Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec10: Medical Image Segmentation as an Energy Minimization ProblemUlaş Bağcı
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method
Energyfunctional
– Data and Smoothness terms
• GraphCut – Min cut
– Max Flow
• ApplicationsinRadiologyImages
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec10: Medical Image Segmentation as an Energy Minimization ProblemUlaş Bağcı
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method
Energyfunctional
– Data and Smoothness terms
• GraphCut – Min cut
– Max Flow
• ApplicationsinRadiologyImages
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec1: Medical Image Computing - Introduction Ulaş Bağcı
2017 Spring, UCF Medical Image Computing Course
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...)
• Introduction to Medical Image Computing and Toolkits
• Image Filtering, Enhancement, Noise Reduction, and
Signal Processing
• MedicalImageRegistration
• MedicalImageSegmentation
• MedicalImageVisualization
• Machine Learning in Medical Imaging
• Shape Modeling/Analysis of Medical Images
Deep Learning in Radiology
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec9: Medical Image Segmentation (III) (Fuzzy Connected Image Segmentation)Ulaş Bağcı
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Fuzzy Connectivity (FC) – Affinity functions
• Absolute FC
• Relative FC (and Iterative Relative FC)
• Successful example applications of FC in medical imaging
• Segmentation of Airway and Airway Walls using RFC based method
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec5: Pre-Processing Medical Images (III) (MRI Intensity Standardization)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec1: Medical Image Computing - Introduction Ulaş Bağcı
2017 Spring, UCF Medical Image Computing Course
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...)
• Introduction to Medical Image Computing and Toolkits
• Image Filtering, Enhancement, Noise Reduction, and
Signal Processing
• MedicalImageRegistration
• MedicalImageSegmentation
• MedicalImageVisualization
• Machine Learning in Medical Imaging
• Shape Modeling/Analysis of Medical Images
Deep Learning in Radiology
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec9: Medical Image Segmentation (III) (Fuzzy Connected Image Segmentation)Ulaş Bağcı
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Fuzzy Connectivity (FC) – Affinity functions
• Absolute FC
• Relative FC (and Iterative Relative FC)
• Successful example applications of FC in medical imaging
• Segmentation of Airway and Airway Walls using RFC based method
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec5: Pre-Processing Medical Images (III) (MRI Intensity Standardization)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec6: Pre-Processing for Nuclear Medicine ImagesUlaş Bağcı
2017 Spring, UCF Medical Image Computing
1. The use of PET/SPECT, PET/CT and MRI/PET Images
2. What to measure from Nuclear Medicine Images?
3. Denoising Nuclear MedicineI mages
4. PartialVolumeCorrection
ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec14: Evaluation Framework for Medical Image SegmentationUlaş Bağcı
How to evaluate accuracy of image segmentation?
– Gold standard ~ surrogate of truths
– Qualitative • Visual
• Inter-andintra-observeragreementrates – Quantitative
• Volumetricmeasurements(regression) • Regionoverlaps
• Shapebasedmeasurements
• Theoreticalcomparisons
• STAPLE,Uncertaintyguidance,andevaluationw/otruths
Clustering – K-means – FCM (fuzzyc-means) – SMC (simple membership based clustering) – AP(affinity propagation) – FLAB(fuzzy locally adaptive Bayesian) – Spectral Clustering Methods ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec13: Clustering Based Medical Image Segmentation MethodsUlaş Bağcı
Clustering – K-means
– FCM (fuzzyc-means)
– SMC (simple membership based clustering) – AP(affinity propagation)
– FLAB(fuzzy locally adaptive Bayesian)
– Spectral Clustering Methods
ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec2: Digital Images and Medical Imaging ModalitiesUlaş Bağcı
2017 Spring, UCF Medical Image Computing Course
X-ray?
• Ultrasound?
• ComputedTomography(CT)?
• MagneticResonanceImaging(MRI)?
• PositronEmissionTomography(PET)? • DiffusionWeightedImaging(DWI)?
• DiffusionTensorImaging(DTI)?
• MagneticParticleImaging(MPI)?
• OpticalCoherenceTomography(OCT)?
