Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model

Liu, Tingting; Xu, Haiyong; Jin, Wei; Liu, Zhen; Zhao, Yiming; Tian, Wenzhe

doi:10.1155/2014/890725

Cited by 48 publications

(25 citation statements)

References 31 publications

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“…These methods rely on morphological operations performed on the images. For example: Local binary fitting(LBF), Local intensity fitting(LIF) [25].Local Gaussian Intensity fitting(LGIF), Local region based active contour method(LRBAC) [26].Local Binary Fitting model [18] by embedding the local image information. It also works well on images with intensity in homogeneity.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Fast Segmentation Methods for Medical Images

Upadhyay¹,

Kashyap²

2016

IJCA

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Image segmentation is about splitting the whole image into segments. In case of image analysis, image processing one of the crucial steps is segmentation of the image. Segmentation of image concern about dividing the entire image in sub parts that may be similar or dissimilar with respect to features. Output of image segmentation has consequence on analysis of image, further processing of images. Analysis of image comprises depiction of object and object representation, measurement of features. Therefore characterization, area of interest"s visualization in the image, description have crucial job in the segmentation of the image. This survey explains some methods of image segmentation. Segmenting an image into meaningful parts is a fundamental operation in image processing. Image segmentation is the process of partitioning a digital image into multiple segments. In this paper, various image segmentation methods are explained like edge detection, region based segmentation, neural network techniques ,energy based and hybrid methods, etc. In this paper review of image segmentation is explained by using different techniques. The efficiency of the segmentation process improved with the help of several algorithms, namely, active contour, level set, fuzzy clustering and K-means clustering. This paper analyses the performance of algorithms for image segmentation in detail. Intensity and texture based image segmentation is the two levels of the level set method. The combination of both intensity and texture based image segmentation provides better results than the traditional methods. The detailed survey of segmentation techniques provides the requirement of the suitable enhancement method that supports both intensity and texture based segmentation for better results.

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Section: Hybrid Methodsmentioning

confidence: 99%

Fast Segmentation Methods for Medical Images

Upadhyay¹,

Kashyap²

2016

IJCA

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“…The research presented in [13,14] is focused on an application of active contour models and segmentation algorithms to medical images processing.…”

Section: Related Workmentioning

confidence: 99%

Method of Medical Images Similarity Estimation Based on Feature Analysis

Hu¹,

Dychka²,

Sulema³

et al. 2018

IJISA

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Abstract-The paper presents the method of medical images similarity estimation based on feature extraction and analysis. The proposed method has been developed for and tested on rat brain histological images, however, it can be applied for other types of medical images, since the general approach is based on consideration of the shape of core components present in a given template image. The proposed method can be used in image analysis tools in a wide range of image-based medical investigations, in particular, in the brain researches.The theoretical background of the proposed method is presented in the paper. The expert evaluation approach used for assessment of the proposed method effectiveness is explained and illustrated by examples. The method of medical images similarity estimation based on feature analysis consists of several stages: colour model conversion, image normalization, anti-noise filtering, contours search, conversion, and feature analysis. The results of the proposed method algorithmic realization are demonstrated and discussed.

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“…However, it continues to be an active research field because there is no single framework that fits all applications and requirements. The most widely used model for identifying and tracking of the vessels from complex 3D images is the active contour model because of its clear curve for the desired object [2]. The model evolves the contour under the impact of various forces.…”

Section: Introductionmentioning

confidence: 99%

Automatic and Effective Delineation of Coronary Arteries from CTA Data Using Two-Way Active Contour Model

Zai

Ansari

Moon

2017

IEICE Trans. Inf. & Syst.

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SUMMARYPrecise estimation of coronary arteries from computed tomography angiography (CTA) data is one of the challenging problems. This study focuses on automatic delineation of coronary arteries from 3D CTA data that may assess the clinicians in identifying the coronary pathologies. In this work, we present a technique that effectively segments the complete coronary arterial tree under the guidance of initial vesselness response without relying on heavily manual operations. The proposed method isolates the coronary arteries with accuracy by using localized statistical energy model in two directions provided with an automated seed which ensures an optimal segmentation of the coronaries. The detection of seed is carried out by analyzing the shape information of the coronary arteries in three successive cross-sections. To demonstrate the efficiency of the proposed algorithm, the obtained results are compared with the reference data provided by Rotterdam framework for lumen segmentation and the level-set active contour based method proposed by Lankton et al. Results reveal that the proposed method performs better in terms of leakages and accuracy in completeness of the coronary arterial tree.

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Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model

Cited by 48 publications

References 31 publications

Fast Segmentation Methods for Medical Images

Fast Segmentation Methods for Medical Images

Method of Medical Images Similarity Estimation Based on Feature Analysis

Automatic and Effective Delineation of Coronary Arteries from CTA Data Using Two-Way Active Contour Model

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