Multilevel thresholding for segmentation of pigmented skin lesions

Humayun, Jawad; Malik, Aamir Saeed; Kamel, Nidal

doi:10.1109/ist.2011.5962214

Cited by 17 publications

(9 citation statements)

References 8 publications

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“…Dataset comprised of more than 1000 pictures, out of that 388 were melanoma and the staying 912 were named generous. Hum et al [11] exhibited a several limit calculation to isolate the graphic representation of derma pictures by methods for cycle on various classes by edge determination for each class using distinctive system. Ge [12] implemented to utilize two form fusing system to separate the neighborhood highlights of VGG organize, after that joined it with worldwide highlights removed by the profound lingering system (ResNet) .…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Detection Based on Fuzzy Logic

Leo¹,

Yokesh²,

Karthikeyan³

et al. 2019

IJITEE

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Melanoma is the maximal destructive type of skin disease. Occurrence ratios of melanoma seemed expanding, particularly with non-Hispanic white guys and females, however durability values are more whenever identified early. Requirement of cost as well as time for the imates to go for dermatologist for melanoma are restrictively exorbitant. Test in actualizing such a framework is finding the derma sore in the computerized picture. An arrangement of agent surface appropriations are found out from an enlightenment remedied photo and surface peculiarity metric is ascertained for every circulation. Melanoma represents around 75% of passings related with skin tumor. The fundamental objective of our work is to build up a structure that naturally right and section the skin injury from an information photo. This philosophy utilizes surface peculiarity injury division fluffy calculation to distinguish the injuries in the skin. The upside of this undertaking is early recognition of the sore to dodge skin tumor at later stages Index Terms: Skin

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Section: Related Workmentioning

confidence: 99%

Skin Lesion Detection Based on Fuzzy Logic

Leo¹,

Yokesh²,

Karthikeyan³

et al. 2019

IJITEE

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“…There are two major types of skin cancer, name malignant melanoma and non-melanoma (basal cell, squamous cell, and Markel cell carcinomas, etc.) [7].Melanoma is more dangerous and can be fatal if not treated. If melanoma is detected in its early stages, it is highly curable,yet advanced melanoma is lethal.…”

Section: Problem Statementmentioning

confidence: 99%

“…Existing automated melanoma detection systems can be used for dermoscopic images. Segmentation is the important step in automated melanoma detection systems to get an accurate result by analyzing only the segmented lesion area.Existing Melanoma detection systems make use of Statistical Region Merging (SRM)[5], Iterative Stochastic Region Merging[6], Multilevel Thresholding[7] and Color Enhancement and Iterative Segmentation[8]. SRM can be used only for dermoscopic images.…”

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confidence: 99%

Neural Network Based Detection of Melanoma Skin Cancer

2016

IJSR

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Skin cancer is the deadliest form of cancers in humans. It is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most unpredictable. Detection of melanoma skin cancer in the earlier stage is very critical. In this paper, we explain the method for the detection of Melanoma Skin Cancer using Segmentation. The input to the system is the Dermoscopic Image and then apply and then by applying novel image processing techniques. The different stages of detection involves-collection of Dermoscopic Images, filtering the images by using Dull Razor filtering for removing hairs and air bubbles in the image, converting to gray scale, contrast enhancement, noise filtering, segmenting the images using Maximum Entropy Threshold. Feature Extraction technique used is Gray Level Co-occurrence Matrix (GLCM).It is a powerful tool for image feature extraction by mapping the gray level co-occurrence probabilities based on spatial relations of pixels in different angular directions. They are mean, standard deviation, Skewness, Kurtosis, Contrast, Energy and Homogeneity. Mean or expected value provides a measure of distribution. The sensitivity and specificity for diagnosis of melanoma achieved by neural network analysis of Raman spectra were 85% and 99%, respectively. We propose that neural network analysis of near-infrared Fourier transform Raman spectra could provide a novel method for rapid, automated skin cancer diagnosis on unstained skin samples.

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“…In this paper, the proposed method involves four popular thresholding algorithms, because there are a variety of dermoscopic images that require different treatments. Some of these algorithms are presented in [4][5][6][7], and a multilevel thresholding algorithm reported in [8]. Moreover, other authors [9] proposed a color image segmentation technique based on region growing and merging that is compared with four widely reported segmentation methods.…”

Section: Introductionmentioning

confidence: 99%