A novel framework for MR image segmentation and quantification by using MedGA.

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dc.creatorRundo, Leonardo
dc.creatorTangherloni, Andrea
dc.creatorCazzaniga, Paolo
dc.creatorNobile, Marco S
dc.creatorRusso, Giorgio
dc.creatorGilardi, Maria Carla
dc.creatorVitabile, Salvatore
dc.creatorMauri, Giancarlo
dc.creatorBesozzi, Daniela
dc.creatorMilitello, Carmelo
dc.date2020-09-17T23:31:06Z
dc.date2020-09-17T23:31:06Z
dc.date2019-07
dc.date.accessioned2026-08-02T19:58:41Z
dc.descriptionBACKGROUND AND OBJECTIVES: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. METHODS: In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. RESULTS: The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics. CONCLUSIONS: Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.
dc.formatapplication/pdf
dc.identifier0169-2607
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/310436
dc.identifier10.17863/CAM.57530
dc.identifier1872-7565
dc.identifier.urihttps://repo.dare.co.zw/handle/123456789/80737
dc.languageeng
dc.languageeng
dc.publisherElsevier
dc.publisherhttps://doi.org/10.1016/j.cmpb.2019.04.016
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAdaptive thresholding
dc.subjectBimodal intensity distribution
dc.subjectEvolutionary computation
dc.subjectImage pre-processing
dc.subjectMagnetic Resonance imaging
dc.subjectQuantitative medical imaging
dc.subjectAlgorithms
dc.subjectBrain Neoplasms
dc.subjectComputer Simulation
dc.subjectDecision Making
dc.subjectFemale
dc.subjectHumans
dc.subjectImage Processing, Computer-Assisted
dc.subjectLeiomyoma
dc.subjectMagnetic Resonance Imaging
dc.subjectNeurosurgery
dc.subjectRadiosurgery
dc.subjectSoftware
dc.titleA novel framework for MR image segmentation and quantification by using MedGA.
dc.typeArticle

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