Leonardo Rundo1, Andrea Tangherloni2, Paolo Cazzaniga3, Marco S Nobile4, Giorgio Russo5, Maria Carla Gilardi6, Salvatore Vitabile7, Giancarlo Mauri8, Daniela Besozzi9, Carmelo Militello10. 1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy; Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, Cambridge, UK. Electronic address: leonardo.rundo@disco.unimib.it. 2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK. Electronic address: andrea.tangherloni@disco.unimib.it. 3. Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy. Electronic address: paolo.cazzaniga@unibg.it. 4. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy. Electronic address: nobile@disco.unimib.it. 5. Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy. Electronic address: giorgio.russo@ibfm.cnr.it. 6. Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy. Electronic address: mariacarla.gilardi@ibfm.cnr.it. 7. Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy. Electronic address: salvatore.vitabile@unipa.it. 8. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy. Electronic address: mauri@disco.unimib.it. 9. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy. Electronic address: daniela.besozzi@unimib.it. 10. Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy. Electronic address: carmelo.militello@ibfm.cnr.it.
Abstract
BACKGROUND 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.
BACKGROUND 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.
Authors: Leonardo Rundo; Andrea Tangherloni; Darren R Tyson; Riccardo Betta; Carmelo Militello; Simone Spolaor; Marco S Nobile; Daniela Besozzi; Alexander L R Lubbock; Vito Quaranta; Giancarlo Mauri; Carlos F Lopez; Paolo Cazzaniga Journal: Appl Sci (Basel) Date: 2020-09-06 Impact factor: 2.679