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Visualization of occipital lobe and zygomatic arch of brain region through non-linear perspective projection using DCO algorithm. Soft comput 2022. [DOI: 10.1007/s00500-022-07427-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Partheepan R, Raja Paul Perinbam J, Krishnamurthy M, Shanker NR. Visualization of Pterygomaxillary Fissure Structure and Shape in CT Image via Non-Linear Perspective Projection. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The neurologist analyses the brain images to diagnose disease via structure and shape of the part in scanned Medical images such as CT, MRI, and PET. The Medical image segmentation performs less in the regions where no or little contrast, artifacts over the different boundary regions.
The manual process of segmentation shows poor boundary differentiation due to discernibility in shape and location, intra and inter observer reliability. In this paper, we propose dyadic CAT optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non-linear
perspective Foreground and Back Ground projection. The DCO algorithm removes the artifacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm shows the region boundary for pterygomaxillary fissure, occipital lobe, vaginal process zygomatic
arch, maxilla and piriform aperture in brain image with high visibility in the regions of inadequately visible boundary and distinguishes the deformable shape. The DCO algorithm applies on 50 images and eight images with complex bone and muscle mass structure for performance evaluation. The
DCO algorithm shows the increased Structural similarity index (SSIM) with 90% accuracy.
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Affiliation(s)
- R. Partheepan
- Department of Electronics and Communication Engineering, J.N.N Institute of Engineering, Chennai 601102, Tamilnadu, India
| | - J. Raja Paul Perinbam
- Department of Electronics and Communication Engineering, KINGS Engineering College, Chennai 602117, Tamilnadu, India
| | - M. Krishnamurthy
- Department of Computer Science and Engineering, KCG College of Technology, Chennai 600115, Tamilnadu, India
| | - N. R. Shanker
- Department of Electronics and Communication Engineering, Aalim Muhammed Salegh College of Engineering, Chennai 600055, Tamilnadu, India
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Das PK, Meher S, Panda R, Abraham A. A Review of Automated Methods for the Detection of Sickle Cell Disease. IEEE Rev Biomed Eng 2019; 13:309-324. [PMID: 31107662 DOI: 10.1109/rbme.2019.2917780] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes elaborate analysis of proper disease diagnosis after accurate detection followed by a classification of irregularities, which plays a vital role in the sickle cell disease diagnosis, treatment planning, and treatment outcome evaluation. Proper segmentation of complex cell clusters makes sickle cell detection more accurate and robust. Cell morphology has a key role in the detection of the sickle cell because the shapes of the normal blood cell and sickle cell differ significantly. This review emphasizes state-of-the-art methods and recent advances in detection, segmentation, and classification of sickle cell disease. We discuss key challenges encountered during the segmentation of overlapping blood cells. Moreover, standard validation measures that have been employed to yield performance analysis of various methods are also discussed. The methodologies and experiments in this review will be useful to further research and work in this area.
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Veeramuthu A, Meenakshi S, Ashok Kumar K. A neural network based deep learning approach for efficient segmentation of brain tumor medical image data. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-169980] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- A. Veeramuthu
- Department of Information Technology, Sathyabama Institute of Science and Technology, Chennai, India
| | - S. Meenakshi
- Department of Information Technology, Jeppiaar SRR Engineering College, Padur, Chennai, India
| | - K. Ashok Kumar
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India
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Deng Z, Guo Q, Zhu Z. Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:4321645. [PMID: 30918620 PMCID: PMC6409057 DOI: 10.1155/2019/4321645] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 02/06/2019] [Indexed: 02/07/2023]
Abstract
Segmentation of liver tumors plays an important role in the choice of therapeutic strategies for liver disease and treatment monitoring. In this paper, we generalize the process of a level set with a novel algorithm of dynamic regulation to energy functional parameters. The presented method is fully automatic once the tumor has been detected. First, a 3D convolutional neural network with dense layers for classification is used to estimate current contour location relative to the tumor boundary. Second, the output 3D CNN probabilities can dynamically regulate parameters of the level set functional over the process of segmentation. Finally, for full automation, appropriate initializations and local window size are generated based on the current contour position probabilities. We demonstrate the proposed method on the dataset of MICCAI 2017 LiTS Challenge and 3DIRCADb that include low contrast and heterogeneous tumors as well as noisy images. To illustrate the strength of our method, we evaluated it against the state-of-the-art methods. Compared with the level set framework with fixed parameters, our method performed better significantly with an average DICE improvement of 0.15. We also analyzed a challenging dataset 3DIRCADb of tumors and obtained a competitive DICE of 0.85 ± 0.06 with the proposed method.
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Affiliation(s)
- Zhuofu Deng
- College of Software, Northeastern University, Shenyang 110004, China
| | - Qingzhe Guo
- College of Software, Northeastern University, Shenyang 110004, China
| | - Zhiliang Zhu
- College of Software, Northeastern University, Shenyang 110004, China
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Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation. BIOMED RESEARCH INTERNATIONAL 2018; 2018:6319879. [PMID: 30402488 PMCID: PMC6196995 DOI: 10.1155/2018/6319879] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 08/31/2018] [Accepted: 09/19/2018] [Indexed: 11/17/2022]
Abstract
Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irregular boundaries (double boundary, weak boundary), and image noise. Therefore, this paper intentionally designed an automatic global level set approach (AGLSA), which is capable of dealing with these issues for lumbar vertebrae CT image segmentation. Unlike the traditional level set methods, we firstly propose an automatically initialized level set function (AILSF) that comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM) to automatically generate a smooth initial contour which is precisely adjacent to the object boundary. Secondly, a regularized level set formulation is introduced to overcome the weak boundary leaking problem, which utilizes the region correlation of histograms inside and outside the level set contour as a global term. Ultimately, a gradient vector flow (GVF) based edge-stopping function is employed to guarantee a fast convergence rate of the level set evolution and to avoid level set function oversegmentation at the same time. Our proposed approach has been tested on 115 vertebrae CT volumes of various patients. Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise.
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Yuan Y, Chen YW, Dong C, Yu H, Zhu Z. Hybrid method combining superpixel, random walk and active contour model for fast and accurate liver segmentation. Comput Med Imaging Graph 2018; 70:119-134. [PMID: 30359946 DOI: 10.1016/j.compmedimag.2018.08.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 04/27/2018] [Accepted: 08/27/2018] [Indexed: 10/28/2022]
Abstract
Organ segmentation is an important pre-processing step in surgery planning and computer-aided diagnosis. In this paper, we propose a fast and accurate liver segmentation framework. Our proposed method combines a knowledge-based slice-by-slice Random Walk (RW) segmentation algorithm (proposed in our previous work) with a superpixel algorithm called the Contrast-enhanced Compact Watershed (CCWS) method to reduce computing time and memory costs. Compared to the commonly used Simple Linear Iterative Clustering (SLIC), we demonstrate that our CCWS is more appropriate for liver segmentation. To improve the methods accuracy, we use a modified narrow band active contour model as a refinement after the initial segmentation. The experiments showed that the superpixel-based slice-by-slice RW could segment the entire liver with improved speed, and the modified active contour model is more precise than the original Chan-Vese Model. As a result, the proposed framework is able to quickly and accurately segment the entire liver.
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Affiliation(s)
- Ye Yuan
- Software College of Northeastern University, No. 195 Chuangxin Road, Shenyang, China
| | - Yen-Wei Chen
- Graduate School of Information Science and Engineering, Ritsumeikan University, Noji-higashi 1-1-1, Kusatsu, Japan
| | - Chunhua Dong
- Department of Mathematics and Computer Science, Fort Valley State University, 1005 State University Drive, Fort Valley, United States
| | - Hai Yu
- Software College of Northeastern University, No. 195 Chuangxin Road, Shenyang, China
| | - Zhiliang Zhu
- Software College of Northeastern University, No. 195 Chuangxin Road, Shenyang, China.
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Liu P, Qin J, Duan B, Wang Q, Tan X, Zhao B, Jonnathan PL, Chui CK, Heng PA. Overlapping radiofrequency ablation planning and robot-assisted needle insertion for large liver tumors. Int J Med Robot 2018; 15:e1952. [PMID: 30117266 DOI: 10.1002/rcs.1952] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 08/02/2018] [Accepted: 08/06/2018] [Indexed: 12/23/2022]
Abstract
BACKGROUND We aimed at facilitating percutaneous radiofrequency ablation (RFA) for large tumors with accurate overlapping ablation planning and robot-assisted needle insertion from a single incision port (SIP). METHODS We developed a personalized and quantitative RFA planning method to obtain multiple needle overlapping ablation planning through a single incision. A robot with a remote center of motion mechanism was designed to perform needle insertions through a SIP according to the planning. RESULTS Numerical and visual evaluation showed that the planning could yield nearly full coverage over large tumors and greatly reduce both ablation times and the ablations of normal tissues. Ex vivo and in vivo experiments showed that our robot-assisted needle insertion system was capable of conducting the RFA procedure in large liver tumors from a SIP. CONCLUSIONS Robot-assisted RFA needle insertions from a SIP makes RFA treatment for large tumors more minimally invasive, predictable, and repeatable.
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Affiliation(s)
- Ping Liu
- Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, China
| | - Jing Qin
- Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong
| | - Bin Duan
- Department of Mechanical Engineering, National University of Singapore, Singapore
| | - Qiong Wang
- Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
| | - Xiaoyu Tan
- Department of Mechanical Engineering, National University of Singapore, Singapore
| | - Baoliang Zhao
- Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
| | | | - Chee-Kong Chui
- Department of Mechanical Engineering, National University of Singapore, Singapore
| | - Pheng-Ann Heng
- Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
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Kharma N, Mazhurin A, Saigol K, Sabahi F. Adaptable image segmentation via simple pixel classification. Comput Intell 2018. [DOI: 10.1111/coin.12173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Nawwaf Kharma
- Department of Electrical and Computer Engineering; Concordia University; Montréal QC Canada
| | | | - Kamil Saigol
- Institute for Robotics and Intelligent Machines; Georgia Institute of Technology; Atlanta GA USA
| | - Farzad Sabahi
- Department of Electrical and Computer Engineering; Concordia University; Montréal QC Canada
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Dora L, Agrawal S, Panda R, Abraham A. State-of-the-Art Methods for Brain Tissue Segmentation: A Review. IEEE Rev Biomed Eng 2017. [PMID: 28622675 DOI: 10.1109/rbme.2017.2715350] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field.
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Tiong TJ, Chandesa T, Yap YH. Comparison of sonochemiluminescence images using image analysis techniques and identification of acoustic pressure fields via simulation. ULTRASONICS SONOCHEMISTRY 2017; 36:78-87. [PMID: 28069242 DOI: 10.1016/j.ultsonch.2016.11.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 11/02/2016] [Accepted: 11/02/2016] [Indexed: 06/06/2023]
Abstract
One common method to determine the existence of cavitational activity in power ultrasonics systems is by capturing images of sonoluminescence (SL) or sonochemiluminescence (SCL) in a dark environment. Conventionally, the light emitted from SL or SCL was detected based on the number of photons. Though this method is effective, it could not identify the sonochemical zones of an ultrasonic systems. SL/SCL images, on the other hand, enable identification of 'active' sonochemical zones. However, these images often provide just qualitative data as the harvesting of light intensity data from the images is tedious and require high resolution images. In this work, we propose a new image analysis technique using pseudo-colouring images to quantify the SCL zones based on the intensities of the SCL images and followed by comparison of the active SCL zones with COMSOL simulated acoustic pressure zones.
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Affiliation(s)
- T Joyce Tiong
- Department of Chemical and Environmental Engineering, University of Nottingham Malaysia Campus, 43500 Semenyih, Selangor, Malaysia.
| | - Tissa Chandesa
- Graduate School, University of Nottingham Malaysia Campus, 43500 Semenyih, Selangor, Malaysia
| | - Yeow Hong Yap
- Department of Chemical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, 43000 Kajang, Selangor, Malaysia
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