This document outlines a research proposal on medical image fusion. It discusses radiotherapy treatment planning which involves target volume delineation using fused images from modalities like PET, CT and MRI. The proposal discusses techniques for image decomposition, fusion and reconstruction. It reviews literature on various fusion methods like multi-resolution analysis, multi-scale geometric analysis and color based methods. It identifies research gaps in appropriate decomposition levels and contouring. The proposal discusses implementing a fusion method using soft computing techniques to differentiate between edge and non-edge regions.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
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.
The Cancer Imaging Archive (TCIA) is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download.
- Update on new data sets
- New features
- New publications
- Other news
Medical Image segmentation from dl .pptxSACHINS902817
Medical image segmentation is a critical task in the field of medical imaging analysis, with far-reaching implications for diagnosis, treatment planning, and disease monitoring. In this comprehensive discussion, we will explore the principles, techniques, challenges, applications, and future directions of medical image segmentation.
Introduction to Medical Image Segmentation
Medical image segmentation refers to the process of partitioning images acquired from various medical imaging modalities into meaningful regions or segments. These segments correspond to specific anatomical structures, pathological lesions, or other regions of interest within the human body. The primary goal of segmentation is to accurately delineate and extract relevant information from medical images, enabling clinicians to interpret and analyze the data effectively.
Importance of Medical Image Segmentation
The significance of medical image segmentation cannot be overstated, as it plays a crucial role in numerous clinical applications:
Diagnosis: Segmentation aids in the identification and characterization of abnormalities, such as tumors, lesions, and other pathological structures.
Treatment Planning: Precise segmentation facilitates treatment planning by providing clinicians with detailed information about the spatial extent and location of anatomical structures and pathological regions.
Image-Guided Interventions: Segmentation enables image-guided interventions, including surgical navigation, radiation therapy, and minimally invasive procedures.
Disease Monitoring: Changes in segmented regions over time can be used to monitor disease progression, treatment response, and patient outcomes.
Techniques for Medical Image Segmentation
A variety of techniques have been developed for medical image segmentation, ranging from traditional methods to advanced machine learning and deep learning approaches:
Thresholding: Simple thresholding techniques segment images based on intensity values, dividing them into foreground and background regions.
Region-Based Methods: Region growing, region merging, and watershed algorithms identify regions of uniform intensity or texture.
Edge-Based Methods: Edge detection algorithms identify boundaries between different regions based on intensity gradients.
Clustering Algorithms: K-means clustering and fuzzy c-means clustering group pixels with similar characteristics into clusters.
Machine Learning Approaches: Supervised and unsupervised machine learning algorithms, such as support vector machines (SVMs) and k-nearest neighbors (KNN), learn segmentation patterns from labeled training data.
Deep Learning Models: Convolutional neural networks (CNNs), particularly architectures like U-Net, FCN (Fully Convolutional Network), and SegNet, have revolutionized medical image segmentation by automatically learning hierarchical features from raw image data.
Challenges in Medical Image Segmentation
Despite significant advancements, medical image segmentatio
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
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.
The Cancer Imaging Archive (TCIA) is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download.
- Update on new data sets
- New features
- New publications
- Other news
Medical Image segmentation from dl .pptxSACHINS902817
Medical image segmentation is a critical task in the field of medical imaging analysis, with far-reaching implications for diagnosis, treatment planning, and disease monitoring. In this comprehensive discussion, we will explore the principles, techniques, challenges, applications, and future directions of medical image segmentation.
Introduction to Medical Image Segmentation
Medical image segmentation refers to the process of partitioning images acquired from various medical imaging modalities into meaningful regions or segments. These segments correspond to specific anatomical structures, pathological lesions, or other regions of interest within the human body. The primary goal of segmentation is to accurately delineate and extract relevant information from medical images, enabling clinicians to interpret and analyze the data effectively.
Importance of Medical Image Segmentation
The significance of medical image segmentation cannot be overstated, as it plays a crucial role in numerous clinical applications:
Diagnosis: Segmentation aids in the identification and characterization of abnormalities, such as tumors, lesions, and other pathological structures.
Treatment Planning: Precise segmentation facilitates treatment planning by providing clinicians with detailed information about the spatial extent and location of anatomical structures and pathological regions.
Image-Guided Interventions: Segmentation enables image-guided interventions, including surgical navigation, radiation therapy, and minimally invasive procedures.
Disease Monitoring: Changes in segmented regions over time can be used to monitor disease progression, treatment response, and patient outcomes.
Techniques for Medical Image Segmentation
A variety of techniques have been developed for medical image segmentation, ranging from traditional methods to advanced machine learning and deep learning approaches:
Thresholding: Simple thresholding techniques segment images based on intensity values, dividing them into foreground and background regions.
