Medical Image Segmentation Using Advanced Machine Learning Algorithms (Learning Active Contour Models)

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

10.1109/rteict52294.2021.9573862 ◽

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

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

Journal of Electrical and Computer Engineering ◽

10.1155/2018/3493070 ◽

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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