Unsupervised color image segmentation
Unsupervised color image segmentation
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DOI：
10.1109/51.482850
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发表时间：
1996-01-01
影响因子：
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通讯作者：
Stoecker, WV
中科院分区：
文献类型：
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作者：
Hance, GA;Umbaugh, SE;Stoecker, WV
本研究中使用的图像是从私人皮肤科诊所和纽约大学获得的35 mm彩色摄影幻灯片数字化的。作者比较了6种颜色分割方法及其作为整体边界查找算法的一部分的有效性。PCT/中值切割和自适应阈值算法提供了最低的平均误差，并显示出最有前途的进一步个别算法的发展。结合不同的方法导致进一步提高正确识别的肿瘤边界的数量，并且通过在合并分割的对象信息时结合额外的几何学，可以潜在地进一步提高成功率。该算法具有广泛的基础，并提出了几个需要进一步研究的领域。一个可能的探索领域是将一个智能决策过程的颜色，应用于分割的PCT/中位数切割和自适应阈值算法的数量。为了比较的目的，在作者的申请中，颜色的数量保持不变，为三种。可以探索的其他领域是噪声去除和对象分类，以确定正确的肿瘤对象。
The images used in this research were digitized from 35mm color photographic slides obtained from a private dermatology practice and from New York University. The authors compared 6 color segmentation methods and their effectiveness as part of an overall border-finding algorithm. The PCT/median cut and adaptive thresholding algorithms provided the lowest average error and show the most promise for further individual algorithm development. Combining the different methods resulted in further improvement in the number of correctly identified tumor borders, and by incorporating additional heuristics in merging the segmented object information, one could potentially further increase the success rate. The algorithm is broad-based and suggests several areas for further research. One possible area of exploration is to incorporate an intelligent decision making process as to the number of colors that should be used for segmentation in the PCT/median cut and adaptive thresholding algorithms. For comparison purposes, the number of colors was kept constant at three in the authors' application. Other areas that can be explored are noise removal and object classification to determine the correct tumor object.