A Clustering Based Transfer Function for Volume Rendering Using Gray-Gradient Mode Histogram

Yisha Lan,Yonghong Peng,Weihong Huang,Yanzhao Zhang,Yimin Ding,Zhicheng Wang,E Y K Ng,Jie Su,Yongqiang Cheng,Chenxi Huang,Xin Luo,Xuezhong Zhou,Wenliang Che

doi:10.1109/access.2019.2923080

Abstract

Volume rendering is an emerging technique widely used in the medical field to visualize human organs using tomography image slices. In volume rendering, sliced medical images are transformed into attributes, such as color and opacity through transfer function. Thus, the design of the transfer function directly affects the result of medical images visualization. A well-designed transfer function can improve both the image quality and visualization speed. In one of our previous paper, we designed a multi-dimensional transfer function based on region growth to determine the transparency of a voxel, where both gray threshold and gray change threshold are used to calculate the transparency. In this paper, a new approach of the transfer function is proposed based on clustering analysis of gray-gradient mode histogram, where volume data is represented in a two-dimensional histogram. Clustering analysis is carried out based on the spatial information of volume data in the histogram, and the transfer function is automatically generated by means of clustering analysis of the spatial information. The dataset of human thoracic is used in our experiment to evaluate the performance of volume rendering using the proposed transfer function. By comparing with the original transfer function implemented in two popularly used volume rendering systems, visualization toolkit (VTK) and RadiAnt DICOM Viewer, the effectiveness and performance of the proposed transfer function are demonstrated in terms of the rendering efficiency and image quality, where more accurate and clearer features are presented rather than a blur red area. Furthermore, the complex operations on the two-dimensional histogram are avoided in our proposed approach and more detailed information can be seen from our final visualized image.

Highlights

In recent years, a series of imaging techniques have been widely used in medical field, such as computer tomography (CT), single-photon emission computed tomography (SPECT), magnetic resonance Imaging (MRI), dynamic single-photon emission computed tomography (D-SPECT), intravascular ultrasound (IVUS), optical coherence tomography (OCT), etc [1]–[3]
We propose a two-dimensional transfer function based on clustering analysis of gray-gradient mode histogram to divide volume data accurately
On the basis of two-dimensional transfer function, we designed and implemented an intuitive and effective automatic transfer function based on clustering analysis of graygradient mode histogram

Summary

Introduction

A series of imaging techniques have been widely used in medical field, such as computer tomography (CT), single-photon emission computed tomography (SPECT), magnetic resonance Imaging (MRI), dynamic single-photon emission computed tomography (D-SPECT), intravascular ultrasound (IVUS), optical coherence tomography (OCT), etc [1]–[3]. Medical images plays an important role in detecting internal information and three-dimensional reconstruction [4]. Two main streams of visualization are surface rendering and volume rendering [5]. Surface rendering uses segmentation technology to extract surface information of an object [6]–[8]. Volume rendering transforms a three-dimensional data fields into a two-dimensional image, including the direct visualization of scanned volume data [9]–[11]. Due to the important role of transfer function on volume rendering, increasing number of research has been carried out on transfer function, both in gradient characteristics based

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Methods

Conclusion