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

Rundo, Leonardo; Tangherloni, Andrea; Cazzaniga, Paolo; Nobile, Marco S.; Russo, Giorgio; Gilardi, Maria Carla; Vitabile, Salvatore; Mauri, Giancarlo; Besozzi, Daniela; Militello, Carmelo

doi:10.1016/j.cmpb.2019.04.016

Cited by 51 publications

(35 citation statements)

References 68 publications

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“…We also developed a Parent-Workers strategy using mpi4py, which provides bindings of the Message Passing Interface (MPI) specifications for Python to leverage multi-core and many-core resources [69]. The distributed strategy used to accelerate ACDC is similar to that employed in [70][71][72], where the Parent allocates the resources and orchestrates the workers, which run ACDC to analyze the assigned images. This distributed version of ACDC is 3.7× faster than the sequential version by exploiting 6 cores of a CPU Intel Core E5-2650 v4 (clock 2.2 GHz).…”

Section: Implementation Detailsmentioning

confidence: 99%

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

et al. 2020

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Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

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Section: Implementation Detailsmentioning

confidence: 99%

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

et al. 2020

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“…ψ c A k is the quality of the color distribution, and H(s) is the evaluation of ψ c A k for each superpixel k. H(s) = k ψ c A k . ψ c A k is used to evaluate the distance between colors and the concentration degree of colors in a histogram, as shown in Equation (11).…”

Section: Metric Function Of Color Uniformitymentioning

confidence: 99%

“…Veganzones et al proposed a hyperspectral image segmentation method using a new spectral unmixing-based binary partition tree representation [10]. Rundo et al proposed an intelligent image analysis framework for image enhancement, automatic global thresholding and segmentation [11]. However, the above traditional object-level segmentation algorithms have the common problem of under-segmentation, which has a significant impact on post-processing.…”

Section: Introductionmentioning

confidence: 99%

Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information

Lin

Zhang

2020

Applied Sciences

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Superpixel segmentation (SS) methods have been proven to be feasible in improving the performance of hybrid algorithms on hyperspectral images (HSIs). In this paper, a superpixel segmentation algorithm based on the information measures with color histogram driving (IM-CHD) was proposed. First, Shannon entropy was applied to measure the image information and preliminarily select spectral bands. Mutual information (MI) is derived from the concept of entropy and measures the statistical dependence between two random variables. Also, MI can effectively identify the redundant spectral bands. Therefore, in this paper, both MI and color matching functions (CMF) were used to select the most useful spectral bands. Second, the selected spectral bands were combined into a false color image containing the main spectral information. A local optimization algorithm named “hill climbing” was used to achieve the superpixel segmentation. Finally, parameter selection experiments and comparative experiments were performed on two hyperspectral data sets. The experimental results showed that the IM-CHD method was more efficient and accurate than other state-of-the-art methods.

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“…A novel evolutionary method based on GAs for medical image enhancement and segmentation, named MedGA, was recently presented in [56], by using the efficient histogram-based encoding of individuals defined in [57]. MedGA aims at better revealing the two underlying sub-distributions occurring in a medical image sub-region characterised by a roughly bimodal histogram, in order to improve thresholding-based segmentation results [58]. An extensive review of the applications of GAs to medical image segmentation is presented in [59].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Rundo

Militello

Vitabile

et al. 2019

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Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets-i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring-to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image

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

Cited by 51 publications

References 68 publications

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

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