Sector expansion and elliptical modeling of blue-gray ovoids for basal cell carcinoma discrimination in dermoscopy images.
Sector expansion and elliptical modeling of blue-gray ovoids for basal cell carcinoma discrimination in dermoscopy images.
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DOI：
10.1111/srt.12006
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发表时间：
2013-02
期刊：
影响因子：
--
通讯作者：
Moss RH
中科院分区：
文献类型：
--
作者：
Guvenc P;LeAnder RW;Kefel S;Stoecker WV;Rader RK;Hinton KA;Stricklin SM;Rabinovitz HS;Oliviero M;Moss RH
蓝灰色卵泡（B-GOs）是基底细胞癌（BCC）的关键皮肤镜结构，为BCC的自动检测提供了机会。由于大小和颜色的变化，b - go在良性病变中很容易被误认为是类似的结构。通过对这些结构的分析，可以对b - go进行准确的表征和自动识别，进一步实现BCC自动检测的目标。本研究利用一种新的分割方法来区分b - go和它们的良性模拟物。获得68例确诊bcc伴B-GOs的接触皮肤镜图像。另一组131张具有B-GO模拟物的良性病变接触皮肤镜图像提供了一组良性竞争图像。所有结构共分析了22个B-GO特征：21个颜色特征和1个尺寸特征。在分割方面，本研究采用了一种新颖的基于扇区的非递归分割方法，扩展了适用于B-GOs和模拟结构的掩模。逻辑回归分析表明，蓝色色度是区分BCC中真正的B-GOs和良性、模拟结构的最佳特征。当最终的B-GO边界近似为最佳拟合椭圆时，恶性结构的识别效果最佳。利用该优化配置进行logistic回归分析，将扩张拟合的恶性结构与相似的良性结构区分开来，分类率高达96.5%。实验结果表明，颜色特征可以精确地从种子区域扩展和定位结构。将这些结构建模为椭圆可以使bcc中的B-GOs与良性图像中的相似结构高度区分。
Blue-gray ovoids (B-GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B-GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B-GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B-GOs from their benign mimics. Contact dermoscopy images of 68 confirmed BCCs with B-GOs were obtained. Another set of 131 contact dermoscopic images of benign lesions possessing B-GO mimics provided a benign competitive set. A total of 22 B-GO features were analyzed for all structures: 21 color features and one size feature. Regarding segmentation, this study utilized a novel sector-based, non-recursive segmentation method to expand the masks applied to the B-GOs and mimicking structures. Logistic regression analysis determined that blue chromaticity was the best feature for discriminating true B-GOs in BCC from benign, mimicking structures. Discrimination of malignant structures was optimal when the final B-GO border was approximated by a best-fit ellipse. Using this optimal configuration, logistic regression analysis discriminated the expanded and fitted malignant structures from similar benign structures with a classification rate as high as 96.5%. Experimental results show that color features allow accurate expansion and localization of structures from seed areas. Modeling these structures as ellipses allows high discrimination of B-GOs in BCCs from similar structures in benign images.
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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
影响因子：
5.7
作者：
Celebi, M. Emre;Iyatomi, Hitoshi;Stoecker, William V.;Moss, Randy H.;Rabinovitz, Harold S.;Argenziano, Giuseppe;Soyer, H. Peter
通讯作者：
Soyer, H. Peter
影响因子：
1.5
作者：
GANDER, W;GOLUB, GH;STREBEL, R
通讯作者：
STREBEL, R
影响因子：
2.2
作者：
Cheng, Yue Iris;Swamisai, Ragavendar;Srinivasan, Subhashini K.
通讯作者：
Srinivasan, Subhashini K.