使用step-DTOCS对组织病理学乳腺癌图像进行计算机辅助图像分析

Tiia Ikonen, Harri Niska, Billy Braithwaite, I. Pöllänen, Keijo Haataja, Pekka J. Toivanen, T. Tolonen, J. Isola
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引用次数: 3

摘要

在本文中,我们解决了乳腺癌的流行病学和形态学问题,特别关注病变产生的不同细胞特征。此外,我们还深入了解了图像分析管道中的特征提取和分类方案。在此基础上,提出了一种新的乳腺癌图像特征提取方法——改进的曲面空间距离变换(DTOCS)。第一个实验结果表明,基于step - dtocs的mlp网络能够很好地区分不同的细胞结构。对所得结果进行了介绍和分析,并讨论了进一步研究的思路。
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Computer-assisted image analysis of histopathological breast cancer images using step-DTOCS
In this paper, we address the epidemiology and morphology questions of breast cancer with special focus on different cell features created by lesions. In addition, we provide an insight into feature extraction and classification schemes in the image analysis pipeline. Based on our conducted research work, a novel feature extraction approach, a modification of Distance Transform on Curved Space (DTOCS), is proposed for analysis and classification of breast cancer images. The first experimental results suggest that the Step-DTOCS-based MLP-network is capable of discriminating different cell structures in a respectable way. The obtained results are presented and analyzed, and further research ideas are discussed.
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