Image Segmentation for Intensity Inhomogeneity in Presence of High Noise

Ali, Haider; Rada, Lavdie; Badshah, Noor

doi:10.1109/tip.2018.2825101

Cited by 73 publications

(45 citation statements)

References 33 publications

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“…Their results validate that the ensemble of contour-based segmentation is robust to the biased initialization. Haider Ali et al [7] proposed a level-set based model for both global and interactive or selective segmentation tasks. Their model can deal with the intensity inhomogeneity and noise.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Segmentation Technique for Different Medical Image Modalities

Mahmoud

El‐Shafai

Taha

et al. 2020

Menoufia Journal of Electronic Engineering Research

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In this paper, a study of the segmentation of medical images is presented. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. Firstly, the local spatial information of the image is enhanced with morphological operations to ensure noise-immunity and image detailprotection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, fuzzy c-means (FCM) clustering is used with modification of membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed technique is very simple to implement and significantly fast, since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy images because of its ability to improve membership partition matrix efficiently. Experimental results performed on different medical image modalities illustrate that the proposed technique can achieve good results, as well as short time and efficient image segmentation.

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Section: Related Workmentioning

confidence: 99%

An Efficient Segmentation Technique for Different Medical Image Modalities

Mahmoud

El‐Shafai

Taha

et al. 2020

Menoufia Journal of Electronic Engineering Research

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“…A kernel function was integrated into the Chan-Vese model for better effectiveness in inhomogeneous images [34]. A combined data fidelity term was integrated into the energy function for efficiently handling images with additive and multiplicative noise [35].…”

Section: Convergence Of Contoursmentioning

confidence: 99%

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

et al. 2019

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Reliable detection of the media-adventitia border (MAB) and the lumen-intima border (LIB) in intravascular ultrasound (IVUS) images remains a challenging task that is of high clinical interest. In this paper, we propose a superpixel-wise fuzzy clustering technique modified by edges, followed by level set evolution (SFCME-LSE), for automatic border extraction in 40 MHz IVUS images. The contributions are three-fold. First, the usage of superpixels suppresses the influence of speckle noise in ultrasound images on the clustering results. Second, we propose a region of interest (ROI) assignment scheme to prevent the segmentation from being distracted by pathological structures and artifacts. Finally, the contour is converged towards the target boundary through LSE with an appropriately improved edge indicator. Quantitative evaluations on two IVUS datasets by the Jaccard measure (JM), the percentage of area difference (PAD), and the Hausdorff distance (HD) demonstrate the effectiveness of the proposed SFCME-LSE method. SFCME-LSE achieves the minimal HD of 1.20 ± 0.66 mm and 1.18 ± 0.70 mm for the MAB and LIB, respectively, among several state-of-the-art methods on a publicly available dataset.

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“…Image segmentation plays a fundamental and important role in image processing and computer vision, since it makes images easier to analyze [1]- [3]. Large numbers of methods, such as, clustering [4]- [6], graph-cut [7]- [9], machinelearning [10]- [12] and active contour [13]- [17], have been used to solve the problem. However, none of them are universal and it is still a challenge to segment the images with noise, complex background or inhomogeneous intensity accurately.…”

Section: Introductionmentioning

confidence: 99%

“…Active contour models have been widely used because of their desirable advantages which are easy to formulate under the energy minimization framework, incorporate the prior knowledge and provide smooth contours as segmentation results [13]- [17]. According to the representation of contour curve, active contour models can be classified into…”

Section: Introductionmentioning

confidence: 99%

An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

Liu

Xing

2020

IEEE Access

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In image processing and computer vision, image segmentation plays a fundamental role since it can make images easier to analyze. However, noise is easily introduced into images and brings great challenges to image segmentation. This paper focuses on the segmentation problem of noisy images and proposes an efficient variational level set model based on adaptive local fitted image to handle it. By utilizing normalized local entropy and local means, an adaptive local fitted image is proposed and introduced into the data term to enhance the robustness of the model against noise. Then a penalty term is proposed to reduce the deviation of the adaptive local fitted image from the original image by punishing the difference between them, so as to guarantee the accuracy of segmentation results. Later, the total variational regularization term is introduced into the model, so the level set function can be smoothed and the effect of noise on the active contour can be further reduced. The energy functional of the whole model is convex rigorously, which can reach the minimum and should have good properties in noisy image segmentation. Numerous experiments on synthetic, natural, synthetic aperture radar and oil spill images demonstrate that the proposed model is strongly robust to different types and levels of noise, which indicates its good performance in noisy image segmentation. INDEX TERMS Adaptive local fitted image, convex model, image segmentation, noisy image, variational level set.

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Image Segmentation for Intensity Inhomogeneity in Presence of High Noise

Cited by 73 publications

References 33 publications

An Efficient Segmentation Technique for Different Medical Image Modalities

An Efficient Segmentation Technique for Different Medical Image Modalities

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

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