Border detection in dermoscopy images using hybrid thresholding on optimized color channels

Garnavi, Rahil; Aldeen, M.; Celebi, M. Emre; Varigos, George; Finch, Sue

doi:10.1016/j.compmedimag.2010.08.001

Cited by 173 publications

(116 citation statements)

References 17 publications

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“…Thresholding techniques use low-level features, including intensity and color to separate the normal skin and cancerous regions. Garnavi et al [6] applied Otsu's method to identify the core-lesion; nevertheless, such process is disposed to skin tone variations and lighting. Moreover, dermoscopic images include some artifacts due to water bubble, dense hairs, and gel that are a great challenge for accurate detection.…”

Section: Introductionmentioning

confidence: 99%

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

2018

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This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach.

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Section: Introductionmentioning

confidence: 99%

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

2018

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“…Color is one of the most important cues that people use extensively to identify real world objects. It is widely used in medical image analysis for screening dermoscopic images in order to discriminate between healthy skin and unhealthy skin regions [39]. The basic assumption in most cases of dermoscopic image analysis is that the lighter shade of color corresponds to healthy skin, while unhealthy skin possesses different color distribution that differs from the healthy skin [40].…”

Section: Saliency Based Segmentationmentioning

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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“…For every suggested threshold value, the method evaluates the feasibility of this value if used as the threshold. Garnavi et al 1 proposed an automatic segmentation procedure using clustering histogram thresholding and then used Otsu's method to separate lesion and healthy skin region from images. Pre-processing between 92 and 96%.…”

Section: Introductionmentioning

confidence: 99%

“…Melanoma is the most deadly form of skin cancer as it can develop quickly to spread to other organs and lymph nodes 1 . According to the American Cancer Society, the 10-year survival rates of this type of cancer can be as high as 86% if found during stage I.…”

Section: Introductionmentioning

confidence: 99%

Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

Al-abayechi¹,

Guo²,

Tan³

et al. 2014

ScienceAsia

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This article provides an improved automated skin lesion segmentation method for dermoscopic images. There are several stages for this method. These include the pre-processing steps such as resizing the images and eliminating noise. Hair was removed and reflective light was reduced using morphological operations and a median filter. The single green channel was rescaled into new intensities, as it provided the highest segmentation accuracy. The threshold value was calculated to separate the skin lesion region from healthy skin. Morphological operations were implemented to merge the small lesion areas around the bigger lesion areas with similar features and trace the boundary of the melanoma. The accuracy of the segmentation was evaluated by comparing the automatic boundary and manual boundary. Compared to other studies, our proposed method achieved the highest average accuracy of 97%.

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Border detection in dermoscopy images using hybrid thresholding on optimized color channels

Cited by 173 publications

References 17 publications

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

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

Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

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