Literature DB >> 18002737

Determination of skin repigmentation progression.

Hermawan Nugroho1, M H Ahmad Fadzil, V V Yap, S Norashikin, H H Suraiya.   

Abstract

In this paper, we describe an image processing scheme to analyze and determine areas of skin that have undergone repigmentation in particular, during the treatment of vitiligo. In vitiligo cases, areas of skin become pale or white due to the lack of skin pigment called melanin. Vitiligo treatment causes skin repigmentation resulting in a normal skin color. However, it is difficult to determine and quantify the amount of repigmentation visually during treatment because the repigmentation progress is slow and moreover changes in skin color can only be discerned over a longer time frame typically 6 months. Here, we develop a digital image analysis scheme that can identify and determine vitiligo skin areas and repigmentation progression on a shorter time period. The technique is based on principal component analysis and independent component analysis which converts the RGB skin image into a skin image that represent skin areas due to melanin and haemoglobin only, followed by segmentation process. Vitiligo skin lesions are identified as skin areas that lack melanin (non-melanin areas). In the initial studies of 4 patients, the method has been able to quantify repigmentation in vitiligo lesion. Hence it is now possible to determine repigmentation progression objectively and treatment efficacy on a shorter time cycle.

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Year:  2007        PMID: 18002737     DOI: 10.1109/IEMBS.2007.4353071

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  5 in total

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Journal:  J Biomed Opt       Date:  2010 Jul-Aug       Impact factor: 3.170

2.  A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions.

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3.  Multimodal facial color imaging modality for objective analysis of skin lesions.

Authors:  Youngwoo Bae; J Stuart Nelson; Byungjo Jung
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4.  Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors.

Authors:  Jana M Kainerstorfer; Jason D Riley; Martin Ehler; Laleh Najafizadeh; Franck Amyot; Moinuddin Hassan; Randall Pursley; Stavros G Demos; Victor Chernomordik; Michael Pircher; Paul D Smith; Christoph K Hitzenberger; Amir H Gandjbakhche
Journal:  Biomed Opt Express       Date:  2011-04-01       Impact factor: 3.732

5.  Evaluation of non-invasive multispectral imaging as a tool for measuring the effect of systemic therapy in Kaposi sarcoma.

Authors:  Jana M Kainerstorfer; Mark N Polizzotto; Thomas S Uldrick; Rafa Rahman; Moinuddin Hassan; Laleh Najafizadeh; Yasaman Ardeshirpour; Kathleen M Wyvill; Karen Aleman; Paul D Smith; Robert Yarchoan; Amir H Gandjbakhche
Journal:  PLoS One       Date:  2013-12-27       Impact factor: 3.240

  5 in total

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