Adaptation and Evaluation of Deep Learning Techniques for Skin Segmentation on Novel Abdominal Dataset

Topiwala, Anirudh; Al-Zogbi, Lidia; Fleiter, Thorsten; Krieger, Axel

doi:10.1109/bibe.2019.00141

Cited by 22 publications

(15 citation statements)

References 26 publications

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“…A novel abdominal skin dataset created from Google images is presented in [9]. It consists of 1400 manually segmented images representing the abdomen of various ethnic groups.…”

Section: Related Workmentioning

confidence: 99%

Deep Semantic Segmentation in Skin Detection

Cuza¹,

Loreggia²,

Lumini³

et al. 2022

ESANN 2022 Proceedings

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Deep semantic segmentation is a task that identifies objects and their boundaries in images, to do that a classification task is performed at the pixel level to tag whether a pixel belongs to an object. In skin detection, areas of images are classified as skin or non-skin regions. In this work, we report a short survey of the recent literature covering the task to help researchers in selecting the most suitable method for their application and to expand the knowledge about the available datasets for this topic. A compact empirical evaluation comparing recent models and a new ensemble model is reported.

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“…A novel abdominal skin dataset created from Google images is presented in [9]. It consists of 1400 manually segmented images representing the abdomen of various ethnic groups.…”

Section: Related Workmentioning

confidence: 99%

Deep Semantic Segmentation in Skin Detection

Cuza¹,

Loreggia²,

Lumini³

et al. 2022

ESANN 2022 Proceedings

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“…Under the encoder-decoder category, Nguyen et al [31] modified the original SegNet [3] architecture by increasing the number of decoders, thereby allowing each encoder to perform multiple tasks at the same time, which discriminate skin components in the hand area more accurately. Topiwala [42] has shown that U-Net stands out among the frequently-used skin detectors on their dataset of the human abdomen with different skin colors, The method based on U-Net was also computationally faster. Tarasiewicz [41] refined the U-Net architecture [34] by considering largescale contextual features, using inception and dense blocks to reduce occurrences of false positives significantly while doing skin detection.…”

Section: Related Work 21 Skin Detection For Natural Imagesmentioning

confidence: 99%

Color Invariant Skin Segmentation

Han¹,

Sarkar²,

Abbott³

2022

Preprint

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This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, with the goal of color invariant feature learning to enhance generalization. We have demonstrated the concept using two architectures, and experimental results show improvements in both precision and recall for most Fitzpatrick skin tones in the benchmark ECU dataset. We further tested the system with the RFW dataset to show that the proposed method performs much more consistently across different ethnicities, thereby reducing the chance of bias based on skin color. To demonstrate the effectiveness of our work, extensive experiments were performed on grayscale images as well as images obtained under unconstrained illumination and with artificial filters.

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“…The literature on image processing and computer vision has reported advances in the use of several lesion segmentation approaches, such as edge-based [8], region-based [7,9], contour-based [10], texture-aware [11], thresholding [12,13], clustering [14,15], and, recently, deep learning [16][17][18]. The edge-based image segmentation methods typically rely on edge operators such as Laplacian of Gaussian (LOG) and Canny to retrieve relevant edge information that can assist in boundary tracing.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors

Okuboyejo

Olugbara

2021

Diagnostics

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The early detection of skin cancer, especially through the examination of lesions with malignant characteristics, has been reported to significantly decrease the potential fatalities. Segmentation of the regions that contain the actual lesions is one of the most widely used steps for achieving an automated diagnostic process of skin lesions. However, accurate segmentation of skin lesions has proven to be a challenging task in medical imaging because of the intrinsic factors such as the existence of undesirable artifacts and the complexity surrounding the seamless acquisition of lesion images. In this paper, we have introduced a novel algorithm based on gamma correction with clustering of keypoint descriptors for accurate segmentation of lesion areas in dermoscopy images. The algorithm was tested on dermoscopy images acquired from the publicly available dataset of Pedro Hispano hospital to achieve compelling equidistant sensitivity, specificity, and accuracy scores of 87.29%, 99.54%, and 96.02%, respectively. Moreover, the validation of the algorithm on a subset of heavily noised skin lesion images collected from the public dataset of International Skin Imaging Collaboration has yielded the equidistant sensitivity, specificity, and accuracy scores of 80.59%, 100.00%, and 94.98%, respectively. The performance results are propitious when compared to those obtained with existing modern algorithms using the same standard benchmark datasets and performance evaluation indices.

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Adaptation and Evaluation of Deep Learning Techniques for Skin Segmentation on Novel Abdominal Dataset

Cited by 22 publications

References 26 publications

Deep Semantic Segmentation in Skin Detection

Deep Semantic Segmentation in Skin Detection

Color Invariant Skin Segmentation

Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors

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