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

被引:68
作者
Claridge, E [1 ]
Cotton, S
Hall, P
Moncrieff, M
机构
[1] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
[2] Astron Clin, Cambridge CB3 7RL, England
[3] Addenbrookes Hosp, Dept Plast Surg, Cambridge CB2 2QQ, England
[4] Mersey Deanery, Liverpool L3, Merseyside, England
基金
英国工程与自然科学研究理事会;
关键词
colour analysis; image analysis; physics-based modelling; pigmented skin lesions; melanoma diagnosis;
D O I
10.1016/S1361-8415(03)00033-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:489 / 502
页数:14
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