Region-Based Methods: Region growing, region merging, and watershed algorithms identify regions of uniform intensity or texture.
Edge-Based Methods: Edge detection algorithms identify boundaries between different regions based on intensity gradients.
Clustering Algorithms: K-means clustering and fuzzy c-means clustering group pixels with similar characteristics into clusters.
Machine Learning Approaches: Supervised and unsupervised machine learning algorithms, such as support vector machines (SVMs) and k-nearest neighbors (KNN), learn segmentation patterns from labeled training data.
Deep Learning Models: Convolutional neural networks (CNNs), particularly architectures like U-Net, FCN (Fully Convolutional Network), and SegNet, have revolutionized medical image segmentation by automatically learning hierarchical features from raw image data.
Challenges in Medical Image Segmentation
Despite significant advancements, medical image segmentatio
Of the 118.5 million blood donations collected globally, 40% of these are collected in high-income countries, home to 16% of the world’s population.
In low-income countries, up to 54 % of blood transfusions are given to children under 5 years of age; whereas in high-income countries, the most frequently transfused patient group is over 60 years of age, accounting for up to 76% of all transfusions.
Based on samples of 1000 people, the blood donation rate is 31.5 donations in high-income countries, 16.4 donations in upper-middle-income countries, 6.6 donations in lower-middle-income countries and 5.0 donations in low-income countries.
An increase of 10.7 million blood donations from voluntary unpaid donors has been reported from 2008 to 2018. In total, 79 countries collect over 90% of their blood supply from voluntary unpaid blood donors; however, 54 countries collect more than 50% of their blood supply from family/replacement or paid donors.
Only 56 of 171 reporting countries produce plasma-derived medicinal products (PDMP) through the fractionation of plasma collected in the reporting countries. A total of 91 countries reported that all PDMP are imported, 16 countries reported that no PDMP were used during the reporting period, and 8 countries did not respond to the question.
The volume of plasma for fractionation per 1000 population varied considerably between the 45 reporting countries, ranging from 0.1 to 52.6 litres, with a median of 5.2 litres.Of the 118.5 million blood donations collected globally, 40% of these are collected in high-income countries, home to 16% of the world’s population.
In low-income countries, up to 54 % of blood transfusions are given to children under 5 years of age; whereas in high-income countries, the most frequently transfused patient group is over 60 years of age, accounting for up to 76% of all transfusions.
Based on samples of 1000 people, the blood donation rate is 31.5 donations in high-income countries, 16.4 donations in upper-middle-income countries, 6.6 donations in lower-middle-income countries and 5.0 donations in low-income countries.
An increase of 10.7 million blood donations from voluntary unpaid donors has been reported from 2008 to 2018. In total, 79 countries collect over 90% of their blood supply from voluntary unpaid blood donors; however, 54 countries collect more than 50% of their blood supply from family/replacement or paid donors.
Only 56 of 171 reporting countries produce plasma-derived medicinal products (PDMP) through the fractionation of plasma collected in the reporting countries. A total of 91 countries reported that all PDMP are imported, 16 countries reported that no PDMP were used during the reporting period, and 8 countries did not respond to the question.
The volume of plasma for fractionation per 1000 population varied considerably between the 45 reporting countries, ranging from 0.1 to 52.6 litres, with a median of 5.2 litres.
Batch -13.pptx lung cancer detection using transfer learninghananth1513
Embedded systems
Embedded systems are special-purpose computing systems embedded in application environments or in other computing systems and provide specialized support. The decreasing cost of processing power, combined with the decreasing cost of memory and the ability to design low-cost systems on chip, has led to the development and deployment of embedded computing systems in a wide range of application environments. Examples include network adapters for computing systems and mobile phones, control systems for air conditioning, industrial systems, and cars,
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" 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/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Generation and Use of Quantitative Pathology PhenotypeJoel Saltz
Motivation, tools and methods analysis of digital pathology imagery, integration with "omics" and Radiology, use in Precision Medicine. Presentation at the Early Detection Research Network meeting, April 2015, Atlanta GA
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Social media marketing (SMM) is a form of digital marketing that utilizes social media platforms to promote products, services, or brands. The goal of social media marketing is to connect with the target audience, build brand awareness, increase website traffic, and drive engagement and conversions. Here are some key aspects of social media marketing:
Strategy Development:
Identify your target audience: Understand the demographics, interests, and online behavior of your target audience.
Set clear goals: Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for your social media campaigns.
Choose the right platforms: Select social media platforms that align with your target audience and business objectives.
Content Creation:
Create engaging content: Develop content that resonates with your audience, such as images, videos, infographics, and text posts.
Maintain consistency: Establish a consistent posting schedule to keep your audience engaged and informed.
Use a variety of content types: Experiment with different content formats to keep your social media presence diverse and interesting.
