Abstract—Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multi-spectral magnetic resonance imaging (MRI) modalities were often employed for this purpose. However, the high costs of MRI scan and the severity of patient conditions restrict the collection of multiple images. Therefore, in this paper, a new 3D automatic lesion detection approach was proposed, which required only a single type of anatomical MRI scan. It was developed on a theory that when lesions were present, the voxel intensity based segmentation and the spatial location based tissue distribution should be inconsistent in the regions of lesions. The degree of this inconsistency was calculated which indicated the likelihood of tissue abnormality. Lesions were identified when the inconsistency exceeded a defined threshold. In this approach, the intensity based segmentation was implemented by the conventional fuzzy c-mean (FCM) algorithm, while the spatial location of tissues was provided by prior tissue probability maps. The use of simulated MRI lesions allowed us to quantitatively evaluate the performance of the proposed method, as the size and location of lesions were pre-specified. The results showed that our method effectively detected lesions with 40-80% signal reduction compared to normal tissues (Similarity Index>0.7). The capability of the proposed method in practice was also demonstrated on real infarct lesions from 15 stroke patients, where the lesions detected were in broad agreement with true lesions. Furthermore, a comparison to a statistical segmentation approach presented in the literature suggested that our 3D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis (MS) lesion detection.