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...) • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
A Novel Efficient Medical Image Segmentation Methodologyaciijournal
Image segmentation plays a crucial role in many medical applications. The threshold based medical image
segmentation approach is the most common and effective method for medical image segmentation, but it
has some shortcomings such as high complexity, poor real time capability and premature convergence, etc.
To address above issues, an improved evolution strategies is proposed to use for medical image
segmentation, there are 2 populations concurrently during evolution, one focuses on local search in order
to search solutions near optimal solution, and the other population that implemented based on chaotic
theory focuses on global search so as to keep the variety of individuals and jump out from the local
maximum to overcome the problem of premature convergence. The encoding scheme, fitness function, and
evolution operators are also designed. The experimental results validated the effectiveness and efficiency of
the proposed approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Texture Analysis As An Aid In CAD And Computational Logiciosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Nutraceutical market, scope and growth: Herbal drug technology
Lec7: Medical Image Segmentation (I) (Radiology Applications of Segmentation, and Thresholding)
1. MEDICAL IMAGE COMPUTING (CAP 5937)
LECTURE 7: Medical Image Segmentation (I)
(Radiology Applications of Segmentation, and Thresholding)
Dr. Ulas Bagci
HEC 221, Center for Research in Computer
Vision (CRCV), University of Central Florida
(UCF), Orlando, FL 32814.
bagci@ucf.edu or bagci@crcv.ucf.edu
1SPRING 2017
2. Outline
• Introduction to Medical Image Segmentation, type of
segmentation methods, and definitions
– Recognition & Delineation
• Simplest Segmentation Method(s): Thresholding
– Otsu Thresholding
– Parametric Method
– PET Image Thresholding Methods
• ITM (Iterative Thresholding Method)
2
3. Motivation for Image Segmentation
In the last 20 years the computer vision and medical imaging
communities have produced a number of useful algorithms for
localizing object boundaries in images.
3
4. Motivation for Image Segmentation
• Content based image retrieval
• Machine Vision
• Medical Imaging applications (tumor delineation,..)
• Object detection (face detection,…)
• 3D Reconstruction
• Object/Motion Tracking
• Object-based measurements such as size and shape
• Object recognition (face recognition,…)
• Fingerprint recognition,
• Video surveillance
• …
4
5. Segmentation Tools in RadiologyApplications
• 3D views to visualize structural information and spatial
anatomic relationships is a difficult task, which is usually
carried out in the clinician’s mind.
5
6. Segmentation Tools in RadiologyApplications
• 3D views to visualize structural information and spatial
anatomic relationships is a difficult task, which is usually
carried out in the clinician’s mind.
• Image-processing tools provide the surgeon with interactively
displayed 3D visual information.
6
8. • Determination of the volumes of abdominal solid organs and focal lesions
has great potential importance (liver, spleen, …).
• Monitoring the response to therapy and the progression of neoplastic
disease and preoperative examination of living liver donors are the most
common clinical applications of volume determination.
8
Segmentation Tools in RadiologyApplications
(credit: Farraher, et al.
Radiology 2005)
9. Segmentation Tools in RadiologyApplications
• Gross Tumor Volume in CT/MRI
• Metabolic Tumor Volume in PET/SPECT/
– Surgery/Therapy Planning
• Planning Tumor Volume (PTV)
– Tumor characterization
• Texture Extraction requires
segmentation to be done
• Shape analysis
9
10. Segmentation Tools in RadiologyApplications
• There is a strong interest in automatic and reproducible
techniques for detection and quantification of vascular
disease
• A first step toward an effective vessel analysis tool is
segmentation of the vasculature.
10
axial coronal sagittal
Credit: Manniesing, et al,
Radiology 2008
MIP: maximum intensity
Projection image of cerebral vessels (in CTA)
11. Segmentation Tools in RadiologyApplications
• MR volumetry of the
hippocampus can help
distinguish patients with
AD (Alzheimer’s
Disease) from elderly
controls with a high
degree of accuracy
(80%–90%).
11
12. Segmentation Tools in RadiologyApplications
• MR volumetry of the
hippocampus can help
distinguish patients with
AD (Alzheimer’s
Disease) from elderly
controls with a high
degree of accuracy
(80%–90%).
12
hippocampus
amygdala
Credit: Colliot et al, Radiology 2008.
14. Image Segmentation
Definition: Partitioning a picture/image into distinctive subsets is
called segmentation.