Audience Engagement:
Respond to comments and messages: Engage with your audience by responding to comments, messages, and mentions in a timely manner.
Encourage user-generated content: Encourage your followers to create and share content related to your brand.
Run contests and giveaways: Organize contests or giveaways to boost engagement and attract new followers.
Paid Advertising:
Utilize paid social media advertising: Platforms like Facebook, Instagram, Twitter, and LinkedIn offer advertising options to reach a larger audience.
Targeted advertising: Use advanced targeting options to reach specific demographics, interests, and behaviors.
Analytics and Monitoring:
Use analytics tools: Monitor the performance of your social media campaigns using analytics tools provided by the platforms or third-party tools.
Adjust strategies based on data: Analyze the data and adjust your strategies to optimize performance and achieve better results.
Influencer Marketing:
Collaborate with influencers: Partner with influencers who align with your brand to reach a wider audience and build credibility.
Leverage user trust: Influencers can help establish trust with their followers, leading to increased brand credibility.
Social Media Trends:
Stay updated: Keep track of emerging trends in social media marketing and adapt your strategies accordingly.
Experiment with new features: Platforms regularly introduce new features; experiment with these features to stay ahead of the curve.
Remember that effective social media marketing requires a consistent and strategic approach. Regularly assess your performance, listen to your audience, and adjust your strategies to meet your goals.
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.
Of the 118.5 million blood donations collected globally, 40% of these are collected in high-income countries, home to 16% of the world’s population.
In low-income countries, up to 54 % of blood transfusions are given to children under 5 years of age; whereas in high-income countries, the most frequently transfused patient group is over 60 years of age, accounting for up to 76% of all transfusions.
Based on samples of 1000 people, the blood donation rate is 31.5 donations in high-income countries, 16.4 donations in upper-middle-income countries, 6.6 donations in lower-middle-income countries and 5.0 donations in low-income countries.
An increase of 10.7 million blood donations from voluntary unpaid donors has been reported from 2008 to 2018. In total, 79 countries collect over 90% of their blood supply from voluntary unpaid blood donors; however, 54 countries collect more than 50% of their blood supply from family/replacement or paid donors.
Only 56 of 171 reporting countries produce plasma-derived medicinal products (PDMP) through the fractionation of plasma collected in the reporting countries. A total of 91 countries reported that all PDMP are imported, 16 countries reported that no PDMP were used during the reporting period, and 8 countries did not respond to the question.
The volume of plasma for fractionation per 1000 population varied considerably between the 45 reporting countries, ranging from 0.1 to 52.6 litres, with a median of 5.2 litres.Of the 118.5 million blood donations collected globally, 40% of these are collected in high-income countries, home to 16% of the world’s population.
In low-income countries, up to 54 % of blood transfusions are given to children under 5 years of age; whereas in high-income countries, the most frequently transfused patient group is over 60 years of age, accounting for up to 76% of all transfusions.
Based on samples of 1000 people, the blood donation rate is 31.5 donations in high-income countries, 16.4 donations in upper-middle-income countries, 6.6 donations in lower-middle-income countries and 5.0 donations in low-income countries.
An increase of 10.7 million blood donations from voluntary unpaid donors has been reported from 2008 to 2018. In total, 79 countries collect over 90% of their blood supply from voluntary unpaid blood donors; however, 54 countries collect more than 50% of their blood supply from family/replacement or paid donors.
Only 56 of 171 reporting countries produce plasma-derived medicinal products (PDMP) through the fractionation of plasma collected in the reporting countries. A total of 91 countries reported that all PDMP are imported, 16 countries reported that no PDMP were used during the reporting period, and 8 countries did not respond to the question.
The volume of plasma for fractionation per 1000 population varied considerably between the 45 reporting countries, ranging from 0.1 to 52.6 litres, with a median of 5.2 litres.
Batch -13.pptx lung cancer detection using transfer learninghananth1513
Embedded systems
Embedded systems are special-purpose computing systems embedded in application environments or in other computing systems and provide specialized support. The decreasing cost of processing power, combined with the decreasing cost of memory and the ability to design low-cost systems on chip, has led to the development and deployment of embedded computing systems in a wide range of application environments. Examples include network adapters for computing systems and mobile phones, control systems for air conditioning, industrial systems, and cars,
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" 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/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Generation and Use of Quantitative Pathology PhenotypeJoel Saltz
Motivation, tools and methods analysis of digital pathology imagery, integration with "omics" and Radiology, use in Precision Medicine. Presentation at the Early Detection Research Network meeting, April 2015, Atlanta GA
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Social media marketing (SMM) is a form of digital marketing that utilizes social media platforms to promote products, services, or brands. The goal of social media marketing is to connect with the target audience, build brand awareness, increase website traffic, and drive engagement and conversions. Here are some key aspects of social media marketing:
Strategy Development:
Identify your target audience: Understand the demographics, interests, and online behavior of your target audience.
