Computed Tomography (CT) has been the typical imaging modality for the preliminary assessment of patients presenting with acute stroke signs. Since quantum noise degrades the visual quality of CT images, the existing contrast enhancement methods fail to provide a good contrast CT. Principal Component Analysis (PCA) based fusion of contrast and edge enhancement imaging has been implemented in this research work for highlighting the affected region during stroke diagnosis. A histogram modification technique called Adaptive gamma correction with weighting distribution (AGCWD) has been proposed for enhancing the contrast, and gradient operators are involved in enhancing the edges. No-reference-based performance estimation metrics are used for evaluating the objective quality of the enhanced CT images. Experimental result shows that the proposed PCA fusion method significantly enhances the visual perception of the CT images than those produced using existing methods by assisting physicians in the detection of stroke signs during the initial hours of occurrence.