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.
Tzeng et al. @cite_10 suggest an interactive visualization system which allows the user to mark regions of interest by roughly painting the boundaries on a few slice images. During painting, the marked regions are used to train a neural network for multi-dimensional classification. Del adapt this approach to specify transfer functions in an augmented reality environment for medical applications @cite_6 . @cite_15 apply general regression neural networks to classify each point of a dataset into a certain class. This information is later used for assigning optical properties (e.g. color). @cite_14 use different methods to classify each point of a dataset. They use this classification information later to assign optical properties to voxels. While these approaches utilize neural networks to assign optical properties, the method presented here aims at classifying datasets into categories. The category information is subsequently used as an knowledge to visualize the dataset.
@cite_15 @cite_14 @cite_10 @cite_6
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Classification of three-dimensional MRI images plays an important role in the volume reconstruction for a variety of medical image analysis, computer-aided diagnosis, three-dimensional reconstruction and visualization applications. By using density, gradient magnitude, and curvature as input parameters, and by defining a set of objective enter ions for the classification evaluation, a novel automatic classification method for volume reconstruction of MRI images by using GRNN is presented in this paper, experiment results demonstrate that the proposed method can not only efficient classify MRI images, but also alleviate people from the time-consuming parameter tune process of previous classification method. This paper analyzes how to introduce machine learning algorithms into the process of direct volume rendering. A conceptual framework for the optical property function elicitation process is proposed and particularized for the use of attribute-value classifiers. The process is evaluated in terms of accuracy and speed using four different off-the-shelf classifiers (J48, Naive Bayes, Simple Logistic and ECOC-Adaboost). The empirical results confirm the classification of biomedical datasets as a tough problem where an opportunity for further research emerges. In the traditional volume visualization paradigm, the user specifies a transfer function that assigns each scalar value to a color and opacity by defining an opacity and a color map function. The transfer function has two limitations. First, the user must define curves based on histogram and value rather than seeing and working with the volume itself. Second, the transfer function is inflexible in classifying regions of interest, where values at a voxel such as intensity and gradient are used to differentiate material, not talking into account additional properties such as texture and position. We describe an intuitive user interface for specifying the classification functions that consists of the users painting directly on sample slices of the volume. These painted regions are used to automatically define high-dimensional classification functions that can be implemented in hardware for interactive rendering. The classification of the volume is iteratively improved as the user paints samples, allowing intuitive and efficient viewing of materials of interest. In the visualization of 3D medical data, the appropriateness of the achieved result is highly dependent on the application. Therefore, an intuitive interaction with the user is of utter importance in order to determine the particular aim of the visualization. In this paper, we present a novel approach for the visualization of 3D medical data with volume rendering combined with AR-based user interaction. The utilization of augmented reality (AR), with the assistance of a set of simple tools, allows the direct manipulation in 3D of the rendered data. The proposed method takes into account regions of interest defined by the user and employs this information to automatically generate an adequate transfer function. Machine learning techniques are utilized for the automatic creation of transfer functions, which are to be used during the classification stage of the rendering pipeline. The validity of the proposed approach for medical applications is illustrated.