Misdiagnosis of subarachnoid hemorrhage (SAH) is as high as 25%, due to the difficulties discerning bleeding since SAH re-circulates within the subarachnoid space to make the change in grayscales of bleeding very subtle. For computer assisted diagnosis (CAD) of SAH, its state-of-the-art is reviewed: SAH can be in the form of effacement of sulci or high signal with low contrast on computed tomography (CT) images, and is difficult to be segmented using traditional segmentation methods; existing CAD system of SAH consisted of 2 steps (approximation of subarachnoid space via atlas registration or distance transformation and judging abnormalities of grayscale distribution in the approximated subarachnoid space by means of pattern recognition), and can yield erroneous conclusions when the bleeding is small or effacement of sulci occurs. Details of the algorithms are given to approximate subarachnoid space based on distance transform and to recognize SAH based on support vector machine. Possible ways to enhance the performance of CAD of SAH are pointed out: to develop new image processing methods such that high signals with low contrast as well as sulci can be well segmented and quantified.