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Soomro S, Niaz A, Soomro TA, Kim J, Manzoor A, Choi KN. Selective image segmentation driven by region, edge and saliency functions. PLoS One 2023; 18:e0294789. [PMID: 38100430 PMCID: PMC10723724 DOI: 10.1371/journal.pone.0294789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/07/2023] [Indexed: 12/17/2023] Open
Abstract
Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.
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Affiliation(s)
- Shafiullah Soomro
- Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
- Department of Computer Science and Media Technology, Linnaeus University, Vaxjo, Sweden
| | - Asim Niaz
- Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
| | | | - Jin Kim
- SecuLayer Inc., Seoul, South Korea
| | - Adnan Manzoor
- Department of Artificial Intelligence, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah, Sindh, Pakistan
| | - Kwang Nam Choi
- Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
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Hosten N, Bülow R, Völzke H, Domin M, Schmidt CO, Teumer A, Ittermann T, Nauck M, Felix S, Dörr M, Markus MRP, Völker U, Daboul A, Schwahn C, Holtfreter B, Mundt T, Krey KF, Kindler S, Mksoud M, Samietz S, Biffar R, Hoffmann W, Kocher T, Chenot JF, Stahl A, Tost F, Friedrich N, Zylla S, Hannemann A, Lotze M, Kühn JP, Hegenscheid K, Rosenberg C, Wassilew G, Frenzel S, Wittfeld K, Grabe HJ, Kromrey ML. SHIP-MR and Radiology: 12 Years of Whole-Body Magnetic Resonance Imaging in a Single Center. Healthcare (Basel) 2021; 10:33. [PMID: 35052197 PMCID: PMC8775435 DOI: 10.3390/healthcare10010033] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/17/2021] [Accepted: 12/20/2021] [Indexed: 12/16/2022] Open
Abstract
The Study of Health in Pomerania (SHIP), a population-based study from a rural state in northeastern Germany with a relatively poor life expectancy, supplemented its comprehensive examination program in 2008 with whole-body MR imaging at 1.5 T (SHIP-MR). We reviewed more than 100 publications that used the SHIP-MR data and analyzed which sequences already produced fruitful scientific outputs and which manuscripts have been referenced frequently. Upon reviewing the publications about imaging sequences, those that used T1-weighted structured imaging of the brain and a gradient-echo sequence for R2* mapping obtained the highest scientific output; regarding specific body parts examined, most scientific publications focused on MR sequences involving the brain and the (upper) abdomen. We conclude that population-based MR imaging in cohort studies should define more precise goals when allocating imaging time. In addition, quality control measures might include recording the number and impact of published work, preferably on a bi-annual basis and starting 2 years after initiation of the study. Structured teaching courses may enhance the desired output in areas that appear underrepresented.
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Affiliation(s)
- Norbert Hosten
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany; (N.H.); (R.B.); (M.D.); (K.H.); (C.R.)
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany; (N.H.); (R.B.); (M.D.); (K.H.); (C.R.)
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; (H.V.); (C.O.S.); (A.T.); (T.I.); (W.H.); (J.-F.C.)
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
| | - Martin Domin
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany; (N.H.); (R.B.); (M.D.); (K.H.); (C.R.)
| | - Carsten Oliver Schmidt
- Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; (H.V.); (C.O.S.); (A.T.); (T.I.); (W.H.); (J.-F.C.)
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; (H.V.); (C.O.S.); (A.T.); (T.I.); (W.H.); (J.-F.C.)
