Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis.
Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis.
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DOI：
10.3390/e22121370
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发表时间：
2020-12-04
期刊：
影响因子：
--
通讯作者：
Yasin S
中科院分区：
文献类型：
--
作者：
Ali T;Masood K;Irfan M;Draz U;Nagra AA;Asif M;Alshehri BM;Glowacz A;Tadeusiewicz R;Mahnashi MH;Yasin S
在这项研究中，提出了一种多阶段分割技术来识别前列腺组织样本中的癌细胞。使用腺体的质地来区分组织的良性区域和癌变区域。纹理的建模基于小波包特征和样本熵值。在多阶段分割过程中，将均值漂移算法应用于经预处理的图像，以执行组织的粗分割。在第二阶段使用小波包来获得腺体结构形状的精细细节。最后，用样本熵值来模拟腺体的纹理，它从间质斑块中识别出上皮区域。虽然该算法分为三个阶段，但由于小波包特征和样本熵值对所需感兴趣区域进行了稳健的建模，因此计算速度很快。与其他最先进的纹理分割技术进行了比较分析，并计算了骰子比率以进行比较。实验结果表明，该算法不仅性能优于其他方法，而且通过引入样本熵特征，实现了对组织癌变区域的识别，分类正确率达到90%，表明了该算法的鲁棒性。
In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.
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影响因子：
10.6
作者：
Huang, Po-Whei;Lee, Cheng-Hsiung
通讯作者：
Lee, Cheng-Hsiung
影响因子：
2.5
作者：
COIFMAN, RR;WICKERHAUSER, MV
通讯作者：
WICKERHAUSER, MV
影响因子：
6.1
作者：
Glotsos, Dimitris;Kalatzis, Loannis;Cavouras, Dionisis
通讯作者：
Cavouras, Dionisis
影响因子：
3
作者：
May, Mike
通讯作者：
May, Mike
DOI：
10.1109/34.1000236
发表时间：
2002-05-01
影响因子：
23.6
作者：
Comaniciu, D;Meer, P
通讯作者：
Meer, P