Tumor segmentation from breast magnetic resonance images using independent component texture analysis

Sheng-Chih Yang,Tsai-Rong Chang,Chi-Yuan Lin,Chieh-Ling Huang

doi:10.1117/1.jei.22.2.023027

Abstract

A new spectral signature analysis method for tumor segmentation in breast magnetic resonance images is presented. The proposed method is called an independent component texture analysis (ICTA), which consists of three techniques including independent component analysis (ICA), entropy-based thresholding, and texture feature registration (TFR). ICTA was mainly developed to resolve the inconsistency in the results of independent components (ICs) due to the random initial projection vector of ICA and then accordingly determine the most likely IC. A series of experiments were conducted to compare and evaluate ICTA with principal component texture analysis, traditional ICA, traditional principal component analysis (PCA), fuzzy c-means, constrained energy minimization, and orthogonal subspace projection methods. The experimental results showed that ICTA had higher efficiency than existing methods.

Highlights

Breast magnetic resonance imaging (MRI) has gradually gained much popularity in clinical use because results have shown that the screening accuracy of MRI is significantly higher than that of mammography and ultrasound.[1]
One demonstrated case which contains a medium size tumor is selected as an example and shown in Fig. 4 with four different sequences: PD-weighted spectral image acquired with TR∕TEðrepetition time∕echo timeÞ 1⁄4 3000∕ 15 ms, T1-weighted spin-echo image acquired with TR∕TE 1⁄4 832∕20 ms, T2-weighted spin-echo image acquired with TR∕TE 1⁄4 3000∕105 ms, and T1_FS (T1weighted fat-saturated image)
The experimental results obtained by the traditional independent component analysis (ICA) method are shown in Table 2, where the highest classification rates are given in bold face

Summary

Introduction

Breast magnetic resonance imaging (MRI) has gradually gained much popularity in clinical use because results have shown that the screening accuracy of MRI is significantly higher than that of mammography and ultrasound.[1] Currently, doctors generally rely on breast MRI for obtaining the region of tumor since it is usually an important sign of breast cancer for diagnosis Based on these considerations, Paper 12418 received Oct. 14, 2012; revised manuscript received May 5, 2013; accepted for publication Jun. 3, 2013; published online Jun. 20, 2013. Several methods have been developed for processing multispectral MRIs, such as orthogonal subspace projection (OSP)[6] and Kalman filter,[7] but both of them require prior knowledge With these considerations, we have developed a new method called the independent component texture analysis (ICTA) to segment the tumor region in multispectral breast MRIs. ICTA comprises three techniques: independent component analysis (ICA), entropy-based thresholding (ET), and texture feature registration (TFR). ICA, originally, is a blind source separation (BSS) method in the signal processing field, and it is a powerful tool for feature extraction and data representation such as speech recognition, image recognition, and statistical analysis.[8]

Methods

Results

Conclusion