Composite fuzzy-wavelet-based active contour for medical image segmentation

2020 ◽  
Vol 37 (9) ◽  
pp. 3525-3541
Author(s):  
Hiren Mewada ◽  
Amit V. Patel ◽  
Jitendra Chaudhari ◽  
Keyur Mahant ◽  
Alpesh Vala
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Legendre Polynomial ◽  
Active Contour ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Low Resolution ◽  
Content Type ◽  
Proposed Model

Purpose In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images. Design/methodology/approach The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach. Findings The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%. Originality/value The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

Proposal of Fast Implicit Level Set Scheme for Medical Image Segmentation Using the Chan and Vese Model

2011 ◽  
Vol 103 ◽  
pp. 695-699 ◽  
Author(s):  
Hui Min Lu ◽  
Serikawa Seiichi ◽  
Yu Jie Li ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Social Life ◽  
Medical Image ◽  
High Speed ◽  
Active Contours ◽  
Operator Splitting ◽  
Medical Image Segmentation ◽  
Proposed Model ◽  
The Social

People living in the information age, are more and more attention to their lives. It is also said, social life is more important in present and future. The social life contains three fields. In this paper, we propose a new model for active contours to detect objects in a given medical image, in order to facilitate people to have medical treatment. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, our model uses the level set which are corresponding to ordinary differential equation (ODE). Our model has more improved characteristics than traditional models, such as: less sensibility of noise; unnecessary of re-initialization and high speed by the simplified ordinary differential function. Finally, we validate the proposed model by numerical synthetic and real images. The experimental results demonstrate that our model is at least two times more efficient than the widely used methods.

scholarly journals Brain Image Segmentation Based on Fuzzy Clustering

2018 ◽  
Vol 28 (3) ◽  
pp. 220
Author(s):  
Shatha J. Mohammed
Keyword(s):  
Image Segmentation ◽  
Performance Evaluation ◽  
Brain Tumour ◽  
Level Set ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Brain Image ◽  
The Brain

The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated. This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

scholarly journals Active Contour Driven by Local Region Statistics and Maximum A Posteriori Probability for Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xiaoliang Jiang ◽  
Bailin Li ◽  
Qiang Wang ◽  
Jiajia Liu
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour ◽  
Medical Images ◽  
Bias Field ◽  
Medical Image Segmentation ◽  
Local Region ◽  
Posteriori Probability ◽  
Field Estimation ◽  
Level Set Formulation

This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD) with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances. Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are simultaneously achieved by minimizing the level set formulation. Experimental results demonstrate desirable performance of the proposed method for different medical images with weak boundaries and noise.

scholarly journals Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shaolei Lang ◽  
Yinxia Xu ◽  
Liang Li ◽  
Bin Wang ◽  
Yang Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Thyroid Cancer ◽  
Deep Learning ◽  
Active Contour ◽  
Medical Image ◽  
Thyroid Nodules ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Type Iv ◽  
Contour Level

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor’s diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

ACTIVE CONTOUR BASED ON 3D STRUCTURE TENSOR APPLIED IN MEDICAL IMAGE SEGMENTATION

2013 ◽  
Vol 06 (04) ◽  
pp. 1350021
Author(s):  
PING ZHANG ◽  
ZHAOHUA CUI ◽  
HALE XUE ◽  
DEXUAN ZOU ◽  
LI GUO
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Function ◽  
Active Contour ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Structure Tensor ◽  
Image Feature ◽  
Intensity Inhomogeneity ◽  
Initial Contour

The paper presents an improved tensor-based active contour model in a variational level set formulation for medical image segmentation. In it, a new energy function is defined with a local intensity fitting term in intensity inhomogeneity of the image, and with a global intensity fitting term in intensity homogeneity domain. Weighting factor is chosen to balance these two intensity fitting terms, which can be calculated automatically by local entropy. The level set regularization term is to replace contour curve to find the minimum of the energy function. Particularly, structure tensor is applied to describe the image, which overcomes the disadvantage of image feature without structure information. The experimental results show that our proposed method can segment image efficiently whether it presents intensity inhomogeneity or not and wherever the initial contour is. Moreover, compared with the Chan–Vese model and local binary fitting model, our proposed model not only handles better intensity inhomogeneity, but also is less sensitive to the location of initial contour.

scholarly journals New Region-Scalable Discriminant and Fitting Energy Functional for Driving Geometric Active Contours in Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xuchu Wang ◽  
Yanmin Niu ◽  
Liwen Tan ◽  
Shao-Xiang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Medical Image Segmentation ◽  
Intensity Inhomogeneity ◽  
Boundary Problems

We propose a novel region-based geometric active contour model that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness.

scholarly journals A New Kernel-Based Fuzzy Level Set Method for Automated Segmentation of Medical Images in the Presence of Intensity Inhomogeneity

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Maryam Rastgarpour ◽  
Jamshid Shanbehzadeh
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Intensity Inhomogeneity ◽  
Integrative Approach ◽  
Robust Segmentation ◽  
Level Set Evolution

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based FuzzyC-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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