Comput Methods Programs Biomed - Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering.

Tópicos

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Resumo

Disease diagnosis based on ultrasound imaging is popular because of its non-invasive nature. However, ultrasound imaging system produces low quality images due to the presence of spackle noise and wave interferences. This shortcoming requires a considerable effort from experts to diagnose a disease from the carotid artery ultrasound images. Image segmentation is one of the techniques, which can help efficiently in diagnosing a disease from the carotid artery ultrasound images. Most of the pixels in an image are highly correlated. Considering the spatial information of surrounding pixels in the process of image segmentation may further improve the results. When data is highly correlated, one pixel may belong to more than one clusters with different degree of membership. In this paper, we present an image segmentation technique namely improved spatial fuzzy c-means and an ensemble clustering approach for carotid artery ultrasound images to identify the presence of plaque. Spatial, wavelets and gray level co-occurrence matrix (GLCM) features are extracted from carotid artery ultrasound images. Redundant and less important features are removed from the features set using genetic search process. Finally, segmentation process is performed on optimal or reduced features. Ensemble clustering with reduced feature set outperforms with respect to segmentation time as well as clustering accuracy. Intima-media thickness (IMT) is measured from the images segmented by the proposed approach. Based on IMT measured values, Multi-Layer Back-Propagation Neural Networks (MLBPNN) is used to classify the images into normal or abnormal. Experimental results show the learning capability of MLBPNN classifier and validate the effectiveness of our proposed technique. The proposed approach of segmentation and classification of carotid artery ultrasound images seems to be very useful for detection of plaque in carotid artery.

Resumo Limpo

diseas diagnosi base ultrasound imag popular noninvas natur howev ultrasound imag system produc low qualiti imag due presenc spackl nois wave interfer shortcom requir consider effort expert diagnos diseas carotid arteri ultrasound imag imag segment one techniqu can help effici diagnos diseas carotid arteri ultrasound imag pixel imag high correl consid spatial inform surround pixel process imag segment may improv result data high correl one pixel may belong one cluster differ degre membership paper present imag segment techniqu name improv spatial fuzzi cmean ensembl cluster approach carotid arteri ultrasound imag identifi presenc plaqu spatial wavelet gray level cooccurr matrix glcm featur extract carotid arteri ultrasound imag redund less import featur remov featur set use genet search process final segment process perform optim reduc featur ensembl cluster reduc featur set outperform respect segment time well cluster accuraci intimamedia thick imt measur imag segment propos approach base imt measur valu multilay backpropag neural network mlbpnn use classifi imag normal abnorm experiment result show learn capabl mlbpnn classifi valid effect propos techniqu propos approach segment classif carotid arteri ultrasound imag seem use detect plaqu carotid arteri

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