Department of Computer Science, National Textile University, Faisalabad, Pakistan.
Skin Res Technol. 2013 Feb;19(1):e27-36. doi: 10.1111/j.1600-0846.2011.00603.x. Epub 2011 Dec 28.
BACKGROUND/PURPOSE: Accurate segmentation and repair of hair-occluded information from dermoscopy images are challenging tasks for computer-aided detection (CAD) of melanoma. Currently, many hair-restoration algorithms have been developed, but most of these fail to identify hairs accurately and their removal technique is slow and disturbs the lesion's pattern.
In this article, a novel hair-restoration algorithm is presented, which has a capability to preserve the skin lesion features such as color and texture and able to segment both dark and light hairs. Our algorithm is based on three major steps: the rough hairs are segmented using a matched filtering with first derivative of gaussian (MF-FDOG) with thresholding that generate strong responses for both dark and light hairs, refinement of hairs by morphological edge-based techniques, which are repaired through a fast marching inpainting method. Diagnostic accuracy (DA) and texture-quality measure (TQM) metrics are utilized based on dermatologist-drawn manual hair masks that were used as a ground truth to evaluate the performance of the system.
The hair-restoration algorithm is tested on 100 dermoscopy images. The comparisons have been done among (i) linear interpolation, inpainting by (ii) non-linear partial differential equation (PDE), and (iii) exemplar-based repairing techniques. Among different hair detection and removal techniques, our proposed algorithm obtained the highest value of DA: 93.3% and TQM: 90%.
The experimental results indicate that the proposed algorithm is highly accurate, robust and able to restore hair pixels without damaging the lesion texture. This method is fully automatic and can be easily integrated into a CAD system.
背景/目的：准确分割和修复皮肤镜图像中的毛发遮挡信息是计算机辅助检测（CAD）黑色素瘤的一项具有挑战性的任务。目前已经开发出许多毛发修复算法，但其中大多数都无法准确识别毛发，并且它们的去除技术速度较慢，会扰乱病变的模式。
本文提出了一种新的毛发修复算法，该算法具有保留皮肤病变特征（如颜色和纹理）的能力，并且能够分割深色和浅色毛发。我们的算法基于三个主要步骤：使用具有高斯一阶导数的匹配滤波（MF-FDOG）和阈值分割粗发，这会为深色和浅色毛发产生强烈的响应；使用基于形态学边缘的技术对毛发进行细化，通过快速行进填充方法进行修复。利用皮肤科医生绘制的手动毛发掩模作为地面实况来评估系统性能，基于诊断准确性（DA）和纹理质量度量（TQM）指标进行评估。
毛发修复算法在 100 张皮肤镜图像上进行了测试。在不同的毛发检测和去除技术之间进行了比较：（i）线性插值，（ii）非线性偏微分方程（PDE）的填充，以及（iii）基于示例的修复技术。在不同的毛发检测和去除技术中，我们提出的算法获得了最高的 DA 值：93.3%和 TQM：90%。
实验结果表明，该算法具有高度的准确性、鲁棒性，并且能够在不损害病变纹理的情况下修复毛发像素。该方法是全自动的，可以很容易地集成到 CAD 系统中。
