Random Forest Classification and Local Region-Based, Level-Set Segmentation for Quantitative Ultraso

来源 :2015两岸超声技术与医学研讨会 | 被引量 : 0次 | 上传用户：kaixin314159

【摘要】

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【出处】

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2015两岸超声技术与医学研讨会

【发表日期】

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2015年5期

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Background, Motivation and Objective To detect metastatic foci in excised human lymph nodes (LNs) using three-dimensional (3D), high-frequency quantitative ultrasound (QUS), it is essential to accuratelysegment three media in the 3D envelope data: LN parenchyma (LNP), fat and normal saline (NS). However, automatic segmentation of the 3D data remains challengingbecause of speckle noise as well as low contrast and intensity inhomogeneity caused by attenuation and focusing effects.

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