Noor Khairiah A. Karim

New image enhancement technique for WMH segmentation of MRI FLAIR image

This paper proposes a new image enhancement technique known as Average Intensity Replacement based on Adaptive Histogram Equalization (AIR-AHE) for FLAIR image based on intensities and contrast mapping techniques. The proposed algorithm consists of partial contrast stretching, contrast limiting enhancement, window sliding neighborhood operation and new pixel centroid replacement. The fluid attenuated inversion recovery (FLAIR) sequences of MRI images which are used for segmentation have low contrast. Therefore, contrast stretching is used to improve the quality of the image. After improving the quality of image, the regions of high intensity are determined to represent potential WMH areas. The result shows that the image has a moderate enhancement on the WMH region which is significant to the image contrast enhancement. With complete brightness preservation, the proposed method gives a relatively natural brightness improvement on the WMH of the periventricular region.

Assessing intensity of white matter hyperintensity and normal appearing white matter in healthy adults

There have been interest on white matter hyperintensity (WMH) and normal white matter (WM) changes reported but have not yet been fully characterized. Different image sequences of magnetic resonance imaging (MRI) scans may shows different gray scale intensity. However, it is difficult to differentiate the intensity of normal WM and WMH as their intensities are visually not much different. In this study, normal WM and WMH changes were investigated based on their intensity to determine the correlation of WMH types and severity in brain of healthy subjects. The assessment was performed by using fully automatic WMH detection and computing algorithms. The main brain regions were segregated into gray matter (GM), normal WM, cerebrospinal fluid (CSF) and non-brain tissue. From the results, it shows that there was significant difference seen between normal appearing WM and hyperintense WM in terms of their intensity levels. The study shows that the development of WMH is prevalent to the occasion of normal WM changes. This is shows that WMH intensity reflects the level of WMH classes and severity; however, further investigations are needed to improve their efficiency.

WMH Detection Using Improved AIR-AHE-Based Algorithm for Two-Tier Segmentation Technique

The segmentation of magnetic resonance imaging (MRI) brain images could be implemented using any technique, either automatic or manual. The different methods commonly show different results because their performance relies on the segmentation precision and accuracy. In this paper, a new image segmentation algorithm is proposed based on k-means and AIR-AHE clustering algorithm to automatically segment and classify WMH severity in brain white matter region. The objective of this new segmentation algorithm is to minimize the false positive (FP) in white matter hyper-intensity (WMH) detection and hence will increase the WMH detection accuracy in MRI images. The proposed algorithm is implemented on two-tier segmentation system by identifying the edge of WMH and WM boundary for image mapping purpose. T2-weighed imaging (T2-WI) and fluid-attenuated inversion recovery (FLAIR) MRI sequences are used for mapping most precise WMH region of interest (ROI). From the experimental results, the proposed algorithm produces significant improvement in terms of correct WMH localization and reduces the false WMH detection. Based on the accuracy and capabilities of the proposed algorithm, this algorithm is suitable to be implemented to aid radiologist in the image analysing.

A New Technique for K-Means Cluster Centers Initialization of WMH Segmentation

K-means algorithm is the most common clustering algorithm being used in medical image processing application. However, the performance of k-means clustering algorithms which converges to numerous local minima would rely on the best initial cluster centers. Generally initial cluster centers are selected randomly and the results are varying on different runs of the algorithm on the same dataset. In this paper, a new method for selecting the best initial centers of k-means clustering is proposed for grouping brain tissues of MRI images. The selection of initial cluster centers namely as Gray Scale Region Intensities (GSRI), is made based on the average intensity value of grayscale region of the images. The proposed method is compared to other method namely as Gray Scale Division Equality (GSDE) which the initial centers were computed by dividing the gray scale 255 and number of clusters. The results show that GSRI outperformed GSDE method in terms of refined segmented regions and converge to local minima with higher iteration number. As a conclusion, it is observed that the newly proposed method has good performance to obtain the initial cluster centers for the k-means algorithm.

Cerebral microbleeds (CMB) from MRI brain images: Review paper

Over the last decade, the sequence of multiecho gradient recalled echo (GRE) T2*-weighted imaging has shown the loss of signal in terms of ‘black dots’ in patients who have spontaneous intra-cerebral hemorrhage (ICH), hypertension, ischaemic stroke and in healthy elderly persons. These ‘black dots’ or cerebral microbleeds are forms of blood breakdown caused by the abnormalities of small vessels resulting in the leakage of blood inside the brain. The presence of cerebral microbleeds persists for a certain period which can be the predictor or marker for certain diseases which are related to small vessel disease. This review paper summarizes the available definition, pathological and detection including clinical studies regarding the microbleeds as the indicator or marker of future diseases in clinical practice.