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用于对照临床试验的定量结膜激发试验。

Quantitative conjunctival provocation test for controlled clinical trials.

作者信息

Sárándi I, Claßen D P, Astvatsatourov A, Pfaar O, Klimek L, Mösges R, Deserno T M

机构信息

Prof. Dr. Thomas M. Deserno, Dept. of Medical Informatics, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52057 Aachen, Germany, E-mail:

出版信息

Methods Inf Med. 2014;53(4):238-44. doi: 10.3414/ME13-12-0142. Epub 2014 Jun 27.

Abstract

BACKGROUND

The conjunctival provocation test (CPT) is a diagnostic procedure for the assessment of allergic diseases. Photographs are taken before and after provocation increasing the redness of the conjunctiva due to hyperemia.

OBJECTIVE

We propose and evaluate an automatic image processing pipeline for objective and quantitative CPT.

METHOD

After scale normalization based on intrinsic image features, the conjunctiva region of interest (ROI) is segmented combining thresholding, edge detection and Hough transform. Redness of the ROI is measured from 0 to 1 by the average pixel redness, which is defined by truncated projection in HSV space. In total, 92 images from an observational diagnostic study are processed for evaluation. The database contains images from two visits for assessment of the test-retest reliability (46 images per visit).

RESULT

All images were successfully processed by the algorithm. The relative redness increment correlates between the two visits with Pearson's r = 0.672 (p < .001). Linear correlation of the automatic measure is larger than the manual measure (r = 0.59). This indicates a higher reproducibility and stability of the automatic method.

CONCLUSION

We presented a robust and effective way to objectify CPT. The algorithm operates on low resolution, is fast and requires no manual input. Quantitative CPT measures can now be established as surrogate endpoint in controlled clinical trials.

摘要

背景

结膜激发试验(CPT)是一种用于评估过敏性疾病的诊断程序。在激发前后拍摄照片,激发会因充血而增加结膜的发红程度。

目的

我们提出并评估一种用于客观定量CPT的自动图像处理流程。

方法

基于固有图像特征进行尺度归一化后,结合阈值处理、边缘检测和霍夫变换分割结膜感兴趣区域(ROI)。通过平均像素发红程度从0到1测量ROI的发红程度,平均像素发红程度由HSV空间中的截断投影定义。总共处理了来自一项观察性诊断研究的92张图像用于评估。该数据库包含两次就诊的图像,用于评估重测信度(每次就诊46张图像)。

结果

所有图像均由该算法成功处理。两次就诊之间的相对发红增量具有相关性,皮尔逊相关系数r = 0.672(p < 0.001)。自动测量的线性相关性大于手动测量(r = 0.59)。这表明自动方法具有更高的可重复性和稳定性。

结论

我们提出了一种客观化CPT的稳健有效方法。该算法在低分辨率下运行,速度快且无需手动输入。定量CPT测量现在可以作为对照临床试验中的替代终点来确立。

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