2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) 2017
DOI: 10.1109/cbms.2017.39
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Automatic Segmentation of Melanoma in Dermoscopy Images Using Fuzzy Numbers

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Cited by 10 publications
(4 citation statements)
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“…Consequently for each pixel value (p i,j ) of the image, it becomes possible to compute the membership value of each pixel related to its referenced neighbourhood pixels. This method is different from classical methods, such as from the thresholding approaches because it sets a membership level, a value between 0 and 1, to the region [40].…”
Section: Lesion Mask Generation and Fuzzy-image Segmentationmentioning
confidence: 99%
“…Consequently for each pixel value (p i,j ) of the image, it becomes possible to compute the membership value of each pixel related to its referenced neighbourhood pixels. This method is different from classical methods, such as from the thresholding approaches because it sets a membership level, a value between 0 and 1, to the region [40].…”
Section: Lesion Mask Generation and Fuzzy-image Segmentationmentioning
confidence: 99%
“…In [6], image-wise supervised learning is proposed to derive a probabilistic map for automated seed selection and multi-scale super-pixel based cellular automata to acquire structural information for skin lesion region segmentation. A Guassian membership function is applied for image fuzzification and to quantify each pixel for skin segmentation [12].…”
Section: Literaturementioning
confidence: 99%
“…It also proposed the use of Bayesian Decision Fusion of a multiple of classifier to increase the melanoma detection rates. The relationship between confidence distribution and accuracy and resulted incomparable confidence intervals with stable recognition rate C. Jessica B.Diniz et al [3] proposed that automatic melanoma segmentation approach based on Fuzzy Numbers. The proposed approach was compared with three state-of-art technique and was evaluated through the metrics of sensitivity, specificity, Jaccard index and balanced accuracy.…”
Section: Methodsmentioning
confidence: 99%
“…Colour tan to brown, maybe flashy or pink -Shape Borders often oval or round, but maybe irregular, often sharply demarcated but will appear as gradually fading into surrounding skin in fair persons. -Surface Rough, raised surface and frequently sharp border -Size Usually 5 -15 mm -Location Face, neck and trunk Moreover, a kind of precursor lesion that may turn into a melanoma mole [3] is 0 Dysplastic Nevus has characteristics: -Colour Mixture of tan or brown, black and red/pink -Shape Irregular borders that may include notches. May fade into surrounding skin and include a flat portion level with the skin.…”
mentioning
confidence: 99%