Effcient and Effective Automated Digital Hair
Removal from Dermoscopy Images

Koehoorn, Joost; Sobiecki, André; Rauber, Paulo E.; Jalba, Andrei C.; Telea, Alexandru

doi:10.1515/mathm-2016-0001

Cited by 7 publications

(6 citation statements)

References 34 publications

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“…Preprocessing is a crucial step of image processing ( 29 ) in order to obtain accurate outcomes. As there are hairs in a number of the images which could interfere with accurate classification, a digital hair removal (DHR) algorithm ( 30 ) is applied next. Next, we apply the rolling ball technique ( 31 ) in order to remove background noise.…”

Section: Methodsmentioning

confidence: 99%

“…As artifacts can be the root cause of poor results, removal of the main artifact for skin cancer detection, hairs, is essential. We remove hairs ( 42 ) from our images by applying the DHR algorithm ( 30 ). This consists of four steps: Grayscale, Morphological BlackHat transformation, creating the mask for InPainting, and the InPainting algorithm.…”

Section: Methodsmentioning

confidence: 99%

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SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

et al. 2022

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Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method.Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

et al. 2022

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“…For training and validation, lesion images with hair, Fig. 3-(a), are fed into Virtual shaver, proposed by Koehoorn et al for hair mask preparation [29]. The extracted hair masks, Fig.…”

Section: Methodsmentioning

confidence: 99%

Realistic Hair Simulator for Skin lesion Images Using Conditional Generative Adversarial Network

Attia¹,

Hossny²,

Zhou³

et al. 2018

Preprint

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Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and healthcare practitioners. Skin lesion restoration is an essential preprocessing step for lesion enhancements for accurate automated analysis and diagnosis. Digital hair removal is a non-invasive method for image enhancement by solving the hair-occlusion artefact in previously captured images. Several hair removal methods were proposed for skin delineation and removal. However, manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or using benchmarking datasets for comparison purposes. In the presented work, we propose a realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with the ground-truth mask for hair location. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximise the synthesis quality. For quantitatively evaluate the realism of image synthesis, the t-SNE feature mapping and Bland-Altman test are employed as objective metrics. Experimental results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture.

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“…To remove artifacts from dermoscopic images, the fast marching method (FMM) was applied at each stage while preserving morphological features during artifacts removal. A threshold set model for digital hair removal from dermoscopic images proposed by Okuboyejo et al and Koehoorn et al [21,22]. They proposed a gap-detection algorithm to find hairs for every threshold and merge results in a single mask image.…”

Section: Related Workmentioning

confidence: 99%

“…Hair removal is an important step in dermoscopy images to classify the skin lesion correctly into benign, suspicious, or malignant. Various techniques were applied to remove hairs automatically from dermoscopic images are discussed in detail by [11,12]. The rest of this research is organized as follows: Section 2 describes an overview of related work.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Block-Based Algorithm for Hair Removal in Dermoscopic Images

Zaqout¹

2020

Computer Methods and Programs in Biomedical Signal and Image Processing

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Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by Dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 nonoverlapped blocks and for each block, white pixels are replaced by locating the highest peak of using a histogram function and a morphological close operation. Our proposed algorithm reports a true positive rate (sensitivity) of 97.36%, a false positive rate (fall-out) of 4.25%, and a true negative rate (specificity) of 95.75%. The diagnostic accuracy achieved is recorded at a high level of 95.78%.

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Effcient and Effective Automated Digital Hair Removal from Dermoscopy Images

Cited by 7 publications

References 34 publications

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

Realistic Hair Simulator for Skin lesion Images Using Conditional Generative Adversarial Network

An Efficient Block-Based Algorithm for Hair Removal in Dermoscopic Images

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