Medical Decision-Making System of Ultrasound Carotid Artery Intima–Media Thickness Using Neural Networks

Santhiyakumari, N.; Rajendran, P.; Madheswaran, M.

doi:10.1007/s10278-010-9356-8

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…Santhiyakumari et al [24] have employed multilayer backpropagation neural networks for classification and have reported a maximum of 96 % classification accuracy. In their approach, IMT measurement is used to train the neural networks.…”

Section: Resultsmentioning

confidence: 99%

Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

et al. 2013

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In this paper, we present automatic image segmentation and classification technique for carotid artery ultrasound images based on active contour approach. For early detection of the plaque in carotid artery to avoid serious brain strokes, active contour-based techniques have been applied successfully to segment out the carotid artery ultrasound images. Further, ultrasound images might be affected due to rotation, scaling, or translational factors during acquisition process. Keeping in view these facts, image alignment is used as a preprocessing step to align the carotid artery ultrasound images. In our experimental study, we exploit intima-media thickness (IMT) measurement to detect the presence of plaque in the artery. Support vector machine (SVM) classification is employed using these segmented images to distinguish the normal and diseased artery images. IMT measurement is used to form the feature vector. Our proposed approach segments the carotid artery images in an automatic way and further classifies them using SVM. Experimental results show the learning capability of SVM classifier and validate the usefulness of our proposed approach. Further, the proposed approach needs minimum interaction from a user for an early detection of plaque in carotid artery. Regarding the usefulness of the proposed approach in healthcare, it can be effectively used in remote areas as a preliminary clinical step even in the absence of highly skilled radiologists.

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Section: Resultsmentioning

confidence: 99%

Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

et al. 2013

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“…In order to get better and meaningful segmentation need to improve the quality of images. The Histogram Equalization method [14] has been used to improve the appearance of the CCA image by adjusting intensity values without an error in the structure of intimamedia layer.…”

Section: B Histogram Equalizationmentioning

confidence: 99%

“…It is a completely automatic layer recognizing technique for detecting the carotid artery in ultrasound images. There are several segmentation method used for diagnosing ultrasound carotid artery images which are used in both fully automatic and semi-automatic manners [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Implementation of medical image segmentation using Virtex FPGA kit

Hemalatha¹,

Santhiyakumari²,

Suresh

2015

2015 International Conference on Signal Processing and Communication Engineering Systems

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In the decade of technological advancement, the image processing techniques has made it possible to save lives of several beings. The diagnosis of cardiovascular involved in complex procedures whereas the analysis of blood vessels carried out. The image segmentation technique is used to eliminate such complexities in Ultrasound carotid image diagnosis. In this paper the implementation of segmented intima-media interface of the common carotid artery image has been proposed in real time Virtex Field Programmable Gate Array. The intima-media interface has been extracted using threshold segmentation technique. The pre-processing step is used to remove speckles present in the ultrasound carotid image and the morphological techniques can be used in segmentation algorithm with the aid of Aphelion Dev software. The resulting image has been implemented in Virtex which provides minimum hardware resources and low power consumption and is desirable for real time medical applications and analysis.

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“…The edge detection techniques reported in papers [20,31] using region growing and gradient filters were ill suited to detect the local border of the artery due to the stopping criteria problems. The dynamic programming procedure can be used to detect the boundaries of artery [27] considering the approximate location of vessel wall interfaces using optimality principle.…”

Section: Introductionmentioning

confidence: 99%

Detection of the intima and media layer thickness of ultrasound common carotid artery image using efficient active contour segmentation technique

Santhiyakumari¹,

Rajendran²,

Madheswaran³

et al. 2011

Med Biol Eng Comput

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An active contour segmentation technique for extracting the intima-media layer of the common carotid artery (CCA) ultrasound images employing semiautomatic region of interest identification and speckle reduction techniques is presented in this paper. An attempt has been made to test the ultrasound images of the carotid artery of different subjects with this contour segmentation based on improved dynamic programming method. It is found that the preprocessing of ultrasound images of the CCA with region identification and despeckleing followed by active contour segmentation algorithm can be successfully used in evaluating the intima-media thickness (IMT) of the normal and abnormal subjects. It is also estimated that the segmentation used in this paper results an intermethod error of 0.09 mm and a coefficient of variation of 18.9%, for the despeckled images. The magnitudes of the IMT values have been used to explore the rate of prediction of blockage existing in the cerebrovascular and cardiovascular pathologies and also hypertension and atherosclerosis.

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Medical Decision-Making System of Ultrasound Carotid Artery Intima–Media Thickness Using Neural Networks

Cited by 17 publications

References 31 publications

Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

Implementation of medical image segmentation using Virtex FPGA kit

Detection of the intima and media layer thickness of ultrasound common carotid artery image using efficient active contour segmentation technique

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