A Neural Network Classifier of Volume Datasets

by   Dzenan Zukic, et al.

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.


page 1

page 2

page 3

page 4


Neural networks in 3D medical scan visualization

For medical volume visualization, one of the most important tasks is to ...

Non-Uniform Conductivity Estimation for Personalized Brain Stimulation using Deep Learning

Electromagnetic stimulation of the human brain is a key tool for the neu...

Deep Learning-based Type Identification of Volumetric MRI Sequences

The analysis of Magnetic Resonance Imaging (MRI) sequences enables clini...

Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation

Segmentation of medical images using seeded region growing technique is ...

MessageNet: Message Classification using Natural Language Processing and Meta-data

In this paper we propose a new Deep Learning (DL) approach for message c...

Visualization of variations in human brain morphology using differentiating reflection functions

Conventional visualization media such as MRI prints and computer screens...

Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images

To train a robust deep learning model, one usually needs a balanced set ...

Please sign up or login with your details

Forgot password? Click here to reset