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
| | - Till Ittermann
- Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; (H.V.); (C.O.S.); (A.T.); (T.I.); (W.H.); (J.-F.C.)
| | - Matthias Nauck
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Stephan Felix
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Department of Internal Medicine B, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Marcus Dörr
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Department of Internal Medicine B, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Marcello Ricardo Paulista Markus
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Department of Internal Medicine B, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Uwe Völker
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Amro Daboul
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, 17475 Greifswald, Germany; (A.D.); (C.S.); (T.M.); (S.S.); (R.B.)
| | - Christian Schwahn
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, 17475 Greifswald, Germany; (A.D.); (C.S.); (T.M.); (S.S.); (R.B.)
| | - Birte Holtfreter
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, 17475 Greifswald, Germany; (B.H.); (T.K.)
| | - Torsten Mundt
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, 17475 Greifswald, Germany; (A.D.); (C.S.); (T.M.); (S.S.); (R.B.)
| | - Karl-Friedrich Krey
- Department of Orthodontics, University Medicine Greifswald, 17475 Greifswald, Germany;
| | - Stefan Kindler
- Department of Oral and Maxillofacial Surgery/Plastic Surgery, University Medicine Greifswald, 17475 Greifswald, Germany; (S.K.); (M.M.)
| | - Maria Mksoud
- Department of Oral and Maxillofacial Surgery/Plastic Surgery, University Medicine Greifswald, 17475 Greifswald, Germany; (S.K.); (M.M.)
| | - Stefanie Samietz
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, 17475 Greifswald, Germany; (A.D.); (C.S.); (T.M.); (S.S.); (R.B.)
| | - Reiner Biffar
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, 17475 Greifswald, Germany; (A.D.); (C.S.); (T.M.); (S.S.); (R.B.)
| | - Wolfgang Hoffmann
- Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; (H.V.); (C.O.S.); (A.T.); (T.I.); (W.H.); (J.-F.C.)
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- German Centre for Neurodegenerative Diseases (DZNE), Partner Site Rostock/Greifswald, 17489 Greifswald, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, 17475 Greifswald, Germany; (B.H.); (T.K.)
| | - Jean-Francois Chenot
- Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; (H.V.); (C.O.S.); (A.T.); (T.I.); (W.H.); (J.-F.C.)
| | - Andreas Stahl
- Clinic of Ophthalmology, University Medicine Greifswald, 17475 Greifswald, Germany; (A.S.); (F.T.)
| | - Frank Tost
- Clinic of Ophthalmology, University Medicine Greifswald, 17475 Greifswald, Germany; (A.S.); (F.T.)
| | - Nele Friedrich
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Stephanie Zylla
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Anke Hannemann
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, 10785 Berlin, Germany; (M.N.); (S.F.); (M.D.); (M.R.P.M.); (U.V.); (N.F.); (S.Z.); (A.H.)
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Martin Lotze
- Functional Imaging Unit, Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany;
| | - Jens-Peter Kühn
- Institute and Policlinic of Diagnostic and Interventional Radiology, Medical University, Carl-Gustav Carus, 01307 Dresden, Germany;
| | - Katrin Hegenscheid
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany; (N.H.); (R.B.); (M.D.); (K.H.); (C.R.)
| | - Christian Rosenberg
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany; (N.H.); (R.B.); (M.D.); (K.H.); (C.R.)
| | - Georgi Wassilew
- Clinic of Orthopedics, University Medicine Greifswald, 17475 Greifswald, Germany;
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17475 Greifswald, Germany; (S.F.); (K.W.); (H.J.G.)
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17475 Greifswald, Germany; (S.F.); (K.W.); (H.J.G.)
- German Center of Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Site Greifswald, 17489 Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17475 Greifswald, Germany; (S.F.); (K.W.); (H.J.G.)
- German Center of Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Site Greifswald, 17489 Greifswald, Germany
| | - Marie-Luise Kromrey
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany; (N.H.); (R.B.); (M.D.); (K.H.); (C.R.)
