A Neural Network Classifier of Volume Datasets

TL;DR

This paper introduces a neural network-based classifier that automatically identifies dataset types from volume scans using intensity and gradient histograms, aiding autonomous visualization systems especially when meta-data is incomplete.

Contribution

It presents a simple, effective neural network approach for classifying volume datasets into categories based on histogram features, even with minimal training data.

Findings

Successfully classified datasets into specific categories with only one training example.

High computational efficiency and ease of implementation.

Effective on 80 datasets across 3 classes plus a miscellaneous class.

Abstract

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.