基于Patch-Based Processing、k-means聚类和目标计数混合技术的早期多发性脑肿瘤检测与定位。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2020-01-06 eCollection Date: 2020-01-01 DOI:10.1155/2020/9035096
Mohamed Nasor, Walid Obaid
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引用次数: 24

摘要

脑肿瘤是影响许多人生活的主要健康问题。这些肿瘤分为良性和癌性。如果诊断和治疗不当,后者可能是致命的。因此,在脑肿瘤发展的早期阶段进行诊断,可以显著提高患者治疗后完全康复的机会。除了实验室分析外,临床医生和外科医生还从磁共振成像(MRI)、x射线和计算机断层扫描(CT)等各种系统记录的医学图像中提取信息。提取的信息用于识别脑肿瘤的基本特征(位置、大小和类型),以实现准确的诊断,确定最合适的治疗方案。在本文中,我们提出了一种自动机器视觉技术,用于在MRI图像的早期阶段检测和定位脑肿瘤,该技术结合了k-means聚类、基于补丁的图像处理、目标计数和肿瘤评估。该技术在20张真实的MRI图像上进行了测试,发现能够检测MRI图像中的多个肿瘤,无论其强度水平变化,大小和位置如何,包括那些非常小的肿瘤。除了用于诊断之外,该技术还可以集成到自动化治疗仪器和机器人手术系统中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting.

Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient's full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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