摘要翻译：
许多最先进的可视化技术必须针对特定类型的数据集、其模态(CT、MRI等）、记录的对象或解剖区域（头部、脊柱、腹部等）以及与数据采集过程相关的其他参数进行定制。虽然部分信息（成像方式和采集序列）可以从体扫描存储的元数据中获得，但还有一些重要信息（解剖区域、示踪化合物）没有明确存储。此外，元数据可能不完整、不合适或只是缺失。本文提出了一种新的简单的方法来确定数据集的类型。基于数据集强度和梯度大小的二维直方图被用作神经网络的输入，神经网络将其分为几个训练类别之一。该方法是可视化系统非专家自主使用的重要组成部分。该方法已经在80个数据集上进行了测试，分为3个类和一个“REST”类。一个重要的结果是，系统在仅用一个数据集训练数据集后，能够将数据集分类到特定的类中。该方法的其他优点是易于实现和计算性能高。
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英文标题：
《A Neural Network Classifier of Volume Datasets》
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作者：
D\v{z}enan Zuki\'c, Christof Rezk-Salama, Andreas Kolb
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Graphics        图形学
分类描述：Covers all aspects of computer graphics. Roughly includes material in all of ACM Subject Class I.3, except that I.3.5 is is likely to have Computational Geometry as the primary subject area.
涵盖了计算机图形学的各个方面。大致包括所有ACM课程I.3的材料，除了I.3.5可能有计算几何作为主要的学科领域。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Many state-of-the art visualization techniques must be tailored to the specific type of dataset, its modality (CT, MRI, etc.), the recorded object or anatomical region (head, spine, abdomen, etc.) and other parameters related to the data acquisition process. While parts of the information (imaging modality and acquisition sequence) may be obtained from the meta-data stored with the volume scan, there is important information which is not stored explicitly (anatomical region, tracing compound). Also, meta-data might be incomplete, inappropriate or simply missing.   This paper presents a novel and simple method of determining the type of dataset from previously defined categories. 2D histograms based on intensity and gradient magnitude of datasets are used as input to a neural network, which classifies it into one of several categories it was trained with. The proposed method is an important building block for visualization systems to be used autonomously by non-experts. The method has been tested on 80 datasets, divided into 3 classes and a "rest" class.   A significant result is the ability of the system to classify datasets into a specific class after being trained with only one dataset of that class. Other advantages of the method are its easy implementation and its high computational performance.
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PDF链接：
https://arxiv.org/pdf/0906.2274