Hybrid method combining superpixel, random walk and active contour model for fast and accurate liver segmentation

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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Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽

10.3390/electronics10020192 ◽

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.

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Inferior alveolar canal segmentation in cone beam computed tomography images using an adaptive diffusion flow active contour model

2015 14th IAPR International Conference on Machine Vision Applications (MVA) ◽

10.1109/mva.2015.7153132 ◽

2015 ◽

Author(s):

Chadaporn Keatmanee ◽

Stanislav S. Makhanov ◽

Kazunori Kotani ◽

Toshiaki Kondo ◽

Saowapak S. Thongvigitmanee

Keyword(s):

Computed Tomography ◽

Cone Beam Computed Tomography ◽

Active Contour ◽

Active Contour Model ◽

Cone Beam ◽

Diffusion Flow ◽

Computed Tomography Images ◽

Contour Model ◽

Inferior Alveolar Canal

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