School of Computer Science and Engineering, Seoul National University, 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Korea.
Med Phys. 2011 Aug;38(8):4667-71. doi: 10.1118/1.3611046.
This study aimed to introduce heat map, a graphical data presentation method widely used in gene expression experiments, to the presentation and interpretation of image fidelity assessment data of compressed computed tomography (CT) images.
The authors used actual assessment data that consisted of five radiologists' responses to 720 computed tomography images compressed using both Joint Photographic Experts Group 2000 (JPEG2000) 2D and JPEG2000 3D compressions. They additionally created data of two artificial radiologists, which were generated by partly modifying the data from two human radiologists.
For each compression, the entire data set, including the variations among radiologists and among images, could be compacted into a small color-coded grid matrix of the heat map. A difference heat map depicted the advantage of 3D compression over 2D compression. Dendrograms showing hierarchical agglomerative clustering results were added to the heat maps to illustrate the similarities in the data patterns among radiologists and among images. The dendrograms were used to identify two artificial radiologists as outliers, whose data were created by partly modifying the responses of two human radiologists.
The heat map can illustrate a quick visual extract of the overall data as well as the entirety of large complex data in a compact space while visualizing the variations among observers and among images. The heat map with the dendrograms can be used to identify outliers or to classify observers and images based on the degree of similarity in the response patterns.
本研究旨在将热图这一广泛应用于基因表达实验的图形数据展示方法引入压缩计算机断层扫描(CT)图像的图像逼真度评估数据的呈现和解释中。
作者使用了实际的评估数据,该数据由五名放射科医生对使用联合图像专家组 2000 版 2D(JPEG2000 2D)和 JPEG2000 3D 压缩的 720 张 CT 图像的响应组成。此外,作者还创建了两名人工放射科医生的数据,这些数据是通过部分修改两名人类放射科医生的数据生成的。
对于每种压缩方式,整个数据集,包括放射科医生之间和图像之间的差异,都可以压缩成一个小的彩色编码网格矩阵热图。差异热图描绘了 3D 压缩相对于 2D 压缩的优势。添加了显示层次凝聚聚类结果的树形图到热图中,以说明放射科医生和图像之间的数据模式的相似性。树形图用于识别两名人工放射科医生为异常值,其数据是通过部分修改两名人类放射科医生的响应创建的。
热图可以快速直观地提取整体数据,以及在紧凑的空间中呈现大量复杂数据的全貌,同时可视化观察者之间和图像之间的差异。带有树形图的热图可用于识别异常值,或根据响应模式的相似程度对观察者和图像进行分类。