Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation

2013 ◽  
Vol 3 (4) ◽  
pp. 47-59 ◽  
Author(s):  
Kai Xiao ◽  
Jianli Li ◽  
Shuangjiu Xiao ◽  
Haibing Guan ◽  
Fang Fang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Clustering Algorithms ◽  
Low Frequency ◽  
Image Data ◽  
Bilateral Filter ◽  
Medical Image Segmentation ◽  
Variation Method ◽  
Frequency Noise

Although fuzzy c-means (FCM) algorithm and some of its variants have been extensively widely used in unsupervised medical image segmentation applications in recent years, they more or less suffer from either noise sensitivity or loss of details, which always is a key point to medical image processing. This paper presents a novel FCM variation method that is suitable for medical image segmentation. The proposed method, typically by incorporating multi-resolution bilateral filter which is combined with wavelet thresholding, provides the following advantages: (1) it is less sensitive to both high- and low-frequency noise and removes spurious blobs and noisy spots, (2) it yields more homogeneous clustering regions, and (3) it preserves detail, thus significantly improving clustering performance. By the use of synthetic and multiple-feature magnetic resonance (MR) image data, the experimental results and quantitative analyses suggest that, compared to other fuzzy clustering algorithms, the proposed method further enhances the robustness to noisy images and capacity of detail preservation.

scholarly journals Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks

2021 ◽  
Vol 12 (1) ◽  
pp. 29-39
Author(s):  
Lars J. Isaksson ◽  
Paul Summers ◽  
Sara Raimondi ◽  
Sara Gandini ◽  
Abhir Bhalerao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Data Augmentation ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Dice Similarity Coefficient ◽  
Surface Distance ◽  
Experienced Radiologist ◽  
Generalization Problem

Abstract Researchers address the generalization problem of deep image processing networks mainly through extensive use of data augmentation techniques such as random flips, rotations, and deformations. A data augmentation technique called mixup, which constructs virtual training samples from convex combinations of inputs, was recently proposed for deep classification networks. The algorithm contributed to increased performance on classification in a variety of datasets, but so far has not been evaluated for image segmentation tasks. In this paper, we tested whether the mixup algorithm can improve the generalization performance of deep segmentation networks for medical image data. We trained a standard U-net architecture to segment the prostate in 100 T2-weighted 3D magnetic resonance images from prostate cancer patients, and compared the results with and without mixup in terms of Dice similarity coefficient and mean surface distance from a reference segmentation made by an experienced radiologist. Our results suggest that mixup offers a statistically significant boost in performance compared to non-mixup training, leading to up to 1.9% increase in Dice and a 10.9% decrease in surface distance. The mixup algorithm may thus offer an important aid for medical image segmentation applications, which are typically limited by severe data scarcity.

scholarly journals An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ningning Zhou ◽  
Tingting Yang ◽  
Shaobai Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Fuzzy Information ◽  
Spatial Features ◽  
Image Segmentation Algorithm ◽  
Truth Degree

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

Methods and Applications for Segmenting 3D Medical Image Data

2011 ◽  
pp. 303-320
Author(s):  
Hong Shen
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
General Problem ◽  
Real World ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Segmentation ◽  
Knowledge Based ◽  
Typical Sample ◽  
Medical Image Data

In this chapter, we will give an intuitive introduction to the general problem of 3D medical image segmentation. We will give an overview of the popular and relevant methods that may be applicable, with a discussion about their advantages and limits. Specifically, we will discuss the issue of incorporating prior knowledge into the segmentation of anatomic structures and describe in detail the concept and issues of knowledge-based segmentation. Typical sample applications will accompany the discussions throughout this chapter. We hope this will help an application developer to improve insights in the understanding and application of various computer vision approaches to solve real-world problems of medical image segmentation.

scholarly journals A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Li Ma ◽  
Yang Li ◽  
Suohai Fan ◽  
Runzhu Fan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Medical Image Segmentation ◽  
Superior Result ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Fcm Clustering ◽  
Swarm Algorithm

Image segmentation plays an important role in medical image processing. Fuzzyc-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

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