This paper presents an image-based radar detector, named neighborhood difference order statistics (NDOS) detector. Different from the classic likelihood ratio detector, the proposed detector treats the echo spectrum as an image and determines the existence of a target by comparing the difference between the test cell and its adjacent cells with a threshold. The closed-form expressions of probabilities of detection and false alarm are derived under Gaussian noise background and Swerling I target model. It is proved that the detection performance of the proposed detector approaches the optimal likelihood ratio detector when the homogenous noise power is known. When the noise power is unknown, we modify the detector into cell-averaging (CA) NDOS detector by estimating the noise power. Analytical derivations show that the CA-NDOS detector holds the constant false alarm rate (CFAR) identity. Moreover, CA-NDOS detector possesses a better detection performance compared with two typical CFAR algorithms under the condition of typical reference window size.