Adaptive local-fitting-based active contour model for medical image segmentation

Ma, Di; Liao, Qingmin; Chen, Ziqin; Liao, Ran; Ma, Hui

doi:10.1016/j.image.2019.05.006

Cited by 36 publications

(33 citation statements)

References 27 publications

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“…The segmentation results of images illustrated in Figure 25(a)–(c) are shown in Figures 27–29 and the segmentation results of images indicated in Figures 26(a‐c) are shown in Figures 30–32, respectively. In Figures 27(a)–(f) to 32(a)–(f), the corresponding segmentation results of the RSF [25], LIC [28], LSACM [29], ALF [22], EWF [31] and LSW‐AC methods are reverberated. For similar conditions, in all methods, the window size is the same and equal to 40.…”

Section: Resultsmentioning

confidence: 99%

“…This model is more successful than the LIC [28] and LCV [30] models in segmenting heterogeneous images, but the results strongly depend on the window size; and it is not as good as the proposed method for extracting smooth edges. The ALF model [22] performance is as same as the RSF model [25]. It is sensitive to initial contour and adjusting parameters, and extract meaningless areas.…”

Section: Resultsmentioning

confidence: 99%

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A level‐set method for inhomogeneous image segmentation with application to breast thermography images

Koshki

Ahmadzadeh

Zekri

et al. 2020

IET Image Processing

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Various level‐set methods have been suggested for segmenting images with intensity inhomogeneity as local region‐based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region‐based active contour model called local self‐weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well‐known local level‐set methods which show the perfect capability of the model. The segmentation results were evaluated using the F‐score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F‐score, precision and recall criteria on the segmentation of breast thermography images, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A level‐set method for inhomogeneous image segmentation with application to breast thermography images

Koshki

Ahmadzadeh

Zekri

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…The traditional methods assume the intensities in the local region are constant, however, the LBF method finds an optimal solution by fitting the original image using an adaptive technique. 46 The ALF method improves on the Local Binary Fitting method. The energy function of the ALF is:…”

Section: Adaptive Local-fitting (Alf) Methodsmentioning

confidence: 99%

COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

Akbari

Hassen

Al‐Maadeed

et al. 2020

Preprint

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Abstract Pneumonia is a lung infection threaten that threats all age groups. In this paper, using CT scans images, we used active contour models to evaluate and determine pneumonia infection caused by the Coronavirus disease (COVID-19). A background of active contour models (ACM) including contour representation and object boundary description methods is presented. The focus of this paper is on the conducted works based on the external forces. These methods include edge-based and region-based methods. Furthermore, the explanations of these methods, as well as the advantages and disadvantages of each method are presented. Finally, a comparison between the performances of the conducted works has been done based on a database of Lung CT Scan Images. The present review helps readers identify research starting points in active contour models on COVID19 research, which is a high priority topic to guide researchers and practitioners. In addition, when there are not enough images to use machine learning techniques, such as deep learning methods, the experimental results indicate that active contour methods obtain promising results.

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“…Region-based methods are further classified into two categories: local region-based (LR) [16]- [20], [39]- [40] and global region-based (GR) methods [14], [15], [25]. The most classic global region-based method, which is based on the Mumford-Shah (M-S) model [31], was proposed by Chan and Vese by transforming the minimization problem into a meancurvature-flow problem [25].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

et al. 2020

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Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy. INDEX TERMS Active contours, bias field, image segmentation, intensity inhomogeneity, level set.

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Adaptive local-fitting-based active contour model for medical image segmentation

Cited by 36 publications

References 27 publications

A level‐set method for inhomogeneous image segmentation with application to breast thermography images

A level‐set method for inhomogeneous image segmentation with application to breast thermography images

COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

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