Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field
Introduction
White matter lesions (WMLs) have been associated with various diseases such as stroke [1], Alzheimer's disease [2], [3], late life depression [4] and impairment of gait [5] etc. Different studies have shown a strong relationship between increased WMLs volume and cognitive decline [6], [7]. Magnetic resonance imaging (MRI) is used for the visualization of WMLs. WMLs appear as hyperintense signal in T2-weighted and fluid attenuated inversion recovery (FLAIR) MRI images. Manual segmentation and quantification of white matter lesion is time consuming, expensive, subjective and impractical for large scale longitudinal studies. Several supervised and unsupervised algorithms are proposed in the literature for the automatic segmentation of white matter lesions [8], [9], [10], [11], [12], [13], [9]. Among the many existing segmentation methods, the accurate segmentation of white matter lesions remains a challenging task.
Low contrast and intensity inhomogeneity are the major challenges of automatic WMLs segmentation in brain MRI images. Histogram equalization [14] and adaptive histogram equalization [14] are two well known contrast enhancement methods in image processing. Since the pathology is not evenly distributed in the medical images therefore global stretching of the histogram as proposed in histogram equlization can suppress the pathology, on the contrary local stretching of the histogram as proposed in adaptive histogram equalization can over-amplify the intensity with false identification of pathology [14]. Presently in medical image processing, contrast enhancement filters are developed considering the properties of the corresponding image modality [15]. FLAIR MRI is the commonly used MRI modality for white matter lesion segmentation [16]. But a few methods have been proposed for the contrast enhancement of FLAIR intensity. Khademi et al. [9] have proposed a method to enhance the contrast of the FLAIR intensity using gradient information. In most of the cases white matter lesions have fuzzy edges thus gradient information based intensity enhancement does not work for all cases. The spatial distribution of the white matter lesion can be useful information for the contrast enhancement of FLAIR intensity. As we know, the ratio of the number of white matter lesion pixel is very low compared to the number of healthy pixel, therefore if we select a set of non-overlapping pixels from the brain area then majority of them have the high probability of being a member of healthy region. Based on this assumption we hypothesize that a lesion pixel will show high contrast with respect to a set of non-overlapping pixels.
Apart from intensity information, anatomical and textural information are also used to segment white matter lesions (WMLs) [8], [10], [11], [12], [13]. Geremia et al. [17] proposed a method where a random forest classifier [18] is used to segment WMLs. The random forest classifier is trained by using multichannel MRI intensity and long range contextual features. But in their method pixel neighbourhood information are not considered for the final segmentation. Since Markov Random Field (MRF) models spatial dependency between neighbouring pixels. Hence it is used widely in white matter lesion segmentation [19], [20]. In those methods image properties such as intensity, gradient, texture, etc. are not included in the neighbourhood model; they only consider the initial labelling information of neighbourhood pixels for final segmentation. As a result, they might show over-smoothing when the number of lesion is small. We hypothesize that incorporation of image properties into the neighbourhood structure of MRF can improve the segmentation accuracy when the number of lesions is small.
In this paper, we have proposed a novel method to enhance the contrast of WMLs in the FLAIR MRI images. Then the enhanced intensity is used with the spatial location and anatomical information to train a random forest classifier to obtain the initial segmentation. Following that a reliable and robust MRF is proposed for obtaining final segmentation by removing false positive WMLs. Our proposed MRF maximizes the segmentation accuracy by using a novel edge function.
The rest of the paper is organized as follows: in Section 2, the details of our method is described. The results and discussions are presented in Section 3. Finally, Section 4 concludes the paper.
Section snippets
Dataset
We primarily investigated a dataset of ENVISion study [21]. The dataset consist of 24 MRI images, selected from a larger cohort of elderly subjects with hypertension. In order to include a wide variety of WMLs severity, subjects are selected based on the severity of WMLs. Monash university health and research ethics committee have approved the study and all subjects gave written informed consent prior to participation. ENVISion dataset contains two modalities of MRI named as T1-weighted and
Results and discussion
White matter lesion (WML) loads are typically divided into three groups: severe lesion load (>10 ml), moderate lesion load (between 6 ml to 10 ml) and mild lesion load (0 ml to 5 ml) [35]. In our method, random forest (RF) classifier is used for the initial segmentation of white matter lesions as mentioned in Section 2.5. Then Markov Random Field (MRF) is used to remove false positive lesions and obtain the final segmentation. We have compared four state of the art classifiers which are random
Conclusion
We have developed a fully automated method for the detection of white matter lesions (WMLs) in brain MR images using enhanced intensity, anatomical and spatial feature based random forest and Markov Random Field (MRF). Our proposed MRF is based on a novel edge potential function that uses spatial and intensity interaction in a local neighbourhood to obtain the optimal segmentation outcome. The proposed method is evaluated on two distinct datasets (ENVISion dataset with WMLs and MICCAI dataset
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