14
Segmentation of an image entails the division or
separation of the image
into regions of similar attribute.
15. Image Segmentation
Definition: Partitioning a picture/image into distinctive subsets is
called segmentation.
15
Segmentation of an image entails the division or
separation of the image
into regions of similar attribute.
The most basic attributes:
-intensity
-edges
-texture
-other features…
16. Image Segmentation
Definition: Partitioning a picture/image into distinctive subsets is
called segmentation.
16
Purpose: To extractobject information
and representthis as a
hard/fuzzygeometric
structure.
Recognition: Determiningthe object’s
whereaboutsin the scene.
(humans> computer)
Delineation: Determining the object’s
spatial extent and
compositionin the scene.
(computers > humans)
19. Approaches to Recognition
19
• Model-based
• Knowledge-based - Non-interactive
• Atlas-based
• Human-assisted - Interactive
- They all originate from human knowledge.
- Their relative efficacy is unknown.
20. Approaches to Delineations
20
pI (purely image-based) approaches
• Rely mostlyon informationavailable in the given image
only.
• Recognition: manual
21. Approaches to Delineations
21
pI (purely image-based) approaches
• Rely mostlyon informationavailable in the given image
only.
• Recognition: manual
SM (shape model-based) approaches
• Employ models to codify object family shape info.
• Recognition: model-based/manual
22. Approaches to Delineations
22
pI (purely image-based) approaches
• Rely mostlyon informationavailable in the given image
only.
• Recognition: manual
SM (shape model-based) approaches
• Employ models to codify object family shape info.
• Recognition: model-based/manual
Hybrid approaches
• Combine among pI and SM approaches.
• Recognition: model-based, automatic.
25. Classification of Methods
25
Boundary-based (BpI):
• optimum boundary
• active boundary
• live wire
• level sets
Region-based (RpI):
• clustering – kNN, CM, FCM
• graph cut
• fuzzy connectedness
• MRF
• watershed
• optimum partitioning
• (Mumford-Shah)
SM Approaches
• manual tracing
• live wire
• active shape/appearance
• M-reps
• atlas-based
26. Classification of Methods
26
Boundary-based (BpI):
• optimum boundary
• active boundary
• live wire
• level sets
Region-based (RpI):
• clustering – kNN, CM, FCM
• graph cut
• fuzzy connectedness
• MRF
• watershed
• optimum partitioning
• (Mumford-Shah)
SM Approaches
• manual tracing
• live wire
• active shape/appearance
• M-reps
• atlas-based
Hybrid Approaches
• BpI + BpI
• RpI + RpI
• BpI + RpI
• BpI + SM
• RpI + SM
• SM + SM
27. Classification of Methods
27
pI Approaches
+ Where image info is good,
accuracy is good;
- Bad where it is poor/absent;
- Need recognition help;
+ Can determine degree of
match of model to image
well;
- Lack obj shape &
geographic info;
28. Classification of Methods
28
SMApproaches
- Even where image info is
good, accuracy suffers;
+ Where bad, model helps;
+ Can help in recognition;
- Need best match info;
+ Good models embody obj
shape & geographic info;
33. Thresholding – Simple Segmentation
33
Brighter objects
Darker objects
DIFFICULTIES
1. The valley may be so broad that
it is difficult to locate a
significant minimum
2. Number of minima due to type
of details in the image
3. Noise
4. No visible valley
5. Histogram may be multi-modal
42. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
42
43. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
• Otsu’s algorithm selects a threshold that maximizes the
between-class variance . In the case of two classes,
43
2
b
2
b = P1(µ1 µ)2
+ P2(µ2 µ)2
= P1P2(µ1 µ2)2
44. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
• Otsu’s algorithm selects a threshold that maximizes the
between-class variance . In the case of two classes,
• where P1 and P2 denote class probabilities, and μi the means
of object and background classes.
