Detection of Infarct Lesions From Single MRI Modality Using Inconsistency Between Voxel Intensity and Spatial Location—A 3-D Automatic Approach

Abstract

Abstract-Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multispectral 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 3-D 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-intensitybased 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 prespecified. 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 3-D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis lesion detection.Index Terms-Fuzzy c-mean (FCM), MRI, lesion detection, tissue probability map.

Abnormality Detection via Iterative Deformable Registration and Basis-Pursuit Decomposition

“…In reality, however, there are hundreds of pathologies causing imaging abnormalities with diverse characteristics in different imaging modalities. For example, in stroke lesions, a hemorrhage appears as a bright region and ischemic stroke appears as a dark region in Computed Tomography scans [2], while in T 1 -weighted MR images an infarct lesion is shown with intensities similar to cerebrospinal fluid (CSF) or grey matter (GM) [3]. …”

Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map

“…Automatic algorithms for segmentation for acute infarct in MRI have been reported [10–15]. The unsupervised method developed by Li et al was based on a multistage procedure including image preprocessing, calculation of tensor field, measurement of diffusion anisotropy, segmentation of infarct volume based on adaptive multiscale statistical classification, and partial volume voxel reclassification [11].…”

“…Gupta et al identified the infarct slices and the hemisphere automatically in DWI based on the difference in the percentile characteristics of intensity normalized images and parameters of infarct slice identification and infarct hemisphere identification [14]. Shen et al detected infarct lesions based on the voxel intensity segmentation and the spatial location of tissue distribution [15]. …”

White matter hyperintensities segmentation: a new semi-automated method

“…Indeed, complex automatic or semi-automatic computer-based segmentation procedures require a large amount of technical resources that may not be available in clinical settings. Furthermore, multi-spectral approaches are not always accessible in clinical practice since the acquisition of all these images is cost-intensive and requires long processing time (Shen et al, 2008). Conversely, FLAIR-based approaches may, in sporadic cases, overestimate the WMH load due to FLAIR typical high intensity appearance in cortical areas, such as the septum pellucidum, and low artifacts in the fourth ventricle where a large percentage of false positive is detected (Wang et al, 2012).…”