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Siracusano G, La Corte A, Nucera AG, Gaeta M, Chiappini M, Finocchio G. Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability. Sci Rep 2023; 13:22471. [PMID: 38110512 PMCID: PMC10728198 DOI: 10.1038/s41598-023-49534-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 12/09/2023] [Indexed: 12/20/2023] Open
Abstract
Preprocessing is an essential task for the correct analysis of digital medical images. In particular, X-ray imaging might contain artifacts, low contrast, diffractions or intensity inhomogeneities. Recently, we have developed a procedure named PACE that is able to improve chest X-ray (CXR) images including the enforcement of clinical evaluation of pneumonia originated by COVID-19. At the clinical benchmark state of this tool, there have been found some peculiar conditions causing a reduction of details over large bright regions (as in ground-glass opacities and in pleural effusions in bedridden patients) and resulting in oversaturated areas. Here, we have significantly improved the overall performance of the original approach including the results in those specific cases by developing PACE2.0. It combines 2D image decomposition, non-local means denoising, gamma correction, and recursive algorithms to improve image quality. The tool has been evaluated using three metrics: contrast improvement index, information entropy, and effective measure of enhancement, resulting in an average increase of 35% in CII, 7.5% in ENT, 95.6% in EME and 13% in BRISQUE against original radiographies. Additionally, the enhanced images were fed to a pre-trained DenseNet-121 model for transfer learning, resulting in an increase in classification accuracy from 80 to 94% and recall from 89 to 97%, respectively. These improvements led to a potential enhancement of the interpretability of lesion detection in CXRs. PACE2.0 has the potential to become a valuable tool for clinical decision support and could help healthcare professionals detect pneumonia more accurately.
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Affiliation(s)
- Giulio Siracusano
- Department of Electric, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125, Catania, Italy.
| | - Aurelio La Corte
- Department of Electric, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125, Catania, Italy
| | - Annamaria Giuseppina Nucera
- Unit of Radiology, Department of Advanced Diagnostic-Therapeutic Technologies, "Bianchi-Melacrino-Morelli" Hospital, Reggio Calabria, Via Giuseppe Melacrino, 21, 89124, Reggio Calabria, Italy
| | - Michele Gaeta
- Department of Biomedical Sciences, Dental and of Morphological and Functional Images, University of Messina, Via Consolare Valeria 1, 98125, Messina, Italy
| | - Massimo Chiappini
- Istituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata 605, 00143, Rome, Italy.
- Maris Scarl, Via Vigna Murata 606, 00143, Rome, Italy.
| | - Giovanni Finocchio
- Istituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata 605, 00143, Rome, Italy.
- Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina, V.le F. Stagno D'Alcontres 31, 98166, Messina, Italy.
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Ghaempanah H, Tavakoli M, Deevband MR, Alvar AA, Najafi M, Kelley P. Electronic portal image enhancement based on nonuniformity correction in wavelet domain. Med Phys 2022; 49:4599-4612. [DOI: 10.1002/mp.15672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 04/04/2022] [Accepted: 04/04/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
- Hanieh Ghaempanah
- Department of Biomedical Engineering and Medical Physics Faculty of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Meysam Tavakoli
- Department of Radiation Oncology University of Pittsburgh School of Medicine and UPMC Hillman Cancer Center Pittsburgh PA USA
- Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA
| | - Mohammad Reza Deevband
- Department of Biomedical Engineering and Medical Physics Faculty of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Amin Asgharzadeh Alvar
- Department of Biomedical Engineering and Medical Physics Faculty of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Mohsen Najafi
- Department of Biomedical Engineering and Medical Physics Faculty of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Patrick Kelley
- Department of Physics Indiana University‐Purdue University Indianapolis Indiana USA
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Panetta K, Rajendran R, Ramesh A, Rao S, Agaian S. Tufts Dental Database: A Multimodal Panoramic X-ray Dataset for Benchmarking Diagnostic Systems. IEEE J Biomed Health Inform 2021; 26:1650-1659. [PMID: 34606466 DOI: 10.1109/jbhi.2021.3117575] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The application of Artificial Intelligence in dental healthcare has a very promising role due to the abundance of imagery and non-imagery-based clinical data. Expert analysis of dental radiographs can provide crucial information for clinical diagnosis and treatment. In recent years, Convolutional Neural Networks have achieved the highest accuracy in various benchmarks, including analyzing dental X-ray images to improve clinical care quality. The Tufts Dental Database, a new X-ray panoramic radiography image dataset, has been presented in this paper. This dataset consists of 1000 panoramic dental radiography images with expert labeling of abnormalities and teeth. The classification of radiography images was performed based on five different levels: anatomical location, peripheral characteristics, radiodensity, effects on the surrounding structure, and the abnormality category. This first-of-its-kind multimodal dataset also includes the radiologist's expertise captured in the form of eye-tracking and think-aloud protocol. The contributions of this work are 1) publicly available dataset that can help researchers to incorporate human expertise into AI and achieve more robust and accurate abnormality detection; 2) a benchmark performance analysis for various state-of-the-art systems for dental radiograph image enhancement and image segmentation using deep learning; 3) an in-depth review of various panoramic dental image datasets, along with segmentation and detection systems. The release of this dataset aims to propel the development of AI-powered automated abnormality detection and classification in dental panoramic radiographs, enhance tooth segmentation algorithms, and the ability to distill the radiologist's expertise into AI.
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Mottola M, Ursprung S, Rundo L, Sanchez LE, Klatte T, Mendichovszky I, Stewart GD, Sala E, Bevilacqua A. Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients. Sci Rep 2021; 11:11542. [PMID: 34078993 PMCID: PMC8172898 DOI: 10.1038/s41598-021-90985-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 05/10/2021] [Indexed: 12/19/2022] Open
Abstract
Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.
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Affiliation(s)
- Margherita Mottola
- Department of Electrical, Electronic, and Information Engineering (DEI), University of Bologna, 40136, Bologna, Italy
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, 40125, Bologna, Italy
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Tobias Klatte
- Department of Surgery, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Urology, Royal Bournemouth Hospital, Bournemouth, BH7 7DW, UK
| | | | - Grant D Stewart
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Surgery, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Alessandro Bevilacqua
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, 40125, Bologna, Italy.
- Department of Computer Science and Engineering (DISI), University of Bologna, 40136, Bologna, Italy.
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Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE). SUSTAINABILITY 2020. [DOI: 10.3390/su12208573] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease; this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post-processing tool, named PACE (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.
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Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images. Int J Biomed Imaging 2016; 2016:4710842. [PMID: 27127497 PMCID: PMC4830760 DOI: 10.1155/2016/4710842] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 03/15/2016] [Indexed: 11/18/2022] Open
Abstract
Medical imaging systems often produce images that require enhancement, such as improving the image contrast as they are poor in contrast. Therefore, they must be enhanced before they are examined by medical professionals. This is necessary for proper diagnosis and subsequent treatment. We do have various enhancement algorithms which enhance the medical images to different extents. We also have various quantitative metrics or measures which evaluate the quality of an image. This paper suggests the most appropriate measures for two of the medical images, namely, brain cancer images and breast cancer images.
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