FMCSSE:用于生物医学图像分割的模糊修正布谷鸟搜索与空间探索

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Soft Computing Pub Date : 2024-07-24 DOI:10.1007/s00500-024-09905-7
Shouvik Chakraborty
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引用次数: 0

摘要

生物医学图像分割被认为是一项重要而具有挑战性的任务。自动生物医学图像分析在早期快速诊断疾病方面发挥着重要作用。准确和精确的分割可以实现早期治疗规划,这就需要复杂的方法。受此启发,我们提出了一种新方法。这种方法被称为空间探索模糊修正布谷鸟搜索(FMCSSE)。像素间的高度相关性是图像数据的一个重要属性,特定像素周围的像素拥有相似的特征信息。因此,考虑空间信息对生成有意义的分割图像极为重要。传统的模糊聚类方法并不适合利用空间信息。因此,这项工作旨在借助模糊修正的布谷鸟搜索方法来探索空间信息,并从生物医学图像中找到最佳聚类。该方法适用于不同的生物医学图像,并与 FEMO、FMCS、MCS 和 CS 等各种最先进的无监督方法进行了比较。所提出的方法不受群集中心初始分配选择的影响。所提出的方法使用 2 型模糊系统,并结合了改进的布谷鸟搜索(McCulloch 方法)和空间探索程序。定性和定量结果都显示了 FMCSSE 方法在性能方面的优越性。
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FMCSSE: fuzzy modified cuckoo search with spatial exploration for biomedical image segmentation

Biomedical image segmentation is considered an important and challenging task. Automated biomedical image analysis plays a major role in the early and quick diagnosis of diseases. Accurate and precise segmentation can lead to early treatment planning and it demands sophisticated approaches. Inspired by this, a novel approach is proposed. This approach will be known as the Fuzzy modified cuckoo search with spatial exploration (FMCSSE). High correlation among pixels is an important property of image data and pixels surrounding a particular pixel possess similar feature information. Therefore, it is extremely essential to consider the spatial information to generate a meaningful segmented image. The traditional fuzzy clustering approach is not suitable for exploiting spatial information. Therefore, this work is designed to explore spatial information and find the optimal clusters from biomedical images with the help of the fuzzy-modified cuckoo search approach. This approach is applied to different biomedical images and compared with various state-of-the-art unsupervised approaches like FEMO, FMCS, MCS, and CS. The proposed approach does not suffer from the choice of the initial assignment of the cluster centers. The proposed approach uses the type-2 fuzzy system blended with the modified cuckoo search (McCulloch approach) and spatial exploration procedure. Both qualitative and quantitative results show the superiority of the FMCSSE approach in terms of performance.

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来源期刊
Soft Computing
Soft Computing 工程技术-计算机:跨学科应用
CiteScore
8.10
自引率
9.80%
发文量
927
审稿时长
7.3 months
期刊介绍: Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.
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