首页> 外文会议>IEEE International Conference on Image Processing >AUTOMATIC DETECTION OF MALIGNANT PROSTATIC GLAND UNITS INCROSS-SECTIONAL MICROSCOPIC IMAGESAUTOMATIC DETECTION OF MALIGNANT PROSTATIC GLAND UNITS INCROSS-SECTIONAL MICROSCOPIC IMAGES
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AUTOMATIC DETECTION OF MALIGNANT PROSTATIC GLAND UNITS INCROSS-SECTIONAL MICROSCOPIC IMAGESAUTOMATIC DETECTION OF MALIGNANT PROSTATIC GLAND UNITS INCROSS-SECTIONAL MICROSCOPIC IMAGES

机译:自动检测恶性前列腺单位增量分区显微镜图像恶性前列腺单元增量分区显微图像

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Prostate cancer is the second most frequent cause of cancer deaths among men in the US. In the most reliable screening method, his-tological images from a biopsy are examined under a microscope by pathologists. In an early stage of prostate cancer, only relatively few gland units in a large region become malignant. Discovering such sparse malignant gland units using a microscope is a labor-intensive and error-prone task for pathologists. In this paper, we develop effective image segmentation and classification methods for automatic detection of malignant gland units in microscopic images. Both segmentation and classification methods are based on carefully designed feature descriptors, including color histograms and texton co-occurrence tables.
机译:前列腺癌是美国男性中癌症死亡的第二次最常见的原因。在最可靠的筛选方法中,通过病理学家在显微镜下检查来自活组织检查的他的文化图像。在前列腺癌的早期阶段,大区域只有相对较少的腺体单元变得恶性。使用显微镜发现这种稀疏的恶性腺单元是病理学家的劳动密集型和易于易于易于的任务。在本文中,我们开发了用于自动检测微观图像中的恶性腺单元的有效图像分割和分类方法。分段和分类方法都基于精心设计的功能描述符,包括颜色直方图和Texton Co-Feationence表。

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