Literature DB >> 14561553

From colour to tissue histology: Physics-based interpretation of images of pigmented skin lesions.

Ela Claridge1, Symon Cotton, Per Hall, Marc Moncrieff.   

Abstract

Through an understanding of the image formation process, diagnostically important facts about the internal structure and composition of pigmented skin lesions can be derived from their colour images. A physics-based model of tissue colouration provides a cross-reference between image colours and the underlying histological parameters. It is constructed by computing the spectral composition of light remitted from the skin given parameters specifying its structure and optical properties. The model is representative of all the normal human skin colours, irrespective of racial origin, age or gender. Abnormal skin colours do not conform to this model and thus can be detected. Once the model is constructed, for each pixel in a colour image its histological parameters are computed from the model. Represented as images, these 'parametric maps' show the concentration of dermal and epidermal melanin, blood and collagen thickness across the imaged skin as well as locations where abnormal colouration exists. In a clinical study the parametric maps were used by a clinician to detect the presence of malignant melanoma in a set of 348 pigmented lesions imaged using a commercial device, the SIAscope. Logistic regression identified the presence of melanin in the dermis, the abnormal distribution of blood within the lesion and the lesion size as the most diagnostically informative features. Classification based on these features showed 80.1% sensitivity and 82.7% specificity in melanoma detection.

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Year:  2003        PMID: 14561553     DOI: 10.1016/s1361-8415(03)00033-1

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  2 in total

1.  Histology image analysis for carcinoma detection and grading.

Authors:  Lei He; L Rodney Long; Sameer Antani; George R Thoma
Journal:  Comput Methods Programs Biomed       Date:  2012-03-20       Impact factor: 5.428

2.  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

  2 in total

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