scholarly journals Segmentation and Tracking of Lymphocytes Based on Modified Active Contour Models in Phase Contrast Microscopy Images

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yali Huang ◽  
Zhiwen Liu
Keyword(s):  
External Force ◽  
Active Contour ◽  
Phase Contrast ◽  
Initial Contour ◽  
Active Contour Models ◽  
Low Contrast ◽  
Microscopic Images ◽  
Initial Location ◽  
Contour Models ◽  
Segmentation And Tracking

The paper proposes an improved active contour model for segmenting and tracking accurate boundaries of the single lymphocyte in phase-contrast microscopic images. Active contour models have been widely used in object segmentation and tracking. However, current external-force-inspired methods are weak at handling low-contrast edges and suffer from initialization sensitivity. In order to segment low-contrast boundaries, we combine the region information of the object, extracted by morphology gray-scale reconstruction, and the edge information, extracted by the Laplacian of Gaussian filter, to obtain an improved feature map to compute the external force field for the evolution of active contours. To alleviate initial location sensitivity, we set the initial contour close to the real boundaries by performing morphological image processing. The proposed method was tested on live lymphocyte images acquired through the phase-contrast microscope from the blood samples of mice, and comparative experimental results showed the advantages of the proposed method in terms of the accuracy and the speed. Tracking experiments showed that the proposed method can accurately segment and track lymphocyte boundaries in microscopic images over time even in the presence of low-contrast edges, which will provide a good prerequisite for the quantitative analysis of lymphocyte morphology and motility.

Heuristic Initialization for Active Contour Models in CT/MRI Image Processing

2015 ◽  
Vol 15 (03) ◽  
pp. 1550010
Author(s):  
Hao Liu ◽  
Hongbo Qian ◽  
Ning Dai ◽  
Jianning Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Heuristic Method ◽  
Initial Contour ◽  
Active Contour Models ◽  
Contour Model ◽  
Segmentation Approach ◽  
Edge Points ◽  
Contour Models ◽  
Level Set Evolution

It is an important segmentation approach of CT/MRI images to automatically extract contours in every slice using active contour models. The key point of the segmentation approach is to automatically construct initial contours for active contour models because any active contour model is sensitive to its initial contour. This paper presents an algorithm to construct such initial contours using a heuristic method. Assume that the contour in previous slice (previous contour) is accurate. The contour in the current slice (current contour) is constructed according to the previous contour using the way: Recognition and link of edge points of tissues according to the previous contour. The contour linking edge points is used as the initial contour of the distance regularized level set evolution (DRLSE) method and then an accurate contour can be extracted in the current slice.

Automatic MRI Brain Tumor Segmentation System Based on Localizing Active Contour Models

2011 ◽  
Vol 219-220 ◽  
pp. 1342-1346 ◽  
Author(s):  
Ying Wang ◽  
Zhi Xian Lin ◽  
Jian Guo Cao ◽  
Mao Qing Li
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Initial Contour ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models

In this paper, an automatic segmentation system was developed for MRI brain tumor. Local region-based active contour models were suitable for heterogeneous features of brain MRI image. But the models are sensitive to initial contour, which generally requires manual setting. An automatic MRI brain tumor segmentation system were developed based on localized contour models, which can identify tumor-dominant slice, set initial contour automatically and segment tumor’s contours from all MRI slices autonomously. K-means clustering and grayscale analysis were combined to identify tumor-dominant slice. Multi-threshold algorithm with the aid of erosion and dilation operators was adopted to obtain an initial contour for the tumor-dominant slice. The segmentation contour from the local active contour models was applied as initial contours of two-side neighboring slices. MRI brain tumor data were applied to validate the automatic segmentation system.

scholarly journals Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Umer Sadiq Khan ◽  
Xingjun Zhang ◽  
Yuanqi Su
Keyword(s):  
Object Detection ◽  
Fourier Transformation ◽  
Active Contour ◽  
Active Contour Model ◽  
Salient Object Detection ◽  
Fast Fourier Transformation ◽  
Salient Object ◽  
Active Contour Models ◽  
Contour Model ◽  
Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

T1w MRI Brain Tumor Image Segmentation using Active Contour Models and Convolution Neural Network Fusion

2021 ◽  
Author(s):  
Vamisdhar Entireddy ◽  
Babu K Rajesh ◽  
R Sampathkumar ◽  
Jyothirmai Gandeti ◽  
Syed Shameem ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Active Contour ◽  
Convolution Neural Network ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

scholarly journals Robust Evolution Method of Active Contour Models and Application in Segmentation of Image Sequence

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Guoqi Liu ◽  
Haifeng Li
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Image Sequence ◽  
Nuclear Norm ◽  
Shape Prior ◽  
Object Boundary ◽  
Active Contour Models ◽  
Ideal Object ◽  
Sequence Segmentation ◽  
Contour Models

Active contour models are widely used in image segmentation. In order to obtain ideal object boundary, researchers utilize various information to define new models for image segmentation. However, the models could not meet all scenes of image. In this paper, we propose a block evolution method to improve the robustness of contour evolution. A block matrix is consisted of contours of former iterations and contours of shape prior, and a nuclear norm of the matrix is a measure of the similarity of these shapes. The constraint of the nuclear norm minimization is imposed on the evolution of active contour models, which could avoid large deformation of the adjacent curves and keep the shape conformability of contour in the evolution. The shape prior can be integrated into the block evolution method, which is effective in dealing with missing features of images and noise. The proposed method can be applied to image sequence segmentation. Experiments demonstrate that the proposed method improves the robust performance of active contour models and can increase the flexibility of applications in image sequence segmentation.

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