Unsupervised Perceptual Segmentation of Natural Color Images Using Fuzzy-Based Hierarchical Algorithm

Maeda, Junji; Kawano, A.; Saga, Sato; Suzuki, Yusuke

doi:10.1007/978-3-540-73040-8_47

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…In Maeda et al [10] and Silveira et al [12], the fuzzy method, combined with both splitting and merging techniques, was used to segment dermoscopy images. This combination, originally proposed by Maeda et al [87,88], generates an algorithm for the unsupervised perceptual segmentation of natural colour images using a fuzzybased homogeneity measure, which performs the fusion of colour and texture features. The algorithm includes four steps: simple splitting, local merging, global merging and boundary refinement.…”

Section: Segmentation Based On Artificial Intelligencementioning

confidence: 99%

Computational methods for the image segmentation of pigmented skin lesions: A review

Oliveira

Filho

et al. 2016

Computer Methods and Programs in Biomedicine

204

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Section: Segmentation Based On Artificial Intelligencementioning

confidence: 99%

Computational methods for the image segmentation of pigmented skin lesions: A review

Oliveira

Filho

et al. 2016

Computer Methods and Programs in Biomedicine

204

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“…Therefore, we will move an initial boundary pixel to the pixel in its neighborhood having a locally maximum gradient magnitude. Region boundaries in an image are best described by pixels with locally maximum gradient magnitudes [14][15][16][17]. Pixels with locally maximum gradient magnitudes can be determined without any user interaction; therefore, the process is automatic.…”

Section: Region Refinementmentioning

confidence: 99%

Automatic Segmentation of Skin Melanoma Images Using Hybrid Method

Kaur¹,

Joshi²

2017

IJARCSSE

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Melanoma is a cancerous lesion in the pigment-bearing basal layers of the epidermis and is the most deadly form of skin cancer, yet it is also the most treatable, with a cure rate for early-stage melanoma of almost 100%. Therefore, there is a need to develop computer-aided diagnostic systems to facilitate the early detection of melanoma. The first step in these systems is skin lesion segmentation. The next essential step is feature extraction and pattern analysis procedures to make a diagnosis. According to the literature, pigment network or reticular pattern is an important diagnostic parameter for melanoma. We decided to work on this automatic melanoma detection system. In this work a brief study has been carried out about the existing segmentation methods adopted by different scholars and a neural network based method is presented for effective segmentation of the melanoma images. In previous work, various methods i.e. Region refinement, adaptive thresholding (AT), gradient vector flow (GVF),adaptive snake (AS);fuzzy-based split-and-merge algorithm (FBSM), k-nearest neighbor, k-means, log filtering etc. has been proposed for the segmentation of melanoma images but their drawback is improper segmentation and more complexity and calculation time . Our method uses testing and training property of neural networks in which a neural object has been created by training it with normal skin and lesion pixels and then same object is used for segmenting other images having similar intensity values. Experimental results shows better results in terms of accuracy values of final segmentation.

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“…Level Set [48] Active Contours Without Edges [38] Whereas binary logic has been traditionally used in segmentation algorithms, the partialtruth-based fuzzy logic may also be applied to improve segmentation performance. This includes using fuzzy logic with region-based methods [50][51][52][53], neural networks [54], and clustering techniques [53], and the use of fuzzy logic to search for the optimal threshold level in thresholdbased segmentation [55].…”

Section: Edge-based Artificial Intelligencementioning

confidence: 99%

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices

Filho

Tavares

2015

J Med Syst

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In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

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Unsupervised Perceptual Segmentation of Natural Color Images Using Fuzzy-Based Hierarchical Algorithm

Cited by 12 publications

References 15 publications

Computational methods for the image segmentation of pigmented skin lesions: A review

Computational methods for the image segmentation of pigmented skin lesions: A review

Automatic Segmentation of Skin Melanoma Images Using Hybrid Method

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices

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