- Correspondence:
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Regression Based Iterative Illumination Compensation Method for Multi-Focal Whole Slide Imaging System. SENSORS 2021; 21:s21217085. [PMID: 34770394 PMCID: PMC8587722 DOI: 10.3390/s21217085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 10/22/2021] [Accepted: 10/22/2021] [Indexed: 12/04/2022]
Abstract
Image quality, resolution and scanning time are critical in digital pathology. In order to create a high-resolution digital image, the scanner systems execute stitching algorithms to the digitized images. Due to the heterogeneity of the tissue sample, complex optical path, non-acceptable sample quality or rapid stage movement, the intensities on pictures can be uneven. The evincible and visible intensity distortions can have negative effect on diagnosis and quantitative analysis. Utilizing the common areas of the neighboring field-of-views, we can estimate compensations to eliminate the inhomogeneities. We implemented and validated five different approaches for compensating output images created with an area scanner system. The proposed methods are based on traditional methods such as adaptive histogram matching, regression-based corrections and state-of-the art methods like the background and shading correction (BaSiC) method. The proposed compensation methods are suitable for both brightfield and fluorescent images, and robust enough against dust, bubbles, and optical aberrations. The proposed methods are able to correct not only the fixed-pattern artefacts but the stochastic uneven illumination along the neighboring or above field-of-views utilizing iterative approaches and multi-focal compensations.
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Arrieta C, Sing-Long CA, Mura J, Irarrazaval P, Andia ME, Uribe S, Tejos C. Level set segmentation with shape prior knowledge using intrinsic rotation, translation and scaling alignment. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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5
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Use of Biomarkers and Imaging for Early Detection of Pancreatic Cancer. Cancers (Basel) 2020; 12:cancers12071965. [PMID: 32707720 PMCID: PMC7409286 DOI: 10.3390/cancers12071965] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 07/04/2020] [Accepted: 07/09/2020] [Indexed: 12/19/2022] Open
Abstract
Pancreatic cancer remains one of the deadliest cancers worldwide, and it is typically diagnosed late, with a poor prognosis. Early detection is the most important underlying factor for improving the prognosis of pancreatic cancer patients. One of the most effective strategies for detecting cancers at an early stage is screening of the general population. However, because of the low incidence of pancreatic cancer in the general population, the stratification of subjects who need to undergo further examinations by invasive and expensive modalities is important. Therefore, minimally invasive modalities involving biomarkers and imaging techniques that would facilitate the early detection of pancreatic cancer are highly needed. Multiple types of new blood biomarkers have recently been developed, including unique post-translational modifications of circulating proteins, circulating exosomes, microRNAs, and circulating tumor DNA. We previously reported that circulating apolipoprotein A2 undergoes unique processing in the bloodstream of patients with pancreatic cancer and its precancerous lesions. Additionally, we recently demonstrated a new method for measuring pancreatic proton density in the fat fraction using a fat–water magnetic resonance imaging technique that reflects pancreatic steatosis. In this review, we describe recent developments in potential biomarkers and imaging modalities for the early detection and risk stratification of pancreatic cancer, and we discuss current strategies for implementing screening programs for pancreatic cancer.
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A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:7595174. [PMID: 32565883 PMCID: PMC7285411 DOI: 10.1155/2020/7595174] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 05/08/2020] [Indexed: 12/16/2022]
Abstract
Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.
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Pishghadam M, Kazemi K, Nekooei S, Seilanian-Toosi F, Hoseini-Ghahfarokhi M, Zabizadeh M, Fatemi A. A new approach to automatic fetal brain extraction from MRI using a variational level set method. Med Phys 2019; 46:4983-4991. [PMID: 31419312 DOI: 10.1002/mp.13766] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 07/30/2019] [Accepted: 07/31/2019] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Appropriate images extracted from the MRI of mothers' wombs can be of great help in the medical diagnosis of fetal abnormalities. As maternal tissue may appear in such images, affecting visualization of myelination of the fetal brain, it is not possible to use methods routinely used for extraction of adult brains for fetal brains. The aim of the present study was to use a variational level set approach to extract fetal brain from T2-weighted MR images of the womb. METHODS Coronal T2-weighted images were acquired using fast MRI protocols (to avoid artifacts). The database includes 105 MR images from eight subjects. After correcting the inhomogeneity of the images, the fetal eyes were located, and from that information, the location of the fetus brain was automatically determined. Then, the variational level set was used for fetus brain extraction. The results were analyzed by a clinical specialist (radiologist) and the similarity (Dice and Jaccard coefficients), sensitivity and specificity were calculated. RESULTS AND CONCLUSIONS The means of the statistical analysis for the Dice and Jaccard coefficients, sensitivity and specificity, were 99.56%, 96.89%, 95.71%, and 97.96%, respectively. Thus, extraction of fetal brain from MR images was confirmed, both statistically and visually through cross-validation.
