Abstract
Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) constituents. Evaluation metrics such as cluster validity functions using partition coefficients and partition entropy; area error metrics such as false positive, true positive, true negative and false negative (FN); similarity index, sensitivity and specificity were used to evaluate the performance of both techniques. A comparative analysis of the experimental results revealed that in most instances, FKM segmentation technique is preferable to FCM segmentation technique for brain MRI segmentation task.
Highlights
For accurate information elicitation from medical images, image segmentation is a crucial task that cannot be undermined
The result showed that Fuzzy K-Means (FKM) segmentation technique is more accurate and reliable in white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) segmentation when compared to Fuzzy C-Means (FCM), though the results of FCM segmentation is not totally bad
Using partition coefficients as a metric, both techniques have the peak partition coefficients at the 3rd cluster points across all the datasets. This showed that the techniques could correctly classify the datasets into three clusters, though, FKM records a higher value when compared to FCM across all the datasets used which showed that FKM classifies at a faster rate when compared to FCM
Summary
For accurate information elicitation from medical images, image segmentation is a crucial task that cannot be undermined. It is an essential routine because the eventual outcome of the analysis will determine the success of the pattern image segmentation stage. This involves measuring and visualizing the brain’s anatomical structures, analyzing brain changes, delineating pathological regions, surgical planning and image-guided interventions [1]. With focus on brain Magnetic Resonance Image (MRI) and iris images, segmentation of MRI image entails classifying the image into its constituent components: the white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) while segmenting IRIS image entails classifying the Iris image into iris setosa, iris versicolour, and iris virginica. An efficient image segmentation technique must be able to uniquely and correctly classify these dataset into their constituent components to an acceptable degree
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