Pig Ear Extraction Based on an Improved Active Contour Model

2020 ◽  
Vol 36 (5) ◽  
pp. 657-665
Author(s):  
Songnan Chen ◽  
Mengxia Tang ◽  
Jiangming Kan

Abstract.With the integration and scale of pig breeding, the frequency of some diseases is also increasing. To automatically detect porcine reproductive and respiratory syndrome (PRRS) during the pig cultivation process, this article proposes an improved method for pig ear extraction that is based on the active contour model. Firstly, we use the Gaussian scale space filtering and piecewise linear transformation algorithm to highlight the target zones of interest. Secondly, we use a randomly picked ear image point to reconstruct the image region and combine the active contour model to coarse segment the ear image. Finally, by taking advantage of the modified active contour model, the method precisely extracts the pig ear image. The experimental result shows that the proposed method can achieve better segmentation results. The segmentation accuracy of the image of pig contains only one ear can exceed 90%, and the accuracy of the image of pig contains two ears is greater than 85%. Keywords: Active contour model, Ear extraction, Image enhancement, Spline interpolation.

2014 ◽  
Vol 519-520 ◽  
pp. 541-547
Author(s):  
Chao Liu ◽  
Jing Liu ◽  
Lu Lu Zhang

To build a new image segmentation model based on level set theory : Add edge detection operator to edgeless active contour model to detect local information; introduce adaptive coefficient of area item to let the model autonomously adjust and evolve curve position according to image information; adopt weighted average gray value to replace traditional absolute mean value to reduce error and improve segmentation result. Experimental result comparison shows that the new model can detect global information and local information at the same time, adaptively adjust curve evolution direction, and has a fast segmentation speed. Compared to edgeless active contour model, the new model is a more effective image segmentation method as it has greater advantages in image segmentation accuracy and computational complexity.


2021 ◽  
pp. 114811
Author(s):  
Aditi Joshi ◽  
Mohammed Saquib Khan ◽  
Asim Niaz ◽  
Farhan Akram ◽  
Hyun Chul Song ◽  
...  

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas

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.


Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Umer Sadiq Khan ◽  
Xingjun Zhang ◽  
Yuanqi Su

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.


1995 ◽  
Author(s):  
Vit. S. Medovy ◽  
A. V. Ivanov ◽  
Irina A. Ivanova ◽  
Vladimir S. Medovy ◽  
Natalya V. Verdenskaya

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