Objective:　The　traditional　ICM　is　widely　used　in　applications,　such　as　image　edge　detection　and　image　segmentation.　However,　several　model　parameters　must　be　set,　which　tend　to　lead　to　reduced　accuracy　and　increased　cost.　As　medical　images　have　more　complex　edges,　contours　and　details,　more　suitable　combinatorial　algorithms　are　needed　to　handle　the　pathological　diagnosis　of　multiple　cerebral　infarcts　and　acute　strokes,　resulting　in　the　findings　being　more　applicable,　as　well　as　having　good　clinical　value.　Methods:　To　better　solve　the　medical　image　fusion　and　diagnosis　problems,　this　paper　introduces　the　image　fusion　algorithm　based　on　the　combination　of　NSCT　and　improved　ICM　and　proposes　low-frequency,　sub-band　fusion　rules　and　high-frequency　sub-band　fusion　rules.　The　above　method　is　applied　to　the　fusion　of　CT/MRI　images,　subsequently,　three　other　fusion　algorithms,　including　NSCT-SF-PCNN,　NSCT-SR-PCNN　and　Adaptive-PCNN　are　compared,　and　the　simulation　results　of　image　fusion　are　analyzed　and　validated.　Results:　According　to　the　experimental　findings,　the　suggested　algorithm　performs　better　than　other　fusion　algorithms　in　terms　of　five　objective　evaluation　metrics　or　subjective　evaluation.　The　NSCT　transform　and　the　improved　ICM　were　combined,　and　the　outcomes　were　evaluated　against　those　of　other　fusion　algorithms.　The　CT/MRI　medical　images　of　healthy　brain　tissue,　numerous　cerebral　infarcts　and　acute　strokes　were　combined　using　this　technique.　Conclusion:　Medical　image　fusion　using　Adaptive-PCNN　produces　satisfactory　results,　not　only　in　relation　to　improved　image　clarity　but　also　in　terms　of　outstanding　edge　information,　high　contrast　and　brightness.　©　2022