J Med Syst - Inter-greedy technique for fusion of different segmentation strategies leading to high-performance carotid IMT measurement in ultrasound images.

Tópicos

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Resumo

User-based estimation of intima-media thickness (IMT) of carotid arteries leads to subjectivity in its decision support systems, while being used as a cardiovascular risk marker. During automated computer-based decision support, we had developed segmentation strategies that follow three main courses of our contributions: (a) signal processing approach combined with snakes and fuzzy K-means (CULEXsa), (b) integrated approach based on seed and line detection followed by probability based connectivity and classification (CALEXsa), and (c) morphological approach with watershed transform and fitting (WS). These grayscale segmentation algorithms yielding carotid wall boundaries has certain bias along with their own merits. We recently developed a fusion technique that was helpful in removing bias which combines two carotid wall boundaries using ground truth as an ideal marker. Here we have extended this fusion concept by taking merits of these multiple boundaries, so called, Inter-Greedy (IG) approach. Further we estimate IMT from these fused boundaries from multiple sources. Starting from the technique with the overall least system error (the snake-based one), we iteratively swapped the vertices of the profiles until we minimized its overall distance with respect to ground truth. The fusion boundary was the Inter-Greedy boundary. We used the polyline distance metric for performance evaluation and error minimization. We ran the segmentation protocol over the database of 200 carotid longitudinal B-mode ultrasound images and compared the performance of all the four techniques (CALEXia, CULEXsa, WS, IG). The mean error of Inter-Greedy technique yielded 0.32?0.44 pixel (20.0?27.5 ?m) for the LI boundary (a 33.3%?5.6% improvement over initial best performing technique) and 0.21?0.34 pixel (13.1?21.3 ?m) for MA boundary (a 32.3%?6.7% improvement). IMT measurement error for Greedy method was 0.74?0.75 pixel (46.3?46.9 ?m), a 43.5%?2.4% improvement.

Resumo Limpo

userbas estim intimamedia thick imt carotid arteri lead subject decis support system use cardiovascular risk marker autom computerbas decis support develop segment strategi follow three main cours contribut signal process approach combin snake fuzzi kmean culexsa b integr approach base seed line detect follow probabl base connect classif calexsa c morpholog approach watersh transform fit ws grayscal segment algorithm yield carotid wall boundari certain bias along merit recent develop fusion techniqu help remov bias combin two carotid wall boundari use ground truth ideal marker extend fusion concept take merit multipl boundari call intergreedi ig approach estim imt fuse boundari multipl sourc start techniqu overal least system error snakebas one iter swap vertic profil minim overal distanc respect ground truth fusion boundari intergreedi boundari use polylin distanc metric perform evalu error minim ran segment protocol databas carotid longitudin bmode ultrasound imag compar perform four techniqu calexia culexsa ws ig mean error intergreedi techniqu yield pixel m li boundari improv initi best perform techniqu pixel m ma boundari improv imt measur error greedi method pixel m improv

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