Automatic Segmentation of Melanoma in Dermoscopy Images Using Fuzzy Numbers

Diniz, Jessica Barbosa; Cordeiro, Fernando

doi:10.1109/cbms.2017.39

Cited by 10 publications

(4 citation statements)

References 15 publications

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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%

Image Masking and Enhancement System for Melanoma Early Stage Detection

Yalcinkaya¹,

Erbas²

2022

Intelligent Automation &Amp; Soft Computing

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Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels' characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has two steps to produce high degree of accuracy; the first is the Fuzzy-Logic (FL) based maskProd and pixel Enhancement vectors and the second is integration with deep CNN algorithms. The vectors, maskProd and pixel Enhancement, are based on lesion masking and image enhancement via fuzzy logic as hSvMask production has three inputs; sChannel, entropyFilter and distan-ceVector. The originality of this paper is based on the regional enhancement of each pixel of the image by fuzzy-logic. To change the brightness of an image by the conventional methods (CM), it is required to multiply the value of all and each pixels of the image by the same constant or weight value in order to generate new values of the pixels. But in the proposed method, FL determines a special and specific constant or weight value for every single pixel of the image; called a variable weight value. Secondly via conventional or classical methods of image processing, mask production is realized based on the threshold value predetermined, and this threshold value decides whether the image lesion is melanoma or not. CM use constant weight values but in the method suggested, FL determines a specific variable weight value for every single pixel of the image. Secondly CMs realize the mask production using predetermined threshold values, but proposed method suggests a new way capable of detecting pixels in the lesion-free regions, to do that, a system using FL was developed for each and every single pixel via the data obtained from three sources: the s-channel of the HSV (Hue, Saturation and Value) colour space, distance vector, and image entropy vector. The proposed method and its integration with algorithms; such as Alex-

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Section: Lesion Mask Generation and Fuzzy-image Segmentationmentioning

confidence: 99%

Image Masking and Enhancement System for Melanoma Early Stage Detection

Yalcinkaya¹,

Erbas²

2022

Intelligent Automation &Amp; Soft Computing

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“…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%

A Deep Residual Architecture for Skin Lesion Segmentation

Venkatesh

Naresh

Little

et al. 2018

Lecture Notes in Computer Science

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In this paper, we propose an automatic approach to skin lesion region segmentation based on a deep learning architecture with multi-scale residual connections. The architecture of the proposed model is based on UNet [22] with residual connections to maximise the learning capability and performance of the network. The information lost in the encoder stages due to the max-pooling layer at each level is preserved through the multi-scale residual connections. To corroborate the efficacy of the proposed model, extensive experiments are conducted on the ISIC 2017 challenge dataset without using any external dermatologic image set. An extensive comparative analysis is presented with contemporary methodologies to highlight the promising performance of the proposed methodology.

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“…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%

Surveys on Detection of Melanoma through Image Processing Techniques

Shwetha¹

2018

IJRASET

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In order to achieve an effective way to identify malignant melanoma stage without performing more number of skin biopsies, digital image at an early analysis of the images is investigated. For the detection of malignant melanoma, appropriate analyses are done on the tumour images according to the clinical characteristics that early melanoma possesses. In the last stage, classification is done based on the results obtained from the above mentioned analyses. The tumours are classified as either "potential malignant melanoma" or "non-melanoma". This can reduce the time spent and pain received by the patients in detecting early malignant melanoma.

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Automatic Segmentation of Melanoma in Dermoscopy Images Using Fuzzy Numbers

Cited by 10 publications

References 15 publications

Image Masking and Enhancement System for Melanoma Early Stage Detection

Image Masking and Enhancement System for Melanoma Early Stage Detection

A Deep Residual Architecture for Skin Lesion Segmentation

Surveys on Detection of Melanoma through Image Processing Techniques

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