Accurate Segmentation of Dermoscopic Images by Image Thresholding Based on Type-2 Fuzzy Logic

Yüksel, Mehmet Emin; Borlu, Murat

doi:10.1109/tfuzz.2009.2018300

Cited by 127 publications

(59 citation statements)

References 38 publications

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“…IVFSs can be used for representing those areas of an image where experts have problems to build the fuzzy membership degrees. In this case, The use of IVFSs leads to improved segmentation or detection of features [11], [30], [31], [32], [59], [109], [136], [162]. For some decision making problems, the use of IVFSs allows to choose a solution when FSs fail to do so [36].…”

Section: -Measures Yielding Intervals From the Definition Ofmentioning

confidence: 99%

A Historical Account of Types of Fuzzy Sets and Their Relationships

Bustince

Barrenechea

Pagola

et al. 2016

IEEE Trans. Fuzzy Syst.

406

159

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Section: -Measures Yielding Intervals From the Definition Ofmentioning

confidence: 99%

A Historical Account of Types of Fuzzy Sets and Their Relationships

Bustince

Barrenechea

Pagola

et al. 2016

IEEE Trans. Fuzzy Syst.

406

159

View full text Add to dashboard Cite

show abstract

“…First, by [19,21,28], such that [19] and [21] both are direct applications to a humanoid robot vision system, although both focused the use of IT2 FLS in different ways, as the first was used for object sample selection and the later was used for feature validity; and [28] is an application in auroral image segmentation. Secondly, the rest of the papers focused their algorithms to specifically solve medical imaging databases of different natures [18,20,22,27,29,31,33], using image segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…In M. Emin Yuksel and Murat Borlu (2009) [27], a thresholding-based segmentation algorithm was proposed, where it utilizes T2 FLS for the automatic thresholding determination for accurate segmentation of pigmented skin lesion images. Experimentation showed the successful handling of uncertainty in determining borders between lesion and skin.…”

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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“…Yukse et al [8] proposed a method which used type-2 fuzzy logic technique for automatic threshold determination to detect the border of the pigmented skin lesion. Celebi et al [9] presented a method to segment skin lesion in dermoscopy images through statistical region merging method.…”

Section: Previous Workmentioning

confidence: 99%

“…Each pixel is assigned to a cluster based on the highest probability P(y|x) using equation (7). Then calculate the prior probability using equation (8). All pixels will be assigned again to different classes by getting the minimum cost, equation (10).…”

Section: Skin Lesion Segmentationmentioning

confidence: 99%

Detection of Melanoma Skin Cancer in Dermoscopy Images

Eltayef

Liu

2017

J. Phys.: Conf. Ser.

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Abstract. Malignant melanoma is the most hazardous type of human skin cancer and its incidence has been rapidly increasing. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. In this paper, we present a novel method for the detection of melanoma skin cancer. To detect the hair and several noises from images, pre-processing step is carried out by applying a bank of directional filters. And therefore, Image inpainting method is implemented to fill in the unknown regions. Fuzzy C-Means and Markov Random Field methods are used to delineate the border of the lesion area in the images. The method was evaluated on a dataset of 200 dermoscopic images, and superior results were produced compared to alternative methods.

show abstract

Accurate Segmentation of Dermoscopic Images by Image Thresholding Based on Type-2 Fuzzy Logic

Cited by 127 publications

References 38 publications

A Historical Account of Types of Fuzzy Sets and Their Relationships

A Historical Account of Types of Fuzzy Sets and Their Relationships

Review of Recent Type-2 Fuzzy Image Processing Applications

Detection of Melanoma Skin Cancer in Dermoscopy Images

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