Bounded multivariate contaminated normal mixture model with applications to skin cancer detection
Background & Aim: In real-world datasets, outliers are a common occurrence that can have a significant impact on the accuracy and reliability of statistical analyses. Detecting these outliers and developing robust models to handle their presence is a crucial challenge in data analysis. For inst...
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Tehran University of Medical Sciences
2024-12-01
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| Series: | Journal of Biostatistics and Epidemiology |
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| Online Access: | https://jbe.tums.ac.ir/index.php/jbe/article/view/1453 |
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| author | Abbas Mahdavi |
| author_facet | Abbas Mahdavi |
| author_sort | Abbas Mahdavi |
| collection | DOAJ |
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Background & Aim: In real-world datasets, outliers are a common occurrence that can have a significant impact on the accuracy and reliability of statistical analyses. Detecting these outliers and developing robust models to handle their presence is a crucial challenge in data analysis. For instance, natural images may have complex distributions of values due to environmental factors like noise and illumination, resulting in objects with overlapping regions and non-trivial contours that cannot be accurately described by Gaussian mixture models. In many real life applications, observed data always fall in bounded support regions. This leads to the idea of bounded support mixture models. Motivated by the aforementioned observations, we introduce a bounded multivariate cntaminated normal distribution for fitting data with non-Gaussian distributions, asymmetry, and bounded support which makes finite mixture models more robust to fitting, since rare observations are given less importance in calculations.
Methods & Materials: A family of finite mixtures of bounded multivariate contaminated normal distributions is introduced. The model is well-suited for computer vision and pattern recognition problems due to its heavily-tailed and bounded nature, providing flexibility in modeling data in the presence of outliers. A feasible expectation-maximization algorithm is developed to compute the maximum likelihood estimates of the model parameters using a selection mechanism.
Results: The proposed methodology is validated by conducting experiments on two real natural skin cancer images. We estimate the parameters by the proposed expectation-maximization algorithm. The obtained results shown that the proposed model showed that the proposed method has successfully enhanced accuracy in segmenting skin lesions.
Conclusion: The reliable model-based clustering using finite mixtures of bounded multivariate contaminated normal distributions is introduced. An expectation-maximization algorithm was created to estimate parameters, with closed-form expressions utilized at the E-step. Practical tests on images for skin cancer detection showed enhanced accuracy in delineating skin lesions.
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| format | Article |
| id | doaj-art-5eccfd8dab9449b69447e89eae7cd363 |
| institution | Kabale University |
| issn | 2383-4196 2383-420X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Journal of Biostatistics and Epidemiology |
| spelling | doaj-art-5eccfd8dab9449b69447e89eae7cd3632025-01-06T08:40:27ZengTehran University of Medical SciencesJournal of Biostatistics and Epidemiology2383-41962383-420X2024-12-0110110.18502/jbe.v10i1.17157Bounded multivariate contaminated normal mixture model with applications to skin cancer detectionAbbas Mahdavi0Department of Statistics, Faculty of Mathematical Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran. Background & Aim: In real-world datasets, outliers are a common occurrence that can have a significant impact on the accuracy and reliability of statistical analyses. Detecting these outliers and developing robust models to handle their presence is a crucial challenge in data analysis. For instance, natural images may have complex distributions of values due to environmental factors like noise and illumination, resulting in objects with overlapping regions and non-trivial contours that cannot be accurately described by Gaussian mixture models. In many real life applications, observed data always fall in bounded support regions. This leads to the idea of bounded support mixture models. Motivated by the aforementioned observations, we introduce a bounded multivariate cntaminated normal distribution for fitting data with non-Gaussian distributions, asymmetry, and bounded support which makes finite mixture models more robust to fitting, since rare observations are given less importance in calculations. Methods & Materials: A family of finite mixtures of bounded multivariate contaminated normal distributions is introduced. The model is well-suited for computer vision and pattern recognition problems due to its heavily-tailed and bounded nature, providing flexibility in modeling data in the presence of outliers. A feasible expectation-maximization algorithm is developed to compute the maximum likelihood estimates of the model parameters using a selection mechanism. Results: The proposed methodology is validated by conducting experiments on two real natural skin cancer images. We estimate the parameters by the proposed expectation-maximization algorithm. The obtained results shown that the proposed model showed that the proposed method has successfully enhanced accuracy in segmenting skin lesions. Conclusion: The reliable model-based clustering using finite mixtures of bounded multivariate contaminated normal distributions is introduced. An expectation-maximization algorithm was created to estimate parameters, with closed-form expressions utilized at the E-step. Practical tests on images for skin cancer detection showed enhanced accuracy in delineating skin lesions. https://jbe.tums.ac.ir/index.php/jbe/article/view/1453ECME algorithmMixture modelContaminated normal distributionBounded distribution |
| spellingShingle | Abbas Mahdavi Bounded multivariate contaminated normal mixture model with applications to skin cancer detection Journal of Biostatistics and Epidemiology ECME algorithm Mixture model Contaminated normal distribution Bounded distribution |
| title | Bounded multivariate contaminated normal mixture model with applications to skin cancer detection |
| title_full | Bounded multivariate contaminated normal mixture model with applications to skin cancer detection |
| title_fullStr | Bounded multivariate contaminated normal mixture model with applications to skin cancer detection |
| title_full_unstemmed | Bounded multivariate contaminated normal mixture model with applications to skin cancer detection |
| title_short | Bounded multivariate contaminated normal mixture model with applications to skin cancer detection |
| title_sort | bounded multivariate contaminated normal mixture model with applications to skin cancer detection |
| topic | ECME algorithm Mixture model Contaminated normal distribution Bounded distribution |
| url | https://jbe.tums.ac.ir/index.php/jbe/article/view/1453 |
| work_keys_str_mv | AT abbasmahdavi boundedmultivariatecontaminatednormalmixturemodelwithapplicationstoskincancerdetection |