She Zhishun, Duller A W G, Liu Y, Fish P J
Faculty of Technology & Computer Science, NEWI, Wrexham LL11 2AW, UK.
Skin Res Technol. 2006 May;12(2):133-44. doi: 10.1111/j.0909-752X.2006.00140.x.
BACKGROUND/PURPOSE: In order to properly analyse the effectiveness of methods for optically differentiating malignant from benign skin lesions, it is necessary to have a set of images for which the ground truth is known. However, aspects of the ground truth of clinical images such as true lesion boundary position are unknown or not known precisely. Therefore, a skin/lesion image simulation with known features including boundary location, skin pattern and lesion colour is needed to enable accurate assessment of feature estimation algorithms for lesion classification.
In this paper, monochrome and colour skin/lesion images are synthesised with known characteristics including boundary, colour and skin pattern. Skin pattern is simulated with segmented lines with variations in length, orientation and intensity. Skin and lesion textures are modelled by an auto-regressive (AR) process. Monochrome skin lesion images are obtained by combining monochrome skin and lesion textures under the control of a known lesion shape with the addition of skin pattern. Colour skin lesion images are generated by mixing coloured skin and lesion textures. Finally, an inflammation area and image artefacts such as hair and specular reflection are added.
The synthesised images provide the image set for evaluating image pre-processing, segmentation and skin pattern analysis. The pre-processing includes hair removal and specular reflection reduction. An AR model interpolation is suggested for hair removal, and multiple illumination processing is developed to decrease specular reflection. A fast snake algorithm is extended to detect the boundaries of skin lesion and inflammation areas. Skin line direction is detected as a feature to measure the disruption of skin pattern caused by lesion.
Simulation of monochrome and colour skin/lesion image has been investigated, which is an alternative way to provide image set with known characteristics to validate image processing algorithms for image pre-processing, lesion/inflammation boundary detection and skin pattern analysis.
背景/目的：为了正确分析光学鉴别恶性与良性皮肤病变方法的有效性，有必要拥有一组已知真实情况的图像。然而，临床图像的真实情况方面，如真实病变边界位置是未知的或无法精确得知。因此，需要一种具有已知特征（包括边界位置、皮肤纹理和病变颜色）的皮肤/病变图像模拟方法，以准确评估用于病变分类的特征估计算法。
本文合成了具有已知特征（包括边界、颜色和皮肤纹理）的单色和彩色皮肤/病变图像。皮肤纹理通过长度、方向和强度变化的分段线来模拟。皮肤和病变纹理通过自回归（AR）过程建模。单色皮肤病变图像是在已知病变形状的控制下，将单色皮肤和病变纹理相结合，并添加皮肤纹理而获得的。彩色皮肤病变图像通过混合彩色皮肤和病变纹理生成。最后，添加炎症区域以及毛发和镜面反射等图像伪影。
合成图像为评估图像预处理、分割和皮肤纹理分析提供了图像集。预处理包括去除毛发和减少镜面反射。提出了一种AR模型插值法用于去除毛发，并开发了多重光照处理以减少镜面反射。扩展了一种快速蛇形算法来检测皮肤病变和炎症区域的边界。检测皮肤纹理方向作为一种特征，以测量病变对皮肤纹理的破坏程度。
研究了单色和彩色皮肤/病变图像的模拟，这是一种提供具有已知特征的图像集的替代方法，用于验证图像预处理、病变/炎症边界检测和皮肤纹理分析的图像处理算法。
