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

Abstract

Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

“…Information about the stroke evolution phase is sometimes omitted (Seghier et al, 2008; Forbes et al, 2010) or, if mentioned, not clearly defined (Saad et al, 2011; Muda et al, 2015). Where provided, the definition of acute stroke often mixes with the sub-acute phase (Ghosh et al, 2014; Mah et al, 2014; Tsai et al, 2014). Only a few studies give details on pathological inclusion and exclusion criteria of the data (James et al, 2006; Maier et al, 2015c), although these are important characteristics: Results obtained on right-hemispheric stroke only (Dastidar et al, 2000) are not comparable to ones omitting small lesions (Mah et al, 2014) nor to those obtained from two central axial slices of each volume (Li et al, 2004).…”

Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier

“…44 An automated approach based on unsupervised classi¯cation with fuzzy c-means clustering with the self-adjusted intensity thresholds detected cerebral infarct lesions with a DSI of 89.9%. 45 An automated approach based on Bayesian MRF was successfully used to segment stroke lesion in FLAIR MRI images with a Dice similarity coe±cient of 0.60. 46 However, the proposed method called HFCM, that integrated the structures of rough sets and fuzzy sets in c-means clustering with self-adjusting thresholding, achieved better results with average accuracies of 87.4%, 89.6% and 95.8% in J48, random forest and SVM classi¯ers, respectively.…”

Segmentation of Hyper-Acute Ischemic Infarcts from Diffusion Weighted Imaging Based on Support Vector Machine

“…For a fair and relevant comparison, we have tried our best to implement them to attain the best performance on the 98 datasets within 6 hours from symptom onset. The Dice, sensitivity and specificity for [3] ( [4]) are respectively 0.215 ± 0.213 (0.597 ± 0.204), 0.565 ± 0.346 (0.585 ± 0.221) and 0.983 ± 0.024 (0.999 ± 0.001).…”

“…The proposed method has been compared with divergence measure based method (DM method) [3] and FCM based method [4]. For a fair and relevant comparison, we have tried our best to implement them to attain the best performance on the 98 datasets within 6 hours from symptom onset.…”

“…Prakash et al [3] segmented infarcts from DW images (DWIs) by first identifying axial slices with ischemic lesions followed by binarization with a global DWI intensity threshold derived from histogram divergence. Tsai and co-workers segmented acute cerebral infarcts from DWIs and apparent diffusion coefficient (ADC) maps base on fuzzy C-means clustering (FCM) determining the histographic distribution and defining self-adjusted intensity thresholds [4]. We investigate support vector machine (SVM) on hyper-acute ischemic infarcts segmentation from DW imaging.…”