Set clear goals: Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for your social media campaigns.
Choose the right platforms: Select social media platforms that align with your target audience and business objectives.
Content Creation:
Create engaging content: Develop content that resonates with your audience, such as images, videos, infographics, and text posts.
Maintain consistency: Establish a consistent posting schedule to keep your audience engaged and informed.
Use a variety of content types: Experiment with different content formats to keep your social media presence diverse and interesting.
Audience Engagement:
Respond to comments and messages: Engage with your audience by responding to comments, messages, and mentions in a timely manner.
Encourage user-generated content: Encourage your followers to create and share content related to your brand.
Run contests and giveaways: Organize contests or giveaways to boost engagement and attract new followers.
Paid Advertising:
Utilize paid social media advertising: Platforms like Facebook, Instagram, Twitter, and LinkedIn offer advertising options to reach a larger audience.
Targeted advertising: Use advanced targeting options to reach specific demographics, interests, and behaviors.
Analytics and Monitoring:
Use analytics tools: Monitor the performance of your social media campaigns using analytics tools provided by the platforms or third-party tools.
Adjust strategies based on data: Analyze the data and adjust your strategies to optimize performance and achieve better results.
Influencer Marketing:
Collaborate with influencers: Partner with influencers who align with your brand to reach a wider audience and build credibility.
Leverage user trust: Influencers can help establish trust with their followers, leading to increased brand credibility.
Social Media Trends:
Stay updated: Keep track of emerging trends in social media marketing and adapt your strategies accordingly.
Experiment with new features: Platforms regularly introduce new features; experiment with these features to stay ahead of the curve.
Remember that effective social media marketing requires a consistent and strategic approach. Regularly assess your performance, listen to your audience, and adjust your strategies to meet your goals.
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.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
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Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
1. Submitted By:
Harmeet Kaur
Dept. of Computer Science and
Applications (DCSA)
Panjab University, Chandigarh –
160014
PUPIN: 18205000930
Supervisor:
Dr. Satish Kumar
Associate Professor
Department of Computer
Science and Applications
Panjab University SSG
Regional Centre
Hoshiarpur, Punjab
CO-GUIDE;Dr
Behgal KS Director
BEHGAL CANCER
HOSPITAL
DR. YAGIYADEEP
SHARMA R.S.O
BEHGAL HOSPITAL
2. 1. Introduction
i. RT Planning
ii. Role of Fusion in RT Planning
iii. Image Decomposition
iv. Image Fusion Rules
v. Image Reconstruction
vi. Image Quality Assessment
vii. International status
viii. National status
ix. Research gaps
2. Review of Literature
i. Image Decomposition And Reconstruction Techniques
ii. Image Fusion
iii. Evaluation metrics
3. Problem Statement
i. Problem Definition
ii. Objectives/ Aims of the proposed research
iii. Scope of the proposed research
2
3. 4. Research Methodology
i. Design Methodology
ii. Implementation
a) Hardware
b) Software
iii. Database
5. Validation and Testing
6. References
3
4. Positron Emission Tomography(PET) image
shows good functional information.
Computed Tomography (CT)is used to provide
information about the anatomical structure of the
organs. CT scanners are used to get the images
of dense structures like bones.
Magnetic Resonance Imaging(MRI) gives better
soft tissue contrast.
There is a need to fuse the above modalities to
assist the expert in taking decisions in treatment
planning and diagnosis.
4
6. Radiotherapy Treatment (RT) Planning is an imperative
phase after the confirmation of disease and before the
treatment delivery.
Target volume delineation is done i.e. GTV, CTV, PTV,
etc. with different colored markers so that the most
effected part is perfectly delineated.
Based on target volume delineation, Dose Planning step
is carried out.
6
8. To provide a better and more complete view of the image
content.
To contour the tumor bed in a better way.
To help in decision making process.
8
10. • Decomposition also known as Analysis phase deals with extraction of
features on the basis of frequency, wavelet or edge.
•Decomposition of an image is done so that the variety of information present
in each image is extracted into sub-bands.
•The sub-images, thus obtained from decomposition contain more useful
information and can be given more weightage as compared to the sub-bands
containing undesirable information.
•Once the sub-bands are obtained, respective sub-band of each image is
fused using fusion technique.
•The main motive behind fusion is to inculcate the features into the final
fused image.
•After successfully fusing the sub-images, single fused image is
reconstructed from the fused sub-images.
10
14. Soft computing based techniques for Medical Image Fusion
• Soft computing techniques: Neural network, Fuzzy logic, Genetic
algorithm and ