1
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Kabir MH, Reformat M, Hryniuk SS, Stampe K, Lou E. Validity of machine learning algorithms for automatically extract growing rod length on radiographs in children with early-onset scoliosis. Med Biol Eng Comput 2024:10.1007/s11517-024-03181-1. [PMID: 39152359 DOI: 10.1007/s11517-024-03181-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 08/04/2024] [Indexed: 08/19/2024]
Abstract
The magnetically controlled growing rod technique is an effective surgical treatment for children who have early-onset scoliosis. The length of the instrumented growing rods is adjusted regularly to compensate for the normal growth of these patients. Manual measurement of rod length on posteroanterior spine radiographs is subjective and time-consuming. A machine learning (ML) system using a deep learning approach was developed to automatically measure the adjusted rod length. Three ML models-rod model, 58 mm model, and head-piece model-were developed to extract the rod length from radiographs. Three-hundred and eighty-seven radiographs were used for model development, and 60 radiographs with 118 rods were separated for final testing. The average precision (AP), the mean absolute difference (MAD) ± standard deviation (SD), and the inter-method correlation coefficient (ICC[2,1]) between the manual and artificial intelligence (AI) adjustment measurements were used to evaluate the developed method. The AP of the 3 models were 67.6%, 94.8%, and 86.3%, respectively. The MAD ± SD of the rod length change was 0.98 ± 0.88 mm, and the ICC[2,1] was 0.90. The average time to output a single rod measurement was 6.1 s. The developed AI provided an accurate and reliable method to detect the rod length automatically.
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Affiliation(s)
- Mohammad Humayun Kabir
- Department of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada
| | - Marek Reformat
- Department of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada
| | | | - Kyle Stampe
- Department of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada
- Department of Surgery, University of Alberta, Edmonton, AB, Canada
| | - Edmond Lou
- Department of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada.
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2
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Chang SS, Lin CT, Wang WC, Hsu KC, Wu YL, Liu CH, Fann YC. Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images. Sci Rep 2024; 14:6640. [PMID: 38503839 PMCID: PMC10951254 DOI: 10.1038/s41598-024-57198-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/15/2024] [Indexed: 03/21/2024] Open
Abstract
Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.
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Affiliation(s)
- Shih-Sheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Ching-Ting Lin
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
| | - Wei-Chun Wang
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan
- Neuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan
- Neuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Ya-Lun Wu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
| | - Chia-Hao Liu
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yang C Fann
- Division of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 35 Convent Dr., Bethesda, MD, 20892, USA.
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3
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Zhao T, Zhang SX. X-ray Image Enhancement Based on Nonsubsampled Shearlet Transform and Gradient Domain Guided Filtering. SENSORS (BASEL, SWITZERLAND) 2022; 22:4074. [PMID: 35684702 PMCID: PMC9185538 DOI: 10.3390/s22114074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/16/2022] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms.
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Affiliation(s)
- Tao Zhao
- School of Mechanical Engineering, Hebei University of Technology, Tianjin 300131, China;
- Department of Mechanical Engineering, Zhonghuan Information College Tianjin University of Technology, Tianjin 300380, China
| | - Si-Xiang Zhang
- School of Mechanical Engineering, Hebei University of Technology, Tianjin 300131, China;
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4
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Ghosh SK, Ghosh A. A novel hyperbolic intuitionistic fuzzy divergence measure based mammogram enhancement for visual elucidation of breast lesions. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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5
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A customized acutance metric for quality control applications in MRI. Med Biol Eng Comput 2022; 60:1511-1525. [DOI: 10.1007/s11517-022-02547-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 03/06/2022] [Indexed: 11/26/2022]
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6
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Ramlal SD, Sachdeva J, Ahuja CK, Khandelwal N. Multimodal Medical Image Fusion Using Nonsubsampled Shearlet Transform and Smallest Uni-Value Segment Assimilating Nucleus. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422570014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper presents a new fusion scheme for medical (CT-MRI) images which is based on the nonsubsampled shearlet transform (NSST). The various image pairs to be fused are obtained from primary and internet sources. Initially, the images are decomposed through NSST into general and detailed features. The smallest uni-value segment assimilating nucleus (SUSAN) and local sum of Gaussian weighted pixel intensities-based activity measures are proposed to fuse the detailed sub-bands and low-frequency sub-band of NSST, respectively, for faster execution of the algorithm. Visual and parametric comparison of the proposed scheme is done through five traditional fusion algorithms using nine fusion performance parameters. In addition, Wilcoxon signed ranks test is also applied to compare different methods scientifically with the proposed fusion scheme. It is observed that the presented method is better in retaining bone, calcification, cerebrospinal fluid (CSF), edema and tumor details of the source images and is faster than other classical fusion schemes. The fused images of the proposed method are suitable for locating the site of biopsy externally or incision location in the bone of the brain skull with minimum diagnostic time.
