An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network

Abstract

Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classification errors in the results of image segmentation.

Objective: An automated algorithm based on multi-layer perceptron neural networks (MLPNN) is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features) and is classified using a MLPNN into one of the three classes WM and GM or unknown.

Results: The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively.

Conclusion: The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest.

Keywords: Artificial neural networks; Image segmentation; Multi-layer perceptron.

Figure 1

Block diagram of the proposed method

Figure 2

Correcting intensity non-uniformity. (a) Original image, (b)Correction of non-uniformity using the FSL software.

Figure 3

Segmentation results for MR image ISBR_13_13. (a) original image, (b) manually segmented,  and (c) segmented using the proposed method.

Figure 4

Segmentation results for MR image ISBR_202_3. (a) Original image, (b) manually segmented,  and (c) segmented using the proposed method.