A novel framework for MR image segmentation and quantification by using MedGA. (July 2019) - Rundo, Leonardo; Tangherloni, Andrea; Cazzaniga, Paolo; Nobile, Marco S.; Russo, Giorgio; Gilardi, Maria Carla; Vitabile, Salvatore; Mauri, Giancarlo; Besozzi, Daniela; Militello, Carmelo
Highlights: We propose an evolutionary-based computational framework for MR images. Pre-processing tool better separates the sub-distributions in bimodal intensity histograms. Genetic Algorithms considerably increase the accuracy of segmentation results. The proposed computational framework outperforms the state-of-the-art approaches. Measurement repeatability in clinical workflows is highly improved. Graphical abstract: 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.