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Affiliation(s)
- Sharma Dileepkumar Ramlal
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
- Electronics and Telecommunication Engineering Department, Eternal University, Baru Sahib, H.P., India
- Chitkara University Institute of Engineering & Technology, Baddi, HP, India
| | - Jainy Sachdeva
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
| | - Chirag Kamal Ahuja
- Department of Radio-Diagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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7
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A skewness reformed complex diffusion based unsharp masking for the restoration and enhancement of Poisson noise corrupted mammograms. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103421] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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8
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Mall PK, Singh PK. BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images. INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022; 13:658-672. [PMCID: PMC8750368 DOI: 10.1007/s13198-021-01580-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/25/2021] [Accepted: 11/30/2021] [Indexed: 06/02/2023]
Abstract
In clinical treatment, deep learning plays a pivotal role in medical image classification. Deep learning techniques provide opportunities for radiologists and orthopedic to ease out their lives with faster and more accurate results. The traditional deep learning approach nevertheless reached its performance ceiling. Therefore, in this paper, we investigate different enhancement techniques to boost the deep neural networks performance and provide a solution as BoostNet. The experiment is categorized into four different phases. We have selected ChampNet from benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet18, VGG19). This phase helps to obtain the best model. In the second phase, The ChampNet evaluates with different resolution datasets. This phase helps to finalize the dataset resolution to enhance the performance of ChampNet. In the third phase, Champ-Net merges with image enhancement techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE), High-frequency filtering (HEF), and Unsharp masking (UM). This phase helps to obtain Boost-Net with enriched performance. The last phase helps us to verify BoostNet results with Lightness Order Error. The presented research work fuses the image enhancement technique with ChampNet to generate BoostNet models. An assessment was performed on the Musculoskeletal Radiograph Bone Classification using classification schemes to demonstrate the proposed model's performance. The Classification accuracy of BoostNet was for the train a test dataset with and without enhancement techniques. The proposed model ChampNet + CLAHE, ChampNet + HEF, ChampNet + UM approach achieved 95.88%, 94.99%, and 94.18% accuracy, respectively. This experiment leads to a more accurate and efficient classification model. The main aim of this paper is to enhance techniques to boost the deep neural networks performance and provide a solution as BoostNet.
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Affiliation(s)
- Pawan Kumar Mall
- Madan Mohan Malaviya University of Technology, Gorakhpur, U.P. India
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9
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Siddiqi AA, Khawaja A, Hashmi A. A novel fuzzy blend scheme for image enhancement of CT scans bearing liver cancer. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2021.1899853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Ayesha Amir Siddiqi
- Telecommunication Engineering, Dawood University of Engineering & Technology, Karachi, Pakistan
| | - Attaullah Khawaja
- Electronic Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Adnan Hashmi
- Radiology Department, KIRAN Hospital, Karachi, Pakistan
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10
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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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11
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Chaahat, Kumar Gondhi N, Kumar Lehana P. An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8113403. [PMID: 34326979 PMCID: PMC8302402 DOI: 10.1155/2021/8113403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022]
Abstract
Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologists and mostly rely on the analysis of remotely accessible experts through mobile-based networks for sharing the images and other related information. Under such circumstances, poor image quality introduced due to the capturing device results in misleading diagnosis. Here, a genetic-algorithm- (GA-) based approach used as an image enhancement technique has been explored to improve the low quality of the dermatological images received from the rural clinic. The diagnosis is performed on the enhanced images using convolutional neural network (CNN) classifier for the identification of the diseases. The scope of this paper is limited to only motion blurred images, which is the most prevalent problem in capturing of the images, specifically when any of the two (device or the object) may move unpredictably. Seven types of skin diseases, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, vascular lesion, and squamous cell carcinoma, have been investigated using ResNet-152 giving an overall accuracy of 87.40% for the blurred images. Use of GA-enhanced images increased the accuracy to 95.85%. The results were further analyzed using a confusion matrix and t-test-based statistical investigations. The advantage of the proposed technique is that it reduces the analysis time and errors due to manual diagnosis. Furthermore, speedy and reliable diagnosis at the earliest stage reduces the risk of developing more severe skin problems.
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Affiliation(s)
- Chaahat
- Department of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182301, India
- MIET, Jammu 181122, India
| | - Naveen Kumar Gondhi
- Department of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182301, India
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12
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Jabbar SI, Aladi AQ, Day C, Chadwick E. A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique. Biomed Phys Eng Express 2021; 7. [PMID: 34161931 DOI: 10.1088/2057-1976/ac0dce] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 06/23/2021] [Indexed: 11/12/2022]
Abstract
Improving the clarity and visual quality of Musculoskeletal Ultrasound Images (MUI) can help clinicians to detect diseases more easily and accurately. In this work, we described how to enhance the contrast of MUI locally based on a fuzzy inference system. Local Fuzzy Inference Technique (LFIT) was introduced as a novel technique to enhance the contrast of MUI. The input data used musculoskeletal ultrasound images were collected from healthy volunteers. Local Fuzzy Inference Technique (LFIT) was compared with a recent fuzzy technique of the image enhancement and validated based on assessment metrics (second-derivative-like measure of enhancement (SDME)). The results advocated an improved quality of the musculoskeletal ultrasound images based on the LFIT technique with approximately 11% greater than recent technique and computation time of LFIT is 28.4% is less. It is possible to apply a proposal technique on the other types of image (panoramic image and video). Furthermore, observed improvements on the MUI quality could potentially invested as a pre-processing step before performing other computer vision applications, such as image segmentation, tracking, and 3D image reconstruction.
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Affiliation(s)
| | | | - Charles Day
- School of computing and mathematics, Keele University, United Kingdom
| | - Edward Chadwick
- School of Engineering, University of Aberdeen, United Kingdom
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13
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Srinivas K, Bhandari AK, Kumar PK. A Context-Based Image Contrast Enhancement Using Energy Equalization With Clipping Limit. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:5391-5401. [PMID: 34057893 DOI: 10.1109/tip.2021.3083448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this paper, a new context-based image contrast enhancement process using energy curve equalization (ECE) with a clipping limit has been proposed. In a fundamental anomaly to the existing contrast enhancement practice using histogram equalization, the projected method uses the energy curve. The computation of the energy curve utilizes a modified Hopfield neural network architecture. This process embraces the image's spatial adjacency information to the energy curve. For each intensity level, the energy value is calculated and the overall energy curve appears to be smoother than the histogram. A clipping limit applies to evade the over enhancement and is chosen as the average of the mean and median value. The clipped energy curve is subdivided into three regions based on the standard deviation value. Each part of the subdivided energy curve is equalized individually, and the final enhanced image is produced by combining transfer functions computed by the equalization process. The projected scheme's qualitative and quantitative efficiency is assessed by comparing it with the conventional histogram equalization techniques with and without the clipping limit.
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14
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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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15
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Deshmukh AB, Rani NU. Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution. INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS 2020. [DOI: 10.4018/ijdcf.2020070106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
One of the major challenges faced by video surveillance is recognition from low-resolution videos or person identification. Image enhancement methods play a significant role in enhancing the resolution of the video. This article introduces a technique for face super resolution based on a deep convolutional neural network (Deep CNN). At first, the video frames are extracted from the input video and the face detection is performed using the Viola-Jones algorithm. The detected face image and the scaling factors are fed into the Fractional-Grey Wolf Optimizer (FGWO)-based kernel weighted regression model and the proposed Deep CNN separately. Finally, the results obtained from both the techniques are integrated using a fuzzy logic system, offering a face image with enhanced resolution. Experimentation is carried out using the UCSD face video dataset, and the effectiveness of the proposed Deep CNN is checked depending on the block size and the upscaling factor values and is evaluated to be the best when compared to other existing techniques with an improved SDME value of 80.888.
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16
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Khan AA, Narejo GB. Analysis of Abdominal Computed Tomography Images for Automatic Liver Cancer Diagnosis Using Image Processing Algorithm. Curr Med Imaging 2019; 15:972-982. [DOI: 10.2174/1573405615666190716122040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 04/23/2019] [Accepted: 06/13/2019] [Indexed: 01/12/2023]
Abstract
Background:
The application of image processing algorithms for medical image analysis
has been found effectual in the past years. Imaging techniques provide assistance to the radiologists
and physicians for the diagnosis of abnormalities in different organs.
Objective:
The proposed algorithm is designed for automatic computer-aided diagnosis of liver
cancer from low contrast CT images. The idea expressed in this article is to classify the malignancy
of the liver tumor ahead of liver segmentation and to locate HCC burden on the liver.
Methods:
A novel Fuzzy Linguistic Constant (FLC) is designed for image enhancement. To classify
the enhanced liver image as cancerous or non-cancerous, fuzzy membership function is applied.
The extracted features are assessed for malignancy and benignancy using the structural similarity
index. The malignant CT image is further processed for automatic tumor segmentation and grading
by applying morphological image processing techniques.
Results:
The validity of the concept is verified on a dataset of 179 clinical cases which consist of
98 benign and 81 malignant liver tumors. Classification accuracy of 98.3% is achieved by Support
Vector Machine (SVM). The proposed method has the ability to automatically segment the tumor
with an improved detection rate of 78% and a precision value of 0.6.
Conclusion:
The algorithm design offers an efficient tool to the radiologist in classifying the malignant
cases from benign cases. The CAD system allows automatic segmentation of tumor and locates
tumor burden on the liver. The methodology adopted can aid medical practitioners in tumor
diagnosis and surgery planning.
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Affiliation(s)
- Ayesha Adil Khan
- Department of Electronics Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Ghous Bakhsh Narejo
- Department of Electronics Engineering, NED University of Engineering & Technology, Karachi, Pakistan
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17
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Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.069] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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18
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Duan X, Xu Y, Mei Y, Wu S, Ling Q, Qin G, Ma J, Chen C, Qi H, Zhou L. A Multiscale Contrast Enhancement for Mammogram Using Dynamic Unsharp Masking in Laplacian Pyramid. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2018.2876873] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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19
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Zhang CJ, Nie HH. An adaptive enhancement method for breast X-ray images based on the nonsubsampled contourlet transform domain and whale optimization algorithm. Med Biol Eng Comput 2019; 57:2245-2263. [PMID: 31410690 DOI: 10.1007/s11517-019-02022-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 07/27/2019] [Indexed: 11/28/2022]
Abstract
We propose a new method for breast X-ray image adaptive enhancement that combines nonsubsampled contourlet transform (NSCT) with the whale optimization algorithm (WOA). First, the mammography X-ray image was processed by histogram equalization to ensure global image contrast. The processed image was then decomposed into three layers in the NSCT domain. Each layer was each decomposed into two, four, and eight directions. A median filter was used to remove noise in the first and second layers. Then, a special edge filter was adopted to enhance each sub-band image, and two parameters are involved. WOA is used to automatically search the optimal two parameters. Blind image quality index (BIQI) adaptive function was used as an objective function of WOA. Then, inverse NSCT was employed to reconstruct the processed image, generating the final adaptive enhancement image. The digital database for screening mammography (DDSM) was used to verify the performance of the proposed method. Five objective evaluation indexes, including information entropy, average gradient, standard deviation, contrast improvement index (CII), and BIQI, are combined together to construct a new comprehensive index to evaluate the visual quality of the enhanced image. The results show that the proposed method has a good enhancement effect for mammography X-ray images. The overall performance of the proposed method is better than some existing similar methods. Graphical abstract .
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Affiliation(s)
- Chang-Jiang Zhang
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China.
| | - Huan-Huan Nie
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China.,College of Telecommunication Engineering, Zhejiang Post and Telecommunication College, Shaoxing, 312000, Zhejiang, China
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Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes. Adv Urol 2019; 2019:3590623. [PMID: 31164907 PMCID: PMC6507235 DOI: 10.1155/2019/3590623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 03/03/2019] [Accepted: 03/20/2019] [Indexed: 12/24/2022] Open
Abstract
Objective To develop software to assess the potential aggressiveness of an incidentally detected renal mass using images. Methods Thirty randomly selected patients who underwent nephrectomy for renal cell carcinoma (RCC) had their images independently reviewed by engineers. Tumor “Roughness” was based on image algorithm of tumor topographic features visualized on computed tomography (CT) scans. Univariant and multivariant statistical analyses are utilized for analysis. Results We investigated 30 subjects that underwent partial or radical nephrectomy. After excluding poor image-rendered images, 27 patients remained (benign cyst = 1, oncocytoma = 2, clear cell RCC = 15, papillary RCC = 7, and chromophobe RCC = 2). The mean roughness score for each mass is 1.18, 1.16, 1.27, 1.52, and 1.56 units, respectively (p < 0.004). Renal masses were correlated with tumor roughness (Pearson's, p=0.02). However, tumor size itself was larger in benign tumors (p=0.1). Linear regression analysis noted that the roughness score is the most influential on the model with all other demographics being equal including tumor size (p=0.003). Conclusion Using basic CT imaging software, tumor topography (“roughness”) can be quantified and correlated with histologies such as RCC subtype and could lead to determining aggressiveness of small renal masses.
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Oulefki A, Aouache M, Bengherabi M. Low-Light Face Image Enhancement Based on Dynamic Face Part Selection. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1007/978-3-030-31321-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Joseph J, Periyasamy R. Nonlinear sharpening of MR images using a locally adaptive sharpness gain and a noise reduction parameter. Pattern Anal Appl 2018. [DOI: 10.1007/s10044-018-0763-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Sadda P, Onofrey J, Imamoglu M, Papademetris X, Qarni B, Bahtiyar MO. Real-time computerized video enhancement for minimally invasive fetoscopic surgery. ACTA ACUST UNITED AC 2018; 1:27-32. [PMID: 31080936 DOI: 10.1016/j.lers.2018.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background The only definitive treatment for twin-to-twin transfusion syndrome is minimally invasive fetoscopic surgery for the selective coagulation of placental blood vessels. Fetoscopic surgery is a technically challenging operation, mainly due to the poor visibility conditions in the uterine environment. We present the design of an algorithm for the computerized enhancement of fetoscopic video and show that the enhanced video increases the ability of human users to identify blood vessels within fetoscopic video rapidly and accurately. Methods A computer algorithm for the enhancement of fetoscopic video frames was created. First, optical fiber artifacts were removed via a modification of unsharp masking. Second, image contrast was increased via Contrast Limited Adaptive Histogram Equalization (CLAHE). Third, the effect of contrast enhancements on stationary features was removed by normalizing to a windowed mean of the video frames. Fourth, color information was reincorporated by combining the mean-normalized result with the unnormalized contrast enhanced image using the soft light blending algorithm. Medical trainees (n = 16) were recruited into a study to validate the algorithm. Subjects were shown enhanced or unenhanced fetoscopic video frames on a screen and were asked to identify whether a randomly placed marker fell on a blood vessel or on background. The accuracy of their responses was recorded. Results On the subset of images where subjects had the lowest mean accuracy in identifying the placement of the marker, subjects performed better when viewing video frames enhanced by the computer (accuracy 74.27%; SE 0.97) than when viewing unenhanced video frames (accuracy 63.78%; SE 2.79). This result was statistically significant (p < 0.01). Conclusion Real-time computerized enhancement of fetoscopic video has the potential to ease the readability of video in poor lighting conditions, thus providing a benefit to the surgeon intraoperatively.
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Affiliation(s)
- Praneeth Sadda
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - John Onofrey
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.,Department of Biomedical Engineering, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Metehan Imamoglu
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.,Department of Obstetrics and Gynecology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.,Yale Fetal Care Center, Yale-New Haven Hospital, 1 Long Wharf Drive, New Haven, CT 06510, USA
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.,Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.,Department of Biomedical Engineering, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Bilal Qarni
- University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
| | - Mert Ozan Bahtiyar
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.,Department of Obstetrics and Gynecology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.,Yale Fetal Care Center, Yale-New Haven Hospital, 1 Long Wharf Drive, New Haven, CT 06510, USA
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Extracting tumor in MR brain and breast image with Kapur’s entropy based Cuckoo Search Optimization and morphological reconstruction filters. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.07.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Deshmukh AB, Usha Rani N. Fractional-Grey Wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0765-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Lee MS, Park CH, Kang MG. Edge enhancement algorithm for low-dose X-ray fluoroscopic imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 152:45-52. [PMID: 29054260 DOI: 10.1016/j.cmpb.2017.09.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 06/26/2017] [Accepted: 09/13/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Low-dose X-ray fluoroscopy has continually evolved to reduce radiation risk to patients during clinical diagnosis and surgery. However, the reduction in dose exposure causes quality degradation of the acquired images. In general, an X-ray device has a time-average pre-processor to remove the generated quantum noise. However, this pre-processor causes blurring and artifacts within the moving edge regions, and noise remains in the image. During high-pass filtering (HPF) to enhance edge detail, this noise in the image is amplified. METHODS In this study, a 2D edge enhancement algorithm comprising region adaptive HPF with the transient improvement (TI) method, as well as artifacts and noise reduction (ANR), was developed for degraded X-ray fluoroscopic images. The proposed method was applied in a static scene pre-processed by a low-dose X-ray fluoroscopy device. First, the sharpness of the X-ray image was improved using region adaptive HPF with the TI method, which facilitates sharpening of edge details without overshoot problems. Then, an ANR filter that uses an edge directional kernel was developed to remove the artifacts and noise that can occur during sharpening, while preserving edge details. RESULTS The quantitative and qualitative results obtained by applying the developed method to low-dose X-ray fluoroscopic images and visually and numerically comparing the final images with images improved using conventional edge enhancement techniques indicate that the proposed method outperforms existing edge enhancement methods in terms of objective criteria and subjective visual perception of the actual X-ray fluoroscopic image. CONCLUSIONS The developed edge enhancement algorithm performed well when applied to actual low-dose X-ray fluoroscopic images, not only by improving the sharpness, but also by removing artifacts and noise, including overshoot.
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Affiliation(s)
- Min Seok Lee
- School of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, South Korea
| | - Chul Hee Park
- School of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, South Korea
| | - Moon Gi Kang
- School of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, South Korea.
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Deeba F, Mohammed SK, Bui FM, Wahid KA. Efficacy Evaluation of SAVE for the Diagnosis of Superficial Neoplastic Lesion. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2017; 5:1800312. [PMID: 28560120 PMCID: PMC5444410 DOI: 10.1109/jtehm.2017.2691339] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 02/22/2017] [Accepted: 03/12/2017] [Indexed: 12/23/2022]
Abstract
The detection of non-polypoid superficial neoplastic lesions using current standard of white light endoscopy surveillance and random biopsy is associated with high miss rate. The subtle changes in mucosa caused by the flat and depressed neoplasms often go undetected and do not qualify for further investigation, e.g., biopsy and resection, thus increasing the risk of cancer advancement. This paper presents a screening tool named the saliency-aided visual enhancement (SAVE) method, with an objective of highlighting abnormalities in endoscopic images to detect early lesions. SAVE is a hybrid system combining image enhancement and saliency detection. The method provides both qualitative enhancement and quantitative suspicion index for endoscopic image regions. A study to evaluate the efficacy of SAVE to localize superficial neoplastic lesion was performed. Experimental results for average overlap index >0.7 indicated that SAVE was successful to localize the lesion areas. The area under the receiver-operating characteristic curve obtained for SAVE was 94.91%. A very high sensitivity (100%) was achieved with a moderate specificity (65.45%). Visual inspection showed a comparable performance of SAVE with chromoendoscopy to highlight mucosal irregularities. This paper suggests that SAVE could be a potential screening tool that can substitute the application of burdensome chromoendoscopy technique. SAVE method, as a simple, easy-to-use, highly sensitive, and consistent red flag technology, will be useful for early detection of neoplasm in clinical applications.
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Albiol A, Corbi A, Albiol F. Automatic intensity windowing of mammographic images based on a perceptual metric. Med Phys 2017; 44:1369-1378. [PMID: 28160525 DOI: 10.1002/mp.12144] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 01/13/2017] [Accepted: 01/24/2017] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. METHODS The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at https://github.com/TheAnswerIsFortyTwo/GRAIL. RESULTS Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. CONCLUSIONS A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.
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Affiliation(s)
- Alberto Albiol
- iTeam Research Institute, Universitat Politlècnica de Valéncia, València, Spain
| | - Alberto Corbi
- Instituto de Física Corpuscular (IFIC), Consejo Superior de Investigaciones Científicas, Universitat de València, València, Spain
| | - Francisco Albiol
- Instituto de Física Corpuscular (IFIC), Consejo Superior de Investigaciones Científicas, Universitat de València, València, Spain
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Anand S. Medical Image Enhancement Using Edge Information-Based Methods. Biometrics 2017. [DOI: 10.4018/978-1-5225-0983-7.ch071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Medical image enhancement improves the quality and facilitates diagnosis. This chapter investigates three methods of medical image enhancement by exploiting useful edge information. Since edges have higher perceptual importance, the edge information based enhancement process is always of interest. But determination of edge information is not an easy job. The edge information is obtained from various approaches such as differential hyperbolic function, Haar filters and morphological functions. The effectively determined edge information is used for enhancement process. The retinal image enhancement method given in this chapter improves the visual quality of the vessels in the optic region. X-ray image enhancement method presented here is to increase the visibility of the bones. These algorithms are used to enhance the computer tomography, chest x-ray, retinal, and mammogram images. These images are obtained from standard datasets and experimented. The performance of these enhancement methods are quantitatively evaluated.
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Affiliation(s)
- S. Anand
- Mepco Schlenk Engineering College, India
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Deng H, Deng W, Sun X, Liu M, Ye C, Zhou X. Mammogram Enhancement Using Intuitionistic Fuzzy Sets. IEEE Trans Biomed Eng 2016; 64:1803-1814. [PMID: 27831857 DOI: 10.1109/tbme.2016.2624306] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets. METHODS The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel. RESULTS This algorithm can improve the contrast and visual quality of regions of interest. CONCLUSION Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods. SIGNIFICANCE This algorithm has potential for understanding and determining abnormalities.
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Deng H, Deng W, Sun X, Ye C, Zhou X. Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images. Sci Rep 2016; 6:35760. [PMID: 27786240 PMCID: PMC5082372 DOI: 10.1038/srep35760] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 10/03/2016] [Indexed: 11/17/2022] Open
Abstract
Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.
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Affiliation(s)
- He Deng
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - Wankai Deng
- Department of Head and Neck and Neurosurgery, Hubei Cancer Hospital, Wuhan 430079, China
| | - Xianping Sun
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - Chaohui Ye
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - Xin Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
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Guo F, Tang J, Peng H, Zou B. Temporal-Spatial Filtering for Enhancement of Low-Light Surveillance Video. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2016. [DOI: 10.20965/jaciii.2016.p0652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A new surveillance video enhancement method is proposed to improve the visual effect of videos captured in low-light conditions. The proposed technique, called temporal-spatial (TS) filtering, uses adaptive temporal filtering and nonlocal mean filtering to smooth the transmission map in the temporal and spatial dimensions and thus yields restored video sequences with significantly reduced noise, improved details and good spatial and temporal coherence. The main advantage of this work is that the performance of contrast enhancement, noise reduction and temporal-spatial coherence can be significantly improved using the proposed framework, which adopts a strategy that applies the same transmission map to a series of video frames. Comparative study and quantitative evaluation demonstrate that the proposed method is better than previous techniques in terms of reducing noise and improving contrast.
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Bhateja V, Misra M, Urooj S. Non-linear polynomial filters for edge enhancement of mammogram lesions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 129:125-134. [PMID: 26831271 DOI: 10.1016/j.cmpb.2016.01.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 12/02/2015] [Accepted: 01/05/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Computer aided analysis of mammograms has been employed by radiologists as a vital tool to increase the precision in the diagnosis of breast cancer. The efficiency of such an analysis is dependent on the employed mammogram enhancement approach; as its major role is to yield a visually improved image for radiologists. METHODS Non-linear Polynomial Filtering (NPF) framework has been explored previously as a robust approach for contrast improvement of mammographic images. This paper presents the extension of NPF framework for sharpening and edge enhancement of mammogram lesions. Proposed NPF serves to provide enhancement of edges and sharpness of the lesion region (region-of-interest) in mammograms, in a manner to minimize the dependencies on pre-selected thresholds. In the present work, Logarithmic Image Processing (LIP) model has been employed for the purpose of improvement in visualization of mammograms based on Human Visual System (HVS) characteristics. RESULTS The proposed NPF filtering framework yields mammograms with significant improvement in contrast, edges as well as sharpness of the lesion region. The performance of the proposed approach has been validated using state-of-art objective evaluation measures (of mammogram enhancement) like Contrast Improvement Index (CII), Peak Signal-to-Noise Ratio (PSNR), Average Signal-to-Noise Ratio (ASNR) and Combined Enhancement Measure (CEM); as well as subjective evaluation by radiologists' opinions. CONCLUSIONS The proposed NPF provides a robust solution to perform noise controlled contrast as well as edge enhancement using a single filtering model. This leads to a better visualization of the fine lesion details predictive of their severity. The applicability of single filtering methodology for carrying out denoising, contrast and edge enhancement improves the worth of the overall framework.
