American Journal of Biomedical and Life Sciences

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Maximum Likelihood and Spectral Angle Mapper and K-means algorithms used to detection of Melanoma

Received: 07 December 2014    Accepted: 09 December 2014    Published: 07 August 2015
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Abstract

Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm.

DOI 10.11648/j.ajbls.s.2015030203.12
Published in American Journal of Biomedical and Life Sciences (Volume 3, Issue 2-3, April 2015)

This article belongs to the Special Issue Spectral Imaging for Medical Diagnosis “Modern Tool for Molecular Imaging”

Page(s) 8-15
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Melanoma; Spectral imaging; spectral spectroscopy; Maximum Likelihood; Spectral Angle Mapper, classification, K-Means clustering, Supported classification, unsupported classification, cancer detection

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    Issa Ibraheem. (2015). Maximum Likelihood and Spectral Angle Mapper and K-means algorithms used to detection of Melanoma. American Journal of Biomedical and Life Sciences, 3(2-3), 8-15. https://doi.org/10.11648/j.ajbls.s.2015030203.12

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    Issa Ibraheem. Maximum Likelihood and Spectral Angle Mapper and K-means algorithms used to detection of Melanoma. Am. J. Biomed. Life Sci. 2015, 3(2-3), 8-15. doi: 10.11648/j.ajbls.s.2015030203.12

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    Issa Ibraheem. Maximum Likelihood and Spectral Angle Mapper and K-means algorithms used to detection of Melanoma. Am J Biomed Life Sci. 2015;3(2-3):8-15. doi: 10.11648/j.ajbls.s.2015030203.12

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  • @article{10.11648/j.ajbls.s.2015030203.12,
      author = {Issa Ibraheem},
      title = {Maximum Likelihood and Spectral Angle Mapper and K-means algorithms used to detection of Melanoma},
      journal = {American Journal of Biomedical and Life Sciences},
      volume = {3},
      number = {2-3},
      pages = {8-15},
      doi = {10.11648/j.ajbls.s.2015030203.12},
      url = {https://doi.org/10.11648/j.ajbls.s.2015030203.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajbls.s.2015030203.12},
      abstract = {Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Maximum Likelihood and Spectral Angle Mapper and K-means algorithms used to detection of Melanoma
    AU  - Issa Ibraheem
    Y1  - 2015/08/07
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajbls.s.2015030203.12
    DO  - 10.11648/j.ajbls.s.2015030203.12
    T2  - American Journal of Biomedical and Life Sciences
    JF  - American Journal of Biomedical and Life Sciences
    JO  - American Journal of Biomedical and Life Sciences
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    AB  - Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm.
    VL  - 3
    IS  - 2-3
    ER  - 

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