Quantitative evaluation for ultrasonographic internal structures in mass lesions of the maxillofacial region

颌面部肿块性病变超声内部结构的定量评价

• 批准号: 04671231
• 负责人: YOSHIURA Kazunori
• 金额: $ 1.34万
• 依托单位: KYUSHU UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for General Scientific Research (C)
• 财政年份: 1992
• 资助国家: 日本
• 起止时间: 1992 至 1993
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-04671231/
• 关键词: Ultrasonogram; Quantitative analysis; Internal structures; Numerical phantom; Texture; Run area analysis; ランエリア分析

The purpose of the project was to construct a new texture analysis method based on the quantitative data for the eye-brain system characteristics in evaluating homogeneity and coarseness. Two kinds of numerical phantoms were constructed to evaluate the effects of sizes and values of the elementary pixels (primitives) or their clusters (areas) on visual homogeneity and coarseness. Images of phantoms were displayd on CRT and presented to 11 observers. The size of primitives and areas greatly effected the results of visual evaluation for homogeneity and coarseness. The effects of the changes of gray value were only a little. Intraobserver variation for homogeneity was significantly larger than that for coarseness. Then, new texture analysis method (run area analysis) based on the eye-brain system characteristics has been developed. Two quantitative textural indices (heterogeneity index and coarseness index) were obtained. Numerical phantom was analyzed using this method, and the indices obtained were compared those of visual evaluation of the phantoms. Run area analysis method properly differentiated homogeneity and coarseness of the image. In addition, correlation between the results of visual evaluation and quantitative data obtained from this method was high. Finally, clinical ultrasonograms were analyzed with this method. Quantitative textural indices properly demonstrated the internal structures of the mass lesions. This fact indicates that this method could be an aid for visual evaluation for texture by offering quantitative data for it.

该项目的目的是构建一种基于眼脑系统特征定量数据的新纹理分析方法，以评估均匀性和粗糙度。构建了两种数值模型来评估基本像素（基元）或其簇（区域）的大小和值对视觉均匀性和粗糙度的影响。模型图像显示在 CRT 上并向 11 名观察者展示。图元和区域的大小极大地影响了均匀性和粗糙度的视觉评估结果。灰度值变化的影响很小。观察者内均匀性的变异明显大于粗糙度的变异。然后，开发了基于眼脑系统特性的新纹理分析方法（运行区域分析）。获得了两个定量的结构指数（异质性指数和粗糙度指数）。利用该方法对数值模型进行分析，并将获得的指标与模型的视觉评价指标进行比较。运行区域分析方法正确区分图像的均匀性和粗糙度。此外，目视评估结果与通过该方法获得的定量数据之间的相关性很高。最后，用该方法对临床超声图进行分析。定量纹理指数正确地显示了肿块病变的内部结构。这一事实表明，该方法可以通过提供定量数据来辅助纹理的视觉评估。