Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata

Bi, Lei; Kim, Jin Man; Ahn, Euijoon; Feng, Dagan; Fulham, Michael

doi:10.1109/isbi.2016.7493448

Cited by 64 publications

(38 citation statements)

References 11 publications

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“…Here, the RGB channels of the images were considered as the input wavelets and an orthogonal least squares algorithm was used to compute network weights and optimize the network structure. Bi et al [24] recently proposed a cellular automata (CA) based approach for lesion segmentation. They used image-wise learning technique to derive a probabilistic map for automated seed selection.…”

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confidence: 99%

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

Ahn

Kim

et al. 2017

IEEE J. Biomed. Health Inform.

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141

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The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e., no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.

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confidence: 99%

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

Ahn

Kim

et al. 2017

IEEE J. Biomed. Health Inform.

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141

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“…This study applies the precision ( ), recall ( ), accuracy ( ), and dice ( ) evaluation metrics to quantitatively score the binary segmentation results computed by the comparative algorithms. These evaluation metrics are widely used for judging the performance of binary segmentation algorithms [8,13,19,20,47,48,61,[83][84][85]. A binary segmentation algorithm with satisfactory performance has high precision, recall, accuracy, and dice values.…”

Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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“…and especially pays attention to evaluation aspects and computational issues. Lei Bi et al [35] suggested a new automated method that performed image segmentation using image-wise supervised learning (ISL) and multiscale super pixel based cellular automata (MSCA). The authors used probabilistic mapping for automatic seed selection that removes user-defined seed selection; afterward, the MSCA model was employed for segmenting skin lesions.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Zafar

Gilani

Waris

et al. 2020

Sensors

109

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Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.

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Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata

Cited by 64 publications

References 11 publications

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

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