Skin cancer extraction with optimum fuzzy thresholding technique

Lee, Howard; Chen, Yi‐Ping Phoebe

doi:10.1007/s10489-013-0474-0

Cited by 23 publications

(13 citation statements)

References 39 publications

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“…Fuzzy c-means (FCM) uses the membership function to segment the images into one or several regions. Lee and Chen [12] proposed a segmentation technique on different skin cancer types using classical FCM clustering. An optimum threshold-based segmentation technique using type-2 fuzzy sets was applied to outline the skin cancerous areas.…”

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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“…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%

“…It is also worth noting that, although most research papers used variations of the IT2 FCM clustering algorithm, some papers solely used IT2 FLSs as some type of expert system [20,23,27,28,31]. Some even used hybrid versions of IT2 FLS or IT2 FCM, such as Interval-Valued Possibilistic Fuzzy C-Means (IVPFCM) [7] and Extended Type-2 Fuzzy Information Theory (ET2FIT) [21].…”

Section: Discussionmentioning

confidence: 99%

“…In Howard Lee and Yi-Ping Phoebe Chen (2014) [20], an optimum threshold image segmentation approach based on T2 FS was proposed, where this technique is applied to the preprocessed image obtained from a 3D color algorithm in order to improve the detection of variations in the image. This approach was directly compared to the Otsu algorithm when using skin cancer images and showed significant improvement in detection.…”

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

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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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“…In order to improve this accuracy it is being proven useful the exploitation of machine learning methods, such as support vector machines [24] or fuzzy clustering, which have been used in many different research fields, and they have clearly proved they success in contexts of biological relevance such as bioinformatics and biomedicine [25][26][27].…”

Section: Editorialmentioning

confidence: 99%