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Affiliation(s)
- Morteza Pishghadam
- Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Sirous Nekooei
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farrokh Seilanian-Toosi
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mojtaba Hoseini-Ghahfarokhi
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mansour Zabizadeh
- Department of Radiology and Nuclear Medicine, School of Para Medical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ali Fatemi
- Department of Radiation Oncology and Radiology, University of Mississippi Medical Center (UMMC), Jackson, MS, USA
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George MM, Kalaivani S. Retrospective correction of intensity inhomogeneity with sparsity constraints in transform-domain: Application to brain MRI. Magn Reson Imaging 2019; 61:207-223. [PMID: 31009687 DOI: 10.1016/j.mri.2019.04.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/05/2019] [Accepted: 04/18/2019] [Indexed: 11/27/2022]
Abstract
An effective retrospective correction method is introduced in this paper for intensity inhomogeneity which is an inherent artifact in MR images. Intensity inhomogeneity problem is formulated as the decomposition of acquired image into true image and bias field which are expected to have sparse approximation in suitable transform domains based on their known properties. Piecewise constant nature of the true image lends itself to have a sparse approximation in framelet domain. While spatially smooth property of the bias field supports a sparse representation in Fourier domain. The algorithm attains optimal results by seeking the sparsest solutions for the unknown variables in the search space through L1 norm minimization. The objective function associated with defined problem is convex and is efficiently solved by the linearized alternating direction method. Thus, the method estimates the optimal true image and bias field simultaneously in an L1 norm minimization framework by promoting sparsity of the solutions in suitable transform domains. Furthermore, the methodology doesn't require any preprocessing, any predefined specifications or parametric models that are critically controlled by user-defined parameters. The qualitative and quantitative validation of the proposed methodology in simulated and real human brain MR images demonstrates the efficacy and superiority in performance compared to some of the distinguished algorithms for intensity inhomogeneity correction.
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Affiliation(s)
- Maryjo M George
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India.
| | - S Kalaivani
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India.
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Subudhi BN, Veerakumar T, Esakkirajan S, Ghosh A. Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction From Magnetic Resonance Images. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:1800309. [PMID: 31281739 PMCID: PMC6537928 DOI: 10.1109/jtehm.2019.2898870] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/23/2018] [Accepted: 02/04/2019] [Indexed: 11/16/2022]
Abstract
In this paper, a novel context-dependent fuzzy set associated statistical model-based intensity inhomogeneity correction technique for magnetic resonance image (MRI) is proposed. The observed MRI is considered to be affected by intensity inhomogeneity and it is assumed to be a multiplicative quantity. In the proposed scheme the intensity inhomogeneity correction and MRI segmentation is considered as a combined task. The maximum a posteriori probability (MAP) estimation principle is explored to solve this problem. A fuzzy set associated Gibbs’ Markov random field (MRF) is considered to model the spatio-contextual information of an MRI. It is observed that the MAP estimate of the MRF model does not yield good results with any local searching strategy, as it gets trapped to local optimum. Hence, we have exploited the advantage of variable neighborhood searching (VNS)-based iterative global convergence criterion for MRF-MAP estimation. The effectiveness of the proposed scheme is established by testing it on different MRIs. Three performance evaluation measures are considered to evaluate the performance of the proposed scheme against existing state-of-the-art techniques. The simulation results establish the effectiveness of the proposed technique.