44
2
b
2
b = P1(µ1 µ)2
+ P2(µ2 µ)2
= P1P2(µ1 µ2)2
45. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
45
P1 =
uX
ı=0
p(i)
P2 =
GmaxX
ı=u+1
p(i)
u
u
46. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
46
P1 =
uX
ı=0
p(i)
P2 =
GmaxX
ı=u+1
p(i)
µ1 =
uX
ı=0
ip(i)/P1
µ2 =
GmaxX
ı=u+1
ip(i)/P2
CLASS MEANS
47. Otsu Thresholding-Algorithm
47
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
48. Otsu Thresholding-Algorithm
48
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
2
b = P1(µ1 µ)2
+ P2(µ2 µ)2
= P1P2(µ1 µ2)2
49. Otsu Thresholding-Algorithm
49
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
50. Otsu Thresholding-Algorithm
50
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
51. Otsu Thresholding-Algorithm
51
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
52. Otsu Thresholding-Algorithm
52
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
53. Otsu Thresholding-Algorithm
53
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
optimal
54. Parametric Method for Optimal Thresholding
• Assuming again a two-class problem and assuming that the
distribution of gray levels for each class can be modeled by a
normal distribution with mean and variance
54
55. Parametric Method for Optimal Thresholding
• Assuming again a two-class problem and assuming that the
distribution of gray levels for each class can be modeled by a
normal distribution with mean and variance
• the overall normalized intensity histogram can be written as
the following mixture probability density function:
55
56. Parametric Method for Optimal Thresholding
• Assuming again a two-class problem and assuming that the
distribution of gray levels for each class can be modeled by a
normal distribution with mean and variance
• the overall normalized intensity histogram can be written as
the following mixture probability density function:
where P1 and P2 are class probabilities. The optimal threshold
(T) can be found as solving the quadratic equation à
56
58. Parametric Method for Optimal Thresholding
58
In case, variances of both classes are equal, then->
59. Parametric Method for Optimal Thresholding
59
In case, variances of both classes are equal, then->
60. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
60
61. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
61
Fixed
Thresholding
Adaptive
Thresholding
Iterative
Thresholding
62. Fixed Thresholding Methods
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
62
63. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
63
Fixed
Thresholding
Adaptive
Thresholding
Iterative
Thresholding
Phantom
Based
Image Quality
metrics based
65. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
65
Fixed
Thresholding
Adaptive
Thresholding
Iterative
Thresholding
Phantom
Based
Image Quality
metrics based
66. Iterative Thresholding Method (ITM)
66
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.
67. Iterative Thresholding Method (ITM)
67
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.
68. Iterative Thresholding Method (ITM)
68
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.
The measured S/B ratios of the
lesions are then estimated from
PET images, and their volumes are
iteratively calculated using the
calibrated S/B-threshold-volume curves
69. Iterative Thresholding Method (ITM)
69
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.
The measured S/B ratios of the
lesions are then estimated from
PET images, and their volumes are
iteratively calculated using the
calibrated S/B-threshold-volume curves
The resulting PET volumes are then
compared with the known sphere volume
and CT volumes of tumors that served
as gold standards.
75. Head-Neck CT – Thresholding for Skull
Modeling
75
(Slice Credit: P.Seutens)
Segmentation of the skull and the mandibula in CT images using thresholding.(a) Original CT
image of the head. (b) Result with a threshold value of 276 Hounsfield units. The segmented bony
structures are represented in color. (c) 3D rendering of the skull shows a congenital growth
deficiency of the mandibula in this 8-year-old patient. This information was used preoperatively to
plan a repositioning of the mandibula.
76. Multiple Thresholds – MRI Thresholding
76
Thresholding can be done interactively and separates the image into different
regions. Valleys in the histogram indicate potentially useful threshold values
Credit: Toeonies,K.
77. Summary of today’s lecture
• Introduction into the Medical Image Segmentation
• Recognition and Delineation concepts in Segmentation
• Simplest Segmentation method: Thresholding
– Otsu
– Parametric method for optimal thresholding
– PET Image thresholding
• ITM, fixed thresholding,etc.
77
78. Slide Credits and References
• Jayaram K. Udupa, MIPG of University of Pennsylvania, PA.
• P. Suetens, Fundamentals of Medical Imaging, Cambridge
Univ. Press.
• Foster, B., et al. CBM, Review paper, 2014.
• Kaus, et al. Radiology 2001.
• Toeonies, K., Medical Image Analysis.
• Farraher, et al., Radiology 2005
• Zaidi, H., Quantitative Analysis in Nuclear Medicine Imaging.
• Bailey et al. Positron Emission Tomography, Springer.
• Dawood, M., et al. Correction Techniques in Emission
Tomography
78