Region growing by sector analysis for detection of blue-gray ovoids in basal cell carcinoma.
Region growing by sector analysis for detection of blue-gray ovoids in basal cell carcinoma.
复制标题
DOI：
10.1111/srt.12036
复制
发表时间：
2013-08
期刊：
影响因子：
--
通讯作者：
Stoecker WV
中科院分区：
文献类型：
--
作者：
Pelin Guvenc S;Leander RW;Kefel S;Rader RK;Hinton KA;Stricklin SM;Stoecker WV
蓝灰色卵形体（B-C）是基底细胞癌（BCC）中的关键皮肤镜结构，对自动检测提出了挑战。由于大小和颜色的变化，B超很容易被误认为是良性病变中的类似结构。对这些结构的分析有助于进一步实现BCC自动检测的目标。本文介绍了一种基于扇区的有效的B超分割方法。传统的区域生长技术的四个修改：（i）采用种子区域，而不是种子点，（ii）利用固定的控制范围确定的种子区域，以消除重新计算先前添加的区域，（iii）确定区域生长标准，使用逻辑回归，和（iv）区域分析和扩展的部门。获得了68例确诊为B-B细胞癌的接触性皮肤镜图像。针对所有B-GO种子区域分析了总共24个颜色特征。Logistic回归分析确定蓝色色度，其次是红色方差，是区分B-GO边缘与周围区域的最佳特征。恶性结构的分割获得了平均普拉特品质因数为0.397。这里提出的技术提供了一个非递归的，基于扇区的，区域增长的方法适用于任何彩色结构出现在数字图像。使用这些技术的进一步研究可能会导致BCC中B-binding的自动检测。
Blue-gray ovoids (B-GOs) are critical dermoscopic structures in basal cell carcinomas (BCCs) that pose a challenge for automatic detection. Due to variation in size and color, B-GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could help further accomplish the goal of automatic BCC detection. This study introduces an efficient sector-based method for segmenting B-GOs. Four modifications of conventional region-growing techniques are presented: (i) employing a seed area rather than a seed point, (ii) utilizing fixed control limits determined from the seed area to eliminate re-calculations of previously-added regions, (iii) determining region growing criteria using logistic regression, and (iv) area analysis and expansion by sectors. Contact dermoscopy images of 68 confirmed BCCs having B-GOs were obtained. A total of 24 color features were analyzed for all B-GO seed areas. Logistic regression analysis determined blue chromaticity, followed by red variance, were the best features for discriminating B-GO edges from surrounding areas. Segmentation of malignant structures obtained an average Pratt's figure of merit of 0.397. The techniques presented here provide a non-recursive, sector-based, region-growing method applicable to any colored structure appearing in digital images. Further research using these techniques could lead to automatic detection of B-GOs in BCCs.
登录
查看更多内容
影响因子：
1.8
作者：
Lim, DH
通讯作者：
Lim, DH
影响因子：
--
作者：
Hance, GA;Umbaugh, SE;Stoecker, WV
通讯作者：
Stoecker, WV
影响因子：
13.8
作者：
Argenziano, G;Soyer, HP;Wolff, K
通讯作者：
Wolff, K
影响因子：
13.8
作者：
Altamura, Davide;Menzies, Scott W.;Peris, Ketty
通讯作者：
Peris, Ketty
DOI：
10.1016/j.jaad.2005.11.1096
发表时间：
2006-04-01
影响因子：
13.8
作者：
Agero, ALC;Busam, KJ;Halpern, AC
通讯作者：
Halpern, AC