Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making

In humans, the abnormality in brain arises due to various reasons and the ischemic-stroke (IS) is one of the major brain syndromes to be diagnosed and treated with appropriate procedures. The brain-signals and brain-images are widely considered for the clinical level diagnosis of IS. The proposed research considered the brain-image (MRI) based assessment of IS, due to its accuracy and multi modality nature. The MRI slices with modalities, such as diffusion-weighted (DW), flair and T1 are considered for the assessment. This work implements the following procedures to extract the IS lesion (ISL); (i) pixel level image fusion based on principal-component-analysis (PCA), (ii) image thresholding using cuckoo-search (CS) and Tsallis entropy, (iii) watershed based ISL extraction, and (iv) comparison of segmented ISL with the ground-truth-image (GTI). To confirm the clinical significance of the proposed work, the test images are collected from the benchmark ISLES2015 database. The results of this research confirms that, the fused brain MRI slices with DW and flair (DW + flair) modality facilitate to attain improved mean values of Jaccard-Index (83.17 ± 7.32%), Dice (88.51 ± 4.76%) and segmentation accuracy (97.34 ± 1.62%) compared to other images. This research confirms that, pixel level fusion will help to achieve better result during the clinical level disease diagnosis.