改进的Fuzzy C-Means聚类与空间邻域融合：医疗图像分割算法

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"Matlab基于K均值聚类与区域合并的彩色图像分割算法是一篇针对医学图像处理领域的研究论文。该方法结合了K均值聚类（K-Means）和模糊聚类（Fuzzy C-Means, FCM）技术，旨在提高医疗图像分割的效率和准确性。传统的K均值算法在处理颜色复杂、噪声较多的医学图像时可能存在局限性，因此，研究者提出了一种融合空间信息的改进版本，即空间邻域信息下的Fuzzy Clustering Technique。文章首先概述了图像分割在医学成像中的关键作用，强调了众多图像分割方法中，基于聚类的概念，特别是Fuzzy C-Means算法的广泛应用。然后，作者重点介绍了他们开发的高效变体，这种变体考虑了图像像素间的空间关系，以增强分割结果的稳定性，并减少噪声的影响。这种方法可能通过计算每个像素点的邻域相似度，动态调整聚类中心和模糊度参数，使得分割结果更加贴近真实物体边界。研究团队由四位来自印度的教育和科研机构的专家组成，他们分别是助理教授、副教授和教授，他们在各自的领域有着丰富的经验。他们的研究成果不仅在理论上具有创新性，而且在实际应用中具有很高的实用价值，尤其是在医疗图像分析和计算机辅助诊断（CAD）系统中。论文的摘要部分着重提到了，通过将空间信息融入到Fuzzy C-Means算法中，他们成功地实现了对医学图像的有效分割，这对于诸如病灶检测、组织分类和手术导航等应用场景具有显著的提升。这种算法的优势在于能够在保持简单性和计算效率的同时，提高分割的精度和鲁棒性，尤其在面对复杂背景和高噪声情况下，能够提供更为精确的结果。这篇论文展示了如何利用Matlab平台的优势，结合K均值聚类和空间信息，设计出一种适用于医疗图像分割的高效算法，这在当前医疗影像分析领域具有重要的研究价值和实践意义。"

2010 Second International conference on Computing, Communication and Networking Technologies

A ROBUST FUZZY CLUSTERING TECHNIQUE WITH SPATIAL NEIGHBORHOOD

INFORMATION FOR EFFECTIVE MEDICAL IMAGE SEGMENTATION

An Efficient Variants of Fuzzy Clustering Technique with Spatial Information

for Effective Noisy Medical Image Segmentation

S.Zulaikha Beevi

, M.Mohammed Sathik

, K.Senthamaraikannan

,J.H.Jaseema Yasmin

Assistant Professor, Department of IT, National College of Engineering, Tamilnadu, India.

Associate Professor, Department of Computer Science, Sathakathullah Appa College, Tamilnadu, India.

Professor & Head, Department of Statistics, Manonmaniam Sundaranar University, Tamilnadu, India.

Assistant Professor, Department of Computer Science, National College of Engineering, Tamilnadu, Inida.

Abstract- Segmentation is an important step in many medical

imaging applications and a variety of image segmentation

techniques do exist. Of them, a group of segmentation algorithms

is based on the clustering concepts. In our research, we have

intended to devise efficient variants of Fuzzy C-Means (FCM)

clustering towards effective segmentation of medical images. The

enhanced variants of FCM clustering are to be devised in a way

to effectively segment noisy medical images. The medical images

generally are bound to contain noise while acquisition. So, the

algorithms devised for medical image segmentation must be

robust to noise for achieving desirable segmentation results. The

existing variants of FCM-based algorithms, segment images

without considering the spatial information, which makes it

sensitive to noise. We proposed the algorithm, which incorporate

spatial information into FCM, have shown considerable resilience

to noise, yet with increased noise levels in images, these

approaches have not performed exceptionally well. In the

proposed research, the input noisy medical images are employed

to a denoising algorithm with the help of effective denoising

algorithm prior to segmentation. Moreover, the proposed

approach will improve upon the existing variants of FCM-based

segmentation algorithms by integrating the spatial neighborhood

information present in the images for better segmentation. The

spatial neighborhood information of the images will be

determined using a factor that represents the spatial influence of

the neighboring pixels on the current pixel. The employed factor

works on the assumption that the membership degree of a pixel to

a cluster is greatly influenced by the membership of its

neighborhood pixels. Subsequently, the denoised images will be

segmented using the designed variants of FCM. The proposed

segmentation approach will be robust to noisy images even at

increased levels of noise, thereby enabling effective segmentation

of noisy medical images.

Index Terms - clustering, fuzzy C-means, image segmentation,

membership function, variants.

I.I

NTRODUCTION

Data clustering is a common technique for statistical data

analysis, which is used in many fields, including machine

learning, data mining, pattern recognition, image analysis and

bioinformatics. Clustering is the classification of similar

objects into different groups, or more precisely, the

partitioning of a data set into subsets (clusters), so that the data

in each subset (ideally) share some common trait - often

proximity according to some defined distance measure.

Medical imaging techniques such as X - ray, computed

tomography (CT), magnetic resonance imaging (MRI),

positron emission tomography (PET), ultrasound (USG), etc.

are indispensable for the precise analysis of various medical

pathologies. Computer power and medical scanner data alone

are not enough. We need the art to extract the necessary

boundaries, surfaces, and segmented volumes these organs in

the spatial and temporal domains. This art of organ extraction

is segmentation. Image segmentation is essentially a process of

pixel classification, wherein the image pixels are segmented

into subsets by assigning the individual pixels to classes. These

segmented organs and their boundaries are very critical in the

quantification process for physicians and medical surgeons, in

any branch of medicine, which deals with imaging [1].

Recently, fuzzy techniques are often applied as complementary

to existing techniques and can contribute to the development

of better and more robust methods, as it has been illustrated in

numerous scientific branches. It seems to be proved that

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