实验结果表明，SVM方法可获得较高的尿沉渣有形成分识别率。
The experimental results show that SVM method has higher recognition rate of urine sediment classification.
为了能够对尿沉渣有形成分进行准确的数据分析，关键是要对该种图像进行正确分割。
It is necessary to correctly segment image in order to analyze accurately urinary sediment visible component.
目的探讨尿沉渣中的有形成分对UF- 100全自动尿沉渣分析仪结果的干扰因素。
Objective to evaluate the disturbing factors of the material ingredients in urinary sediments on UF-100 automated urinalysis analyzer.
实验结果表明，该方法能够准确有效的实现尿沉渣图像有形成分的分割。
Experimental results show that the proposed method can segment urinary sediment images effectively and precisely.
实验结果表明，该方法能够准确有效的实现尿沉渣图像有形成分的分割。
Experimental results show that the proposed method can segment urinary sediment images effectively and precisely.
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