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Affiliation(s)
- Badri Narayan Subudhi
- 1Department of Electrical EngineeringIndian Institute of Technology JammuJammu181221India
| | - T Veerakumar
- 2Department of Electronics and Communication EngineeringNational Institute of TechnologyGoa403401India
| | - S Esakkirajan
- 3Department of Instrumentation and Control EngineeringPSG College of TechnologyCoimbatore641004India
| | - Ashish Ghosh
- 4Machine Intelligence UnitIndian Statistical InstituteKolkata700105India
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Yang Y, Tian D, Jia W, Shu X, Wu B. Split Bregman method based level set formulations for segmentation and correction with application to MR images and color images. Magn Reson Imaging 2019; 57:50-67. [DOI: 10.1016/j.mri.2018.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Revised: 09/28/2018] [Accepted: 10/06/2018] [Indexed: 10/28/2022]
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11
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Ivanovska T, Jentschke TG, Daboul A, Hegenscheid K, Völzke H, Wörgötter F. A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts. Int J Comput Assist Radiol Surg 2019; 14:1627-1633. [PMID: 30838510 DOI: 10.1007/s11548-019-01928-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 02/27/2019] [Indexed: 11/30/2022]
Abstract
PURPOSE The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities. METHODS We present a pipeline for breast density estimation, which consists of intensity inhomogeneity correction, breast volume segmentation, nipple extraction, and fibroglandular tissue segmentation. For the segmentation steps, a well-known deep learning architecture is employed. RESULTS The average Dice coefficient for the breast parenchyma is [Formula: see text], which outperforms the classical state-of-the-art approach by a margin of [Formula: see text]. CONCLUSION The proposed solution is accurate and highly efficient and has potential to be applied for big epidemiological data with thousands of participants.
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Affiliation(s)
- Tatyana Ivanovska
- Department of Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz, 1, 37077, Göttingen, Germany.
| | - Thomas G Jentschke
- Department of Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz, 1, 37077, Göttingen, Germany
| | - Amro Daboul
- Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Fleischmannstr. 42-44, 17475, Greifswald, Germany
| | | | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str. 48, 17489, Greifswald, Germany
| | - Florentin Wörgötter
- Department of Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz, 1, 37077, Göttingen, Germany
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12
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Liu H, Liu S, Guo D, Zheng Y, Tang P, Dan G. Original intensity preserved inhomogeneity correction and segmentation for liver magnetic resonance imaging. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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13
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Yang Y, Tian D, Wu B. A fast and reliable noise-resistant medical image segmentation and bias field correction model. Magn Reson Imaging 2018; 54:15-31. [PMID: 30075185 DOI: 10.1016/j.mri.2018.06.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/21/2018] [Accepted: 06/21/2018] [Indexed: 11/30/2022]
Abstract
In recent years, with the rapid development of modern medical image technology, the medical image processing technology is becoming more important. In particular, the accurate segmentation of medical images is significant for doctors to diagnose and analyze the etiology. However, the false contours appearing in medical images due to fuzzy image boundary, intensity inhomogeneity and random noise, may lead to the inaccurate segmentation results. In this paper, an improved active contour model based on global image information is proposed, which can accurately segment images disturbed by intensity inhomogeneities and serious noise. We give the two-phase energy functional and multi-phase energy functional of our model, and apply it to segment magnetic resonance (MR) images, ultrasound (US) images and synthetic images. Experimental results and comparisons with other models have shown that our model has the advantages of higher accuracy, higher efficiency and robustness in dealing with the intensity inhomogeneity and serious noise in image segmentation.
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Affiliation(s)
- Yunyun Yang
- School of Science, Harbin Institute of Technology, Shenzhen 518055, China.
