An Automatic Segmentation Method Combining an Active Contour Model and a Classification Technique for Detecting Polycomb-group Proteinsin High-Throughput Microscopy Images

The underwater images are disturbed with low signal to noise ratio and edge blur, because there are the light scattering and absorption effects. If the traditional thresholding method is used directly to segment underwater images, it will usually lead to be less effective to process underwater images. An image segmentation method of underwater target based on active contour model was proposed in this paper. Firstly, using Canny edge detection algorithm to detect the edges of the original image to obtain the information of a crude outline, then the algorithm based on C-V active contour model to segment underwater target images was addressed. The images processing results based on threshold segmentation method and C-V model method were compared. Experiments demonstrate the effectiveness of the proposed algorithm for underwater targets images segmentation.

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Bone fracture detection using active contour model with prior shape

10.32920/ryerson.14648202 ◽

2021 ◽

Author(s):

Yun Jia

Keyword(s):

Active Contour ◽

Active Contour Model ◽

Morphological Operations ◽

Segmentation Method ◽

Fracture Detection ◽

Cast Material ◽

X Ray ◽

Detection Tool ◽

Contour Model ◽

Prior Shape

In this research, an image segmentation method based on active contouring model was studied, which incorporates the prior shape into the active contour evolving process as the global constraint. The active contour model is implemented based on the level set method. The prior shape regulates the behavior of the active contour and keeps it from leaking out of the weak edges. The goal of this research is to determine the displacement and alignment between two fractured pieces of a bone which is encased in the cast material by segmenting them out and calculating their axes difference. The noise introduced by the cast material makes this task difficult. Morphological operations of dilation and erosion are deployed in this research as the noise reduction and edge detection tool. Experiment results are obtained successfully by applying this method upon the X-ray images of patients' fractured arm.

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A SEGMENTATION FRAMEWORK FOR PHASE CONTRAST AND FLUORESCENCE MICROSCOPY IMAGES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001414600131 ◽

2014 ◽

Vol 28 (07) ◽

pp. 1460013 ◽

Cited By ~ 1

Author(s):

MITCHEL ALIOSCHA-PEREZ ◽

RONNIE WILLAERT ◽

HICHEM SAHLI

Keyword(s):

Active Contour ◽

Phase Contrast ◽

Active Contour Model ◽

Region Of Interest ◽

Energy Functional ◽

Living Cells ◽

Shape Fitting ◽

Contour Model ◽

Microscopy Images

The noninvasive imaging of unstained living cells is a widely used technique in biotechnology for determining biological and biochemical role of proteins, since it allows studying living specimens without altering them. Usually, fluorescence and contrast (or transmission) images are both used complementarily, as their combination allows possible better outcomes. However, segmentation of contrast images is particularly difficult due to the presence of defocused scans, lighting/shade-off artifacts and cells overlapping. In this work, we investigate the optical properties intervening during the image formation process, and propose different segmentation strategies that can benefit from these properties. The proposed scheme (i) combines the estimated phase and the fluorescence information in order to obtain initial markers for a latter segmentation stage; and (ii) use the shear oriented polar snakes, an active contour model that implicitly involves phase information on its energy functional. The obtained contour can be used as region of interest estimation, as data for a latter shape-fitting process, or as smart markers for a more detailed segmentation process (i.e. watershed). Experimental results provide a comparison of the different segmentation schemes, and confirm the suitability of the proposed strategy and model for cell images segmentation.

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A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI

BioMed Research International ◽

10.1155/2017/8381094 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Chao Ma ◽

Gongning Luo ◽

Kuanquan Wang

Keyword(s):

Left Atrium ◽

Random Forests ◽

Active Contour ◽

Active Contour Model ◽

Automatic Segmentation ◽

Image Resolution ◽

Volumetric Mri ◽

Contour Model ◽

Fully Automatic ◽

Magnetic Resonance Imaging Mri

Segmentation of the left atrium (LA) from cardiac magnetic resonance imaging (MRI) datasets is of great importance for image guided atrial fibrillation ablation, LA fibrosis quantification, and cardiac biophysical modelling. However, automated LA segmentation from cardiac MRI is challenging due to limited image resolution, considerable variability in anatomical structures across subjects, and dynamic motion of the heart. In this work, we propose a combined random forests (RFs) and active contour model (ACM) approach for fully automatic segmentation of the LA from cardiac volumetric MRI. Specifically, we employ the RFs within an autocontext scheme to effectively integrate contextual and appearance information from multisource images together for LA shape inferring. The inferred shape is then incorporated into a volume-scalable ACM for further improving the segmentation accuracy. We validated the proposed method on the cardiac volumetric MRI datasets from the STACOM 2013 and HVSMR 2016 databases and showed that it outperforms other latest automated LA segmentation methods. Validation metrics, average Dice coefficient (DC) and average surface-to-surface distance (S2S), were computed as0.9227±0.0598and1.14±1.205 mm, versus those of 0.6222–0.878 and 1.34–8.72 mm, obtained by other methods, respectively.

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