Today I read a paper titled “A Neural Network Classifier of Volume Datasets”
The abstract is:
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