| | - Dongcai Tian
- School of Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Boying Wu
- Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China
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14
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Leger S, Löck S, Hietschold V, Haase R, Böhme HJ, Abolmaali N. Physical correction model for automatic correction of intensity non-uniformity in magnetic resonance imaging. Phys Imaging Radiat Oncol 2017. [DOI: 10.1016/j.phro.2017.11.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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15
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Xue W, Vegunta S, Zwart CM, Aguilar MI, Patel AC, Hoxworth JM, Demaerschalk BM, Mitchell JR. Retrospective Validation of a Computer-Assisted Quantification Model of Intracerebral Hemorrhage Volume on Accuracy, Precision, and Acquisition Time, Compared with Standard ABC/2 Manual Volume Calculation. AJNR Am J Neuroradiol 2017; 38:1536-1542. [PMID: 28596188 DOI: 10.3174/ajnr.a5256] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 04/09/2017] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND PURPOSE Intracerebral hemorrhage accounts for 6.5%-19.6% of all acute strokes. Initial intracerebral hemorrhage volume and expansion are both independent predictors of clinical outcomes and mortality. Therefore, a rapid, unbiased, and precise measurement of intracerebral hemorrhage volume is a key component of clinical management. The most commonly used method, ABC/2, results in overestimation. We developed an interactive segmentation program, SegTool, using a novel graphic processing unit, level set algorithm. Until now, the speed, bias, and precision of SegTool had not been validated. MATERIALS AND METHODS In a single stroke academic center, 2 vascular neurologists and 2 neuroradiologists independently performed a test-retest experiment that involved repeat measurements of static, unchanging intracerebral hemorrhage volumes on CT from 76 intracerebral hemorrhage cases. Measurements were made with SegTool and ABC/2. True intracerebral hemorrhage volumes were estimated from a consensus of repeat manual tracings by 2 operators. These data allowed us to estimate measurement bias, precision, and speed. RESULTS The measurements with SegTool were not significantly different from the true intracerebral hemorrhage volumes, while ABC/2 overestimated volume by 45%. The interrater measurement variability with SegTool was 50% less than that with ABC/2. The average measurement times for ABC/2 and SegTool were 35.7 and 44.6 seconds, respectively. CONCLUSIONS SegTool appears to have attributes superior to ABC/2 in terms of accuracy and interrater reliability with a 9-second delay in measurement time (on average); hence, it could be useful in clinical trials and practice.
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Affiliation(s)
- W Xue
- From the Department of Biomedical Informatics (W.X., J.R.M.), Arizona State University, Scottsdale, Arizona
| | - S Vegunta
- Moran Eye Center (S.V.), University of Utah, Salt Lake City, Utah
| | - C M Zwart
- Departments of Radiology (C.M.Z., A.C.P., J.M.H.)
| | | | - A C Patel
- Departments of Radiology (C.M.Z., A.C.P., J.M.H.)
| | - J M Hoxworth
- Departments of Radiology (C.M.Z., A.C.P., J.M.H.)
| | | | - J R Mitchell
- From the Department of Biomedical Informatics (W.X., J.R.M.), Arizona State University, Scottsdale, Arizona.,Research (J.R.M.), Mayo Clinic, Scottsdale, Arizona
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16
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Akram F, Garcia MA, Puig D. Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity. PLoS One 2017; 12:e0174813. [PMID: 28376124 PMCID: PMC5380353 DOI: 10.1371/journal.pone.0174813] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 03/15/2017] [Indexed: 11/19/2022] Open
Abstract
This paper presents a region-based active contour method for the segmentation of intensity inhomogeneous images using an energy functional based on local and global fitted images. A square image fitted model is defined by using both local and global fitted differences. Moreover, local and global signed pressure force functions are introduced in the solution of the energy functional to stabilize the gradient descent flow. In the final gradient descent solution, the local fitted term helps extract regions with intensity inhomogeneity, whereas the global fitted term targets homogeneous regions. A Gaussian kernel is applied to regularize the contour at each step, which not only smoothes it but also avoids the computationally expensive re-initialization. Intensity inhomogeneous images contain undesired smooth intensity variations (bias field) that alter the results of intensity-based segmentation methods. The bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. In this paper, a two-phase model is first derived and then extended to a four-phase model to segment brain magnetic resonance (MR) images into the desired regions of interest. Experimental results with both synthetic and real brain MR images are used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation technique in practical terms.
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Affiliation(s)
- Farhan Akram
- Department of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, Spain
| | - Miguel Angel Garcia
- Department of Electronic and Communications Technology, Autonomous University of Madrid, Madrid, Spain
| | - Domenec Puig
- Department of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, Spain
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