Bacterial foraging optimization based brain magnetic resonance image segmentation

TL;DR

This paper introduces a novel brain MRI segmentation method combining multilevel thresholding with bacterial foraging optimization, improving accuracy over traditional edge detection techniques.

Contribution

It presents a new segmentation approach that optimizes threshold selection using bacterial foraging optimization, enhancing brain MRI segmentation accuracy.

Findings

Outperforms traditional edge detectors like Canny and Sobel.

Achieves higher accuracy in brain MRI segmentation.

Validated using edge detector evaluation parameters.

Abstract

Segmentation partitions an image into its constituent parts. It is essentially the pre-processing stage of image analysis and computer vision. In this work, T1 and T2 weighted brain magnetic resonance images are segmented using multilevel thresholding and bacterial foraging optimization (BFO) algorithm. The thresholds are obtained by maximizing the between class variance (multilevel Otsu method) of the image. The BFO algorithm is used to optimize the threshold searching process. The edges are then obtained from the thresholded image by comparing the intensity of each pixel with its eight connected neighbourhood. Post processing is performed to remove spurious responses in the segmented image. The proposed segmentation technique is evaluated using edge detector evaluation parameters such as figure of merit, Rand Index and variation of information. The proposed brain MR image segmentation technique outperforms the traditional edge detectors such as canny and sobel.