Computational cancer detection of pathological images based on an optimization method for color-index local auto-correlation feature extraction

Jia Qu, H. Nosato, H. Sakanashi, E. Takahashi, Kensuke Terai, N. Hiruta
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引用次数: 4

Abstract

Aiming to lessen the burdens of the pathologist with efficient diagnosis assistance, this paper proposes a cancer detection method for pathological images utilizing color features based on color-index local auto-correlations (CILAC), applied to color-indexed images to utilize co-occurrence information about indexed pixels. Moreover, a method for the automatic optimization of feature extraction is also proposed. Based on a database including both benign and cancerous pathological images, experimental results show enhanced performance compared to prior research, which demonstrate the effectiveness of the proposed cancer detection method.
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基于颜色指数局部自相关特征提取优化方法的病理图像计算癌检测
为了减轻病理医师的诊断负担,提供有效的诊断辅助,本文提出了一种基于颜色索引局部自相关(CILAC)的病理图像癌症检测方法,并将其应用于颜色索引图像,利用索引像素的共现信息。此外,还提出了一种特征提取的自动优化方法。基于包含良性和癌性病理图像的数据库,实验结果表明,与先前的研究相比,该方法的性能有所提高,证明了所提出的癌症检测方法的有效性。
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