Implementation of Face Annotation with refined label content

Rai, Anuj; Singh, Piyush

doi:10.5958/2231-3915.2016.00004.3

Int. Jour. of Tech.

2016

DOI: 10.5958/2231-3915.2016.00004.3

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Implementation of Face Annotation with refined label content

Anuj Rai¹,

Piyush Singh²

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2022

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Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images

Maniraj

2022

RJPT

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In this paper, clustering approaches are analyzed for skin lesion segmentation using dermoscopic images. Three widely used machine learning approaches for image segmentation are Centroid-based clustering (CBC). Fuzzy C-Means Clustering (FCMC), and Expectation-Maximization (EM)–E&M step algorithm. The difference between CBC and FCMC lies in the partitioning method. The former one uses hard partitioning, and the later uses a variable degree of membership. In the EM algorithm, statistical methods are employed for distance calculation whereas, in CBC, the Euclidean distance measure is used. The segmentation results of individual clustering approaches are combined to get the refined skin lesion. Results show that the combined segmentation provides promising results for skin lesion segmentation in comparison with CBC, FCMC and EM- M step algorithm.

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Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images

Maniraj

2022

RJPT

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Implementation of Face Annotation with refined label content

Cited by 1 publication

References 0 publications

Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images

Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images

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