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Affiliation(s)
- Vikrant Bhateja
- Faculty of Electronics & Communication Engineering, Shri Ramswaroop Memorial University, Lucknow-Deva Road, UP, India; Department of Electronics and Communication Engineering, SRMGPC, Lucknow, UP, India.
| | - Mukul Misra
- Faculty of Electronics & Communication Engineering, Shri Ramswaroop Memorial University, Lucknow-Deva Road, UP, India
| | - Shabana Urooj
- Dept. of Electrical Engineering, School of Engineering, Gautam Buddha University, Greater-Noida, UP, India
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Song L, Hsu W, Xu J, van der Schaar M. Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening. IEEE J Biomed Health Inform 2016; 20:902-914. [DOI: 10.1109/jbhi.2015.2414934] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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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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Choosing the optimal spatial domain measure of enhancement for mammogram images. Int J Biomed Imaging 2014; 2014:937849. [PMID: 25177347 PMCID: PMC4142175 DOI: 10.1155/2014/937849] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 06/19/2014] [Indexed: 11/17/2022] Open
Abstract
Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative processes that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the enhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms. A variety of measures evaluating the quality of an image enhancement will be presented along with each measure's basis for analysis, namely, on image content and image attributes. We also provide guidelines for systematically choosing the proper measure of image quality for medical images.
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Swaminathan A, Ramapackiam SSK, Thiraviam T, Selvaraj J. Contourlet transform-based sharpening enhancement of retinal images and vessel extraction application. ACTA ACUST UNITED AC 2013; 58:87-96. [PMID: 23314502 DOI: 10.1515/bmt-2012-0055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2012] [Accepted: 12/10/2012] [Indexed: 11/15/2022]
Abstract
Low-contrast retinal images have to be enhanced for good visual perception to aid in retinal vessel analysis. Classical sharpening enhancement techniques such as unsharp masking (USM) improve the contrast and bring out the information along with noise. This article uses a shift-invariant anisotropic Contourlet transform (CT) to decompose the retinal image into subbands. A new nonlinear method is applied over the subbands to modify the CT coefficients, followed by inverse CT. The proposed method is compared with a nonlinear USM (NLUSM) technique and wavelet transform-based method. The objective performance is measured in terms of enhancement measure. We observed that the proposed methodology provides better result. We demonstrate that this sharpening algorithm can be used as a preprocessing step to (i) adaptive histogram equalization and (ii) retinal vessel extraction. Pratt's figure of merit was used to analyze the vessel extracted from the retinal images with their ground truth that were obtained from STARE and DRIVE databases.
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Affiliation(s)
- Anand Swaminathan
- Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
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Nercessian SC, Panetta KA, Agaian SS. Non-linear direct multi-scale image enhancement based on the luminance and contrast masking characteristics of the human visual system. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:3549-3561. [PMID: 23674451 DOI: 10.1109/tip.2013.2262287] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Image enhancement is a crucial pre-processing step for various image processing applications and vision systems. Many enhancement algorithms have been proposed based on different sets of criteria. However, a direct multi-scale image enhancement algorithm capable of independently and/or simultaneously providing adequate contrast enhancement, tonal rendition, dynamic range compression, and accurate edge preservation in a controlled manner has yet to be produced. In this paper, a multi-scale image enhancement algorithm based on a new parametric contrast measure is presented. The parametric contrast measure incorporates not only the luminance masking characteristic, but also the contrast masking characteristic of the human visual system. The formulation of the contrast measure can be adapted for any multi-resolution decomposition scheme in order to yield new human visual system-inspired multi-scale transforms. In this article, it is exemplified using the Laplacian pyramid, discrete wavelet transform, stationary wavelet transform, and dual-tree complex wavelet transform. Consequently, the proposed enhancement procedure is developed. The advantages of the proposed method include: 1) the integration of both the luminance and contrast masking phenomena; 2) the extension of non-linear mapping schemes to human visual system inspired multi-scale contrast coefficients; 3) the extension of human visual system-based image enhancement approaches to the stationary and dual-tree complex wavelet transforms, and a direct means of; 4) adjusting overall brightness; and 5) achieving dynamic range compression for image enhancement within a direct multi-scale enhancement framework. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements.
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Affiliation(s)
- Shahan C Nercessian
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA.
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Anand S, Kumari RSS, Jeeva S, Thivya T. Directionlet transform based sharpening and enhancement of mammographic X-ray images. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.02.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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