Academic radiology · 2008
Computer-aided detection of ischemic lesions related to subcortical vascular dementia on magnetic resonance images.
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Yamashita Yasuo, Arimura Hidetaka, Tsuchiya Kazuhiro
Аннотация
RATIONALE AND OBJECTIVES: The purpose of this study was to develop an automated method for detection of the hyperintense ischemic lesions related to subcortical vascular dementia based on conventional magnetic resonance images (T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery images [FLAIR]).
MATERIALS AND METHODS: Our proposed method was based on subtraction between the T1-weighted image and the FLAIR image. First, a brain region was extracted by an automated thresholding technique based on a linear discriminant analysis for a pixel value histogram. Next, for enhancement of ischemic lesions, the T1-weighted image was subtracted from the fluid-attenuated inversion-recovery image. Ischemic lesion candidates were identified using a multiple gray-level thresholding technique and a feature-based region-growing technique on the subtraction image. Finally, an artificial neural network trained with 15 image features of the ischemic candidates was used to remove false-positives. We applied our method to nine patients with vascular dementia (age range, 64-94 years, mean age, 69.4 years; four males and five females), who were scanned on a 1.5-T magnetic resonance unit.
RESULTS: Our method achieved a sensitivity of 90% with 4.0 false-positives per slice in detection of ischemic lesions. The overlap measure between ischemic lesion areas obtained by our method and a neuroradiologist was 60.7% on average. The ratio of ischemic lesion area to the whole brain area obtained by our method correlated with that determined by a neuroradiologist with a correlation coefficient of 0.911.
CONCLUSION: Our preliminary results suggest that the proposed method may have feasibility for evaluation of the ischemic lesion area ratio.
MATERIALS AND METHODS: Our proposed method was based on subtraction between the T1-weighted image and the FLAIR image. First, a brain region was extracted by an automated thresholding technique based on a linear discriminant analysis for a pixel value histogram. Next, for enhancement of ischemic lesions, the T1-weighted image was subtracted from the fluid-attenuated inversion-recovery image. Ischemic lesion candidates were identified using a multiple gray-level thresholding technique and a feature-based region-growing technique on the subtraction image. Finally, an artificial neural network trained with 15 image features of the ischemic candidates was used to remove false-positives. We applied our method to nine patients with vascular dementia (age range, 64-94 years, mean age, 69.4 years; four males and five females), who were scanned on a 1.5-T magnetic resonance unit.
RESULTS: Our method achieved a sensitivity of 90% with 4.0 false-positives per slice in detection of ischemic lesions. The overlap measure between ischemic lesion areas obtained by our method and a neuroradiologist was 60.7% on average. The ratio of ischemic lesion area to the whole brain area obtained by our method correlated with that determined by a neuroradiologist with a correlation coefficient of 0.911.
CONCLUSION: Our preliminary results suggest that the proposed method may have feasibility for evaluation of the ischemic lesion area ratio.