<scp>SLICACO</scp>: An automated novel hybrid approach for dermatoscopic melanocytic skin lesion segmentation

Singh, Lokesh; Janghel, Rekh Ram; Sahu, Satya Prakash

doi:10.1002/ima.22591

Cited by 16 publications

(12 citation statements)

References 43 publications

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“…As methods [ 5 , 8 , 11 – 14 , 19 , 23 ] do not deal with images containing artifacts like hairs, and borders and the PH2 dataset contains images with borders and hairs, therefore their results’ accuracy in term of Jaccard index and dice index are not satisfactory.…”

Section: Resultsmentioning

confidence: 99%

“…In [ 11 ] the proposed method used the fusion threshold method for lesion border detection. This fusion threshold method joins four different threshold methods, i.e., Huang & Wang’s fuzzy similarity method [ 18 ], Kapur et al’s maximum entropy method (LIANG-KAI HUANG et al 1995), Kittler & Illingworth’s minimum error thresholding method [ 23 ], and Otsu’s clustering-based method [ 24 ], and the goal of this is to obtain best results of the segmentation that is independent of image statistical properties. The proposed method uses only the blue color channel from the RGB color space and applies each threshold method of the ensemble to the blue color channel image to generate the set of threshold images.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 23 ], the hair is removed from the dermoscopic image first. The SLIC is then employed on the hair free image to generate similar clusters.…”

Section: Related Workmentioning

confidence: 99%

“…The methods [ 10 , 13 , 19 , 23 ] remove hairs from the dermoscopic images first and then segment the lesion from the dermoscopic. However, they blur the dermoscopic image after hair removal which in turn degrade the segmentation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning based skin lesion segmentation method with novel borders and hair removal techniques

et al. 2022

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The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The evaluation results show that our proposed skin lesion segmentation method obtained Jaccard Index of 0.77, 0.80 and Dice index of 0.87, 0.82 values on PH2, and ISIC2018 datasets, respectively, which are better than state-of-the-art skin lesion segmentation techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In [ 23 ], the hair is removed from the dermoscopic image first. The SLIC is then employed on the hair free image to generate similar clusters.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning based skin lesion segmentation method with novel borders and hair removal techniques

et al. 2022

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“…We utilized SLICACO, 25 a method based on super‐pixels for segmenting an image. The approach combines simple linear iterative clustering (SLIC) and ant colony optimization (ACO) methods.…”

Section: Proposed Frameworkmentioning

confidence: 99%

A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection

Singh

Janghel

Sahu

2021

Int J Imaging Syst Tech

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A computer-aided-diagnostic system for diagnosing melanoma often uses distinct kinds of features for characterizing the lesions. Extracting distinct features from melanocytic images represents the various characteristics of pigmented lesions. Concatenating such features distinguishes extracted feature's information effectively while eliminating the redundant information amid them, which aids in discriminating the cancerous and noncancerous lesions. This article proposes a framework comprising segmentation, feature extraction, feature fusion, and classification to differentiate benign lesions from melanoma. The proposed framework is four-fold: beginning with the extraction of ROI from an image, the SLICACO method is used for segmentation. Thereafter, ABCD rule-based global and local features are extracted for effective melanoma detection. Further, we develop a new hybrid feature fusion strategy, PCAFA, leveraging the benefits of principal component analysis and factor analysis. The method performs early fusion by combining all extracted features within an individual feature vector and fed to a learning model for the prediction. While late fusion is performed using majority voting by combining the outputs of machine learning models. After that, gradient tree boosting, support vector machine, and decision tree models are trained to utilize distinct features of skin lesions for their classification as benign or malignant. The effectiveness of the designed framework is validated on the ISIC2017 benchmark skin lesion dataset based on specificity, sensitivity, and accuracy. The generalizability of the designed framework is gauged by conducting a fair comparison with conventional methods. Evaluated results reveal the potential of the proposed fused feature-set in discriminating malignant and nonmalignant lesions with an accuracy of 96.8%.

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A region of interest focused Triple UNet architecture for skin lesion segmentation

Liu,

Guo,

Jin

et al. 2024

Int J Imaging Syst Tech

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Skin lesion segmentation is a crucial step for skin lesion analysis and subsequent treatment. However, it is still a challenging task due to the irregular and fuzzy lesion borders, and the diversity of skin lesions. In this article, we propose Triple‐UNet, an organic combination of three UNet architectures with suitable modules, to automatically segment skin lesions. To enhance the target object region of the image, we design a region of interest enhancement module (ROIE) that uses the predicted score map of the first UNet. The enhanced image and the features learned by the first UNet help the second UNet obtain a better score map. Finally, the results are fine‐tuned by the third UNet. We evaluate our algorithm on a publicly available dataset of skin lesion segmentation. Experiments have shown that TripleUNet achieves an accuracy of 92.5% on the ISIC‐2018 skin lesion segmentation benchmark, with Dice and mIoU of 0.909 and 0.836, respectively, which outperforms the state‐of‐the‐art algorithms.

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SLICACO: An automated novel hybrid approach for dermatoscopic melanocytic skin lesion segmentation

Cited by 16 publications

References 43 publications

Machine learning based skin lesion segmentation method with novel borders and hair removal techniques

Machine learning based skin lesion segmentation method with novel borders and hair removal techniques

A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection

A region of interest focused Triple UNet architecture for skin lesion segmentation

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