with convolutional neural networks, for the detection and posterior restoration of hair’s pixels from the images.

A straightforward approach to automatic hair and consequently noise removal is proposed, which starts with a median filter on each color space of RGB, a bottom hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels.

Statistical and quantitative analyses prove the reliability of the algorithm for incorporation in CAD systems and its role in providing robust and flexible segmentation.

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A new method to identify hair in dermoscopic images using a universal approach, which can segment both dark and light hair without prior knowledge of the hair type, which was not previously possible with previous hair segmentation algorithms.

A new scheme that automatically detects and removes hairs and ruler markings from dermoscopy images is presented, which addresses the issue of preserving morphological features during artifact removal and produces superior results compared to existing techniques.

This study aims to detect multiple borders from dermoscopy with increased sensitivity and specificity for the detection of early melanoma and other pigment lesions with the noise removal algorithm which may contribute to skin cancer classification.

A digital dermoscopy analyzer is used to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions and the good diagnostic performance and high speed in reading and analyzing lesions (real time) of this method constitute an important step in the direction of automated diagnosis of pigmentedskin lesions.

An unsupervised algorithm, called the Independent Histogram Pursuit, for segmenting dermatological lesions is proposed, which estimates a set of linear combinations of image bands that enhance different structures embedded in the image.

The automatic tumor area extraction algorithm successfully extracted the tumor in 199 cases, and a diagnostic classifier using these images was developed, and the features used in the melanoma-nevus classifier and the parallel ridge detector have significant overlap.

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