Precise segmentation rendering for medical images based on maximum entropy processing

Authors:
Tsair-Fwu Lee;Ming-Yuan Cho;Chin-Shiuh Shieh;Pei-Ju Chao;Huai-Yang Chang
Affiliations:
Department of Electrical Engineering, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan, ROC;Department of Electrical Engineering, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan, ROC;Department of Electronic Engineering, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan, ROC;Department of Radiation Oncology, Kaohsiung Yuan's General, Hospital, Kaohsiung, Taiwan, ROC;Department of Radiation Oncology, Chang Gung Memorial Hospital, Kaohsiung, Taiwan, ROC
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

Citing 5
Cited 2

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
The image processing handbook (3rd ed.)

The image processing handbook (3rd ed.)
Digital Image Processing

Digital Image Processing
Symmetric region growing

IEEE Transactions on Image Processing

Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis

Journal of Medical Systems
Intensity modulated radiotherapy target volume definition by means of wavelet segmentation

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Precision is definitely required in medical treatments, however, most three-dimensional (3-D) renderings of medical images lack for required precision. This study aimed at the development of a precise 3-D image processing method to discriminate clearly the edges. Since conventional Computed Tomography (CT), Positron Emission Tomography (PET), or Magnetic Resonance Imaging (MRI) medical images are all slice-based stacked 3-D images, one effective way to obtain precision 3-D rendering is to process the sliced data with high precision first then to stack them together carefully to reconstruct a desired 3-D image. A recent two-dimensional (2-D) image processing method known as the entropy maximization procedure proposed to combine both the gradient and the region segmentation approaches to achieve a much better result than either alone seemed to be our best choice to extend it into 3-D processing. Three examples of CT scan data of medical images were used to test the validity of our method. We found our 3-D renderings not only achieved the precision we sought but also has many interesting characteristics that shall be of significant influence to the medical practice.