{"title":"3D Microscopic Images Segmenter Modeling by Applying Two-Stage Optimization to an Ensemble of Segmentation Methods Using a Genetic Algorithm","authors":"Muamer Kafadar, Zikrija Avdagic, Ingmar Besic, Samir Omanovic","doi":"10.1002/ima.70058","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This paper presents research related to segmentation based on supervisory control, at multiple levels, of optimization of parameters of segmentation methods, and adjustment of 3D microscopic images, with the aim of creating a more efficient segmentation approach. The challenge is how to improve the segmentation of 3D microscopic images using known segmentation methods, but without losing processing speed. In the first phase of this research, a model was developed based on an ensemble of 11 segmentation methods whose parameters were optimized using genetic algorithms (GA). Optimization of the ensemble of segmentation methods using GA produces a set of segmenters that are further evaluated using a two-stage voting system, with the aim of finding the best segmenter configuration according to multiple criteria. In the second phase of this research, the final segmenter model is developed as a result of two-level optimization. The best obtained segmenter does not affect the speed of image processing in the exploitation process as its operating speed is practically equal to the processing speed of the basic segmentation method. Objective selection and fine-tuning of the segmenter was done using multiple segmentation methods. Each of these methods has been subject to an intensive process of a significant number of two-stage optimization cycles. The metric has been specifically created for objective analysis of segmenter performance and was used as a fitness function during GA optimization and result validation. Compared to the expert manual segmentation, segmenter score is 99.73% according to the best mean segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set). Segmenter score is 99.49% according to the most stable segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set and considering the reference image classes MGTI median, MGTI voter and GGTI).</p>\n </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Imaging Systems and Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ima.70058","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents research related to segmentation based on supervisory control, at multiple levels, of optimization of parameters of segmentation methods, and adjustment of 3D microscopic images, with the aim of creating a more efficient segmentation approach. The challenge is how to improve the segmentation of 3D microscopic images using known segmentation methods, but without losing processing speed. In the first phase of this research, a model was developed based on an ensemble of 11 segmentation methods whose parameters were optimized using genetic algorithms (GA). Optimization of the ensemble of segmentation methods using GA produces a set of segmenters that are further evaluated using a two-stage voting system, with the aim of finding the best segmenter configuration according to multiple criteria. In the second phase of this research, the final segmenter model is developed as a result of two-level optimization. The best obtained segmenter does not affect the speed of image processing in the exploitation process as its operating speed is practically equal to the processing speed of the basic segmentation method. Objective selection and fine-tuning of the segmenter was done using multiple segmentation methods. Each of these methods has been subject to an intensive process of a significant number of two-stage optimization cycles. The metric has been specifically created for objective analysis of segmenter performance and was used as a fitness function during GA optimization and result validation. Compared to the expert manual segmentation, segmenter score is 99.73% according to the best mean segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set). Segmenter score is 99.49% according to the most stable segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set and considering the reference image classes MGTI median, MGTI voter and GGTI).
期刊介绍:
The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals.
IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging.
The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered.
The scope of the journal includes, but is not limited to, the following in the context of biomedical research:
Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.;
Neuromodulation and brain stimulation techniques such as TMS and tDCS;
Software and hardware for imaging, especially related to human and animal health;
Image segmentation in normal and clinical populations;
Pattern analysis and classification using machine learning techniques;
Computational modeling and analysis;
Brain connectivity and connectomics;
Systems-level characterization of brain function;
Neural networks and neurorobotics;
Computer vision, based on human/animal physiology;
Brain-computer interface (BCI) technology;
Big data, databasing and data mining.