scholarly journals A Systematic Examination of Microarray Segmentation Algorithms

2019 ◽  
Vol 9 (2S) ◽  
pp. 633-637
Keyword(s):  
Gene Expression ◽  
Image Segmentation ◽  
Microarray Data ◽  
Active Contour ◽  
Active Contour Model ◽  
Watershed Segmentation ◽  
Microarray Image ◽  
Contour Model ◽  
Segmentation Algorithms ◽  
Statistical Approaches

Microarray is a significant tool and influential method which is used to analyze the cDNA expression in living beings. With the help of this technology one can compute gene expression profile in massive and parallel way. Microarray image segmentation offers an input for subsequent analysis of the extracted microarray data. This work addresses on the different approaches used for segmentation of microarray images. Based on the morphology, topology of spots various methods such as circular shaped, region based, active-contour model based segmentation, shape based, supervised learning and watershed segmentation has been taken for this study. This paper explores and compiles various non statistical approaches used in the field of microarray image segmentation. Finally general tendencies in microarray image segmentation are presented.

scholarly journals Efficient Active Contour and K-Means Algorithms in Image Segmentation

2004 ◽  
Vol 12 (2) ◽  
pp. 101-120 ◽  
Author(s):  
J.R. Rommelse ◽  
H.X. Lin ◽  
T.F. Chan
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Clustering Algorithm ◽  
Active Contour Model ◽  
Asynchronous Communication ◽  
Synchronous Communication ◽  
Image Domain ◽  
Parallel Efficiency ◽  
Contour Model ◽  
Segmentation Algorithms

In this paper we discuss a classic clustering algorithm that can be used to segment images and a recently developed active contour image segmentation model. We propose integrating aspects of the classic algorithm to improve the active contour model. For the resulting CVK and B-means segmentation algorithms we examine methods to decrease the size of the image domain. The CVK method has been implemented to run on parallel and distributed computers. By changing the order of updating the pixels, it was possible to replace synchronous communication with asynchronous communication and subsequently the parallel efficiency is improved.

scholarly journals Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions

2021 ◽  
pp. 114811
Author(s):  
Aditi Joshi ◽  
Mohammed Saquib Khan ◽  
Asim Niaz ◽  
Farhan Akram ◽  
Hyun Chul Song ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Function ◽  
Contour Model ◽  
Robust Image

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.

An Improved Image Segmentation Active Contour Model

2014 ◽  
Vol 513-517 ◽  
pp. 3463-3467
Author(s):  
Li Fen Zhou ◽  
Chang Xu Cai
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Local Region ◽  
Intensity Inhomogeneity ◽  
Contour Model ◽  
Global And Local ◽  
Region Information

The Chan-Vese (C-V) active contour model has low computational complexity, initialization and insensitive to noise advantagesand utilizes global region information of images, so it is difficult to handle images with intensity inhomogeneity. The Local binary fitting (LBF) model based on local region information has its certain advantages in mages segmentation of weak boundary or uneven greay.but , the segmentation results are very sensitive to the initial contours, In order to address this problem, this paper proposes a new active contour model with a partial differential equation, which integrates both global and local region information. Experimental results show that it has a distinctive advantage over C-V model for images with intensity inhomogeneity, and it is more efficient than LBF.

scholarly journals Local and Global Active Contour Model for Image Segmentation With Intensity Inhomogeneity

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54224-54240 ◽  
Author(s):  
Qing Cai ◽  
Huiying Liu ◽  
Yiming Qian ◽  
Jing Li ◽  
Xiaojun Duan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Contour Model
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