Hair Artifact Removal and Skin Lesion Segmentation of Dermoscopy Images

Salido, Julie Ann A.; Ruiz, Conrado

doi:10.22159/ajpcr.2018.v11s3.30025

Cited by 19 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Morphological operations [18,19] More suitable to reduce the high level of noise. Preserve critical information.…”

Section: Methodsmentioning

confidence: 99%

“…The major problem in skin cancer images is hairlines, as these occlude essential information such as color, texture, and boundary. 18 presented morphological filters to remove noise from medical images effectively. They used a collection of nonlinear operations to extract shape-related image components such as boundaries, skeletons, and convex hulls.…”

Section: Preprocessing Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

Kaur

GholamHosseini

Sinha

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Automatic lesion segmentation is a key phase of skin lesion analysis that significantly increases the performance of subsequent classification steps. Segmentation is a highly complex task due to the varying nature of lesions, such as unique shapes, different colors, and structures. In this study, a two-step system is proposed comprising preprocessing algorithm and lesion segmentation network. The hairlines removal algorithm is designed using morphological operators to eliminate noise artifacts. The resulting output images are fed to the convolutional neural network (CNN) to perform lesion segmentation. The proposed CNN network is a new framework designed from scratch based on encoder-decoder architecture. The layers are stacked in a unique sequence to perform downsampling and upsampling, generating a high-resolution segmentation map. Additionally, a Tversky loss function is implemented to reduce the error rate between predicted and target output. In this study, we focus on the challenges related to the extraction of accurate lesion regions and the presence of hairlines in lesion images that can occlude important information resulting in poor segmentation. The proposed model is evaluated on four publicly available datasets, namely ISIC 2016-2018. The mean intersection over union (IoU) obtained for ISIC 2016-2018 and PH 2 is 87.1%, 77.8%, 85.2%, and 88.8%, respectively. The proposed model demonstrated higher performance as compared to other state-of-the-art methods. The hair removal step aids in the improvement of the overall performance.

show abstract

“…Morphological operations [18,19] More suitable to reduce the high level of noise. Preserve critical information.…”

Section: Methodsmentioning

confidence: 99%

Section: Preprocessing Techniquesmentioning

confidence: 99%

Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

Kaur

GholamHosseini

Sinha

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…To automatically remove hair from the images hair detection is needed and subsequently is followed by inpainting of the images. In our implementation to detect the pixels of the dermatoscopic images that contained natural hair we utilized a median filter for smoothing and then applied bottom-hat filtering as in [23]. The bottom-hat (also referred to as black top-hat) filter is performing an image transformation, based on a morphological operation which is defined as…”

Section: 1mentioning

confidence: 99%

Deep Learning for Cancer Diagnosis

Naronglerdrit¹,

Mporas²

2021

Studies in Computational Intelligence

View full text Add to dashboard Cite

This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is based on convolutional neural networks and it is evaluated using new CNN models as well as retrained modification of pre-existing CNN models were used. The experimental results showed that CNN models pre-trained on big datasets for general purpose image classification when re-trained in order to identify skin lesion types offer more accurate results when compared to convolutional neural network models trained explicitly from the dermatoscopic images. The best performance was achieved by retraining a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13% and 82.88%, respectively.

show abstract

“…etc. 14 The presence of hair in dermoscopy images leads to the occlusion of the information of the lesion concerning its texture and boundary. 15 Hence, it is important to eliminate this artifact in the preprocessing step before carrying out lesion segmentation.…”

Section: Introductionmentioning

confidence: 99%

Hair removal in dermoscopy images using variational autoencoders

Bardou

Bouaziz

et al. 2022

Skin Research and Technology

View full text Add to dashboard Cite

Background: In recent years, melanoma is rising at a faster rate compared to other cancers. Although it is the most serious type of skin cancer, the diagnosis at early stages makes it curable. Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin. In the last few decades, digital imaging devices have made great progress which allowed capturing and storing high-quality images from these examinations. The stored images are now being standardized and used for the automatic detection of melanoma. However, when the hair covers the skin, this makes the task challenging. Therefore, it is important to eliminate the hair to get accurate results. Methods:In this paper, we propose a simple yet efficient method for hair removal using a variational autoencoder without the need for paired samples. The encoder takes as input a dermoscopy image and builds a latent distribution that ignores hair as it is considered noise, while the decoder reconstructs a hair-free image. Both encoder and decoder use a decent convolutional neural networks architecture that provides high performance. The construction of our model comprises two stages of training. In the first stage, the model has trained on hair-occluded images to output hair-free images, and in the second stage, it is optimized using hair-free images to preserve the image textures. Although the variational autoencoder produces hair-free images, it does not maintain the quality of the generated images. Thus, we explored the use of three-loss functions including the structural similarity index (SSIM), L1-norm, and L2-norm to improve the visual quality of the generated images. Results:The evaluation of the hair-free reconstructed images is carried out using tdistributed stochastic neighbor embedding (SNE) feature mapping by visualizing the distribution of the real hair-free images and the synthesized hair-free images. The conducted experiments on the publicly available dataset HAM10000 show that our method is very efficient.

show abstract

Hair Artifact Removal and Skin Lesion Segmentation of Dermoscopy Images

Cited by 19 publications

References 25 publications

Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

Deep Learning for Cancer Diagnosis

Hair removal in dermoscopy images using variational autoencoders

Contact Info

Product

Resources

About