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. 2020 May 22;10(3):333-344.
doi: 10.1007/s13534-020-00158-5. eCollection 2020 Aug.

Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection

R Kanchana  1 , R Menaka  1
Affiliations

Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection

R Kanchana et al. Biomed Eng Lett. .

Abstract

Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.

Keywords: CAD; CT images; Classification; Features; Histogram; Ischemic stroke.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interests.

Figures

Fig. 1
Fig. 1
Clinical validation dataset showing ischemic stroke lesion region circled
Fig. 2
Fig. 2
Flowchart showing the methodology
Fig. 3
Fig. 3
a Input CT image, b Skull stripped image c Segmented ischemic stroke lesion
Fig. 4
Fig. 4
LR showing CA’s of selected 8 FG’s for fourfolds
Fig. 5
Fig. 5
SVMC showing CA’s of selected 8 FG’s for fourfolds
Fig. 6
Fig. 6
RFC showing CA’s of selected 8 FG’s for fourfolds
Fig. 7
Fig. 7
NNC showing CA’s of selected 8 FG’s for fourfolds
Fig. 8
Fig. 8
Normal and abnormal ischemic stroke lesion region differentiation plot using a mean feature, b coarseness index feature, c short run emphasis feature, d contrast feature, e Humoment4 feature
See this image and copyright information in PMC

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