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

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

doi:10.1007/s11517-011-0800-9

Cited by 23 publications

(10 citation statements)

References 43 publications

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“…2 The therapy evaluation and clinical data analysis are important to the cerebrovascular and cardiovascular pathologies diagnosis; and have attracted significant attention amongst the health and science community. 3,4 Ultrasound (US) has been employed as a routine examination for inexpensive non-invasive clinical diagnosis of atherosclerosis (the hardening of the arteries). 5,6 Furthermore, three-dimensional ultrasound (3D US) imaging provides reproducible volumetric data, yielding added sensitivity to changes in plaque.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of the common carotid artery with active shape models from 3D ultrasound images

Yang

Jin

et al. 2012

SPIE Proceedings

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Carotid atherosclerosis is a major cause of stroke, a leading cause of death and disability. In this paper, we develop and evaluate a new segmentation method for outlining both lumen and adventitia (inner and outer walls) of common carotid artery (CCA) from three-dimensional ultrasound (3D US) images for carotid atherosclerosis diagnosis and evaluation. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80mg atorvastain and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. We investigate the use of Active Shape Models (ASMs) to segment CCA inner and outer walls after statin therapy. The proposed method was evaluated with respect to expert manually outlined boundaries as a surrogate for ground truth. For the lumen and adventitia segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 93.6% ± 2.6%, 91.8% ± 3.5%, mean absolute distances (MAD) of 0.28 ± 0.17mm and 0.34 ± 0.19mm, maximum absolute distances (MAXD) of 0.87 ± 0.37mm and 0.74 ± 0.49mm. The proposed algorithm took 4.4 ± 0.6min to segment a single 3D US images, compared to 11.7 ± 1.2min for manual segmentation. Therefore, the method would promote the translation of carotid 3D US to clinical care for the fast, safety and economical monitoring of the atherosclerotic disease progression and regression during therapy.

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Section: Introductionmentioning

confidence: 99%

Segmentation of the common carotid artery with active shape models from 3D ultrasound images

Yang

Jin

et al. 2012

SPIE Proceedings

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“…After speckle reduction, the contours have been extracted using energy minimization process [33,34]. The extracted contours are shown in Fig.…”

Section: Preprocessing and Contour Extraction For Carotid Artery Of Umentioning

confidence: 99%

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

Santhiyakumari¹,

Rajendran²,

Madheswaran³

2010

J Digit Imaging

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The objective of this work is to develop and implement a medical decision-making system for an automated diagnosis and classification of ultrasound carotid artery images. The proposed method categorizes the subjects into normal, cerebrovascular, and cardiovascular diseases. Two contours are extracted for each and every preprocessed ultrasound carotid artery image. Two types of contour extraction techniques and multilayer back propagation network (MBPN) system have been developed for classifying carotid artery categories. The results obtained show that MBPN system provides higher classification efficiency, with minimum training and testing time. The outputs of decision support system are validated with medical expert to measure the actual efficiency. MBPN system with contour extraction algorithms and preprocessing scheme helps in developing medical decision-making system for ultrasound carotid artery images. It can be used as secondary observer in clinical decision making.

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

Cited by 23 publications

References 43 publications

Segmentation of the common carotid artery with active shape models from 3D ultrasound images

Segmentation of the common carotid artery with active shape models from 3D ultrasound images

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

Implementation of medical image segmentation using Virtex FPGA kit

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