Multifractal feature descriptor for histopathology.
Multifractal feature descriptor for histopathology.
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DOI：
10.3233/acp-2011-0045
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发表时间：
2012
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影响因子：
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通讯作者：
Hashiguchi A
中科院分区：
文献类型：
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作者：
Atupelage C;Nagahashi H;Yamaguchi M;Sakamoto M;Hashiguchi A
背景：组织学图像分析在癌症诊断中起着重要作用。它描述了身体组织的结构，异常结构会引起癌症或其他一些疾病的怀疑。从人眼观察这些混沌纹理的结构变化是一个具有挑战性的过程。然而，这一挑战可以通过形成数学描述符来表示组织学纹理并通过复杂的计算方法对结构变化进行分类来克服。 目的：提出一种纹理描述子，将组织学纹理引入到具有高分辨力的特征空间。 方法：分形维数以不同于拓扑维数的方式更准确地描述了自相似结构。此外，分形现象已被扩展到自然结构（图像）的多重分形维数。我们利用多重分形分析来表示组织学纹理，从而得到更有鉴别力的分类特征空间。 结果如下：我们利用一组组织学图像（属于肝脏和前列腺标本）来评估多重分形特征的区分能力。组织实验以将给定的组织学纹理分类为癌症和非癌症。结果表明，多重分形特征的歧视能力，达到约95%的正确分类率。 结论：多重分形特征能更有效地描述组织结构。所提出的特征描述符对肝脏和前列腺数据样本集都表现出较高的分类率。
Background: Histologic image analysis plays an important role in cancer diagnosis. It describes the structure of the body tissues and abnormal structure gives the suspicion of the cancer or some other diseases. Observing the structural changes of these chaotic textures from the human eye is challenging process. However, the challenge can be defeat by forming mathematical descriptor to represent the histologic texture and classify the structural changes via a sophisticated computational method. Objective: In this paper, we propose a texture descriptor to observe the histologic texture into highly discriminative feature space. Methods: Fractal dimension describes the self-similar structures in different and more accurate manner than topological dimension. Further, the fractal phenomenon has been extended to natural structures (images) as multifractal dimension. We exploited the multifractal analysis to represent the histologic texture, which derive more discriminative feature space for classification. Results: We utilized a set of histologic images (belongs to liver and prostate specimens) to assess the discriminative power of the multifractal features. The experiment was organized to classify the given histologic texture as cancer and non-cancer. The results show the discrimination capability of multifractal features by achieving approximately 95% of correct classification rate. Conclusion: Multifractal features are more effective to describe the histologic texture. The proposed feature descriptor showed high classification rate for both liver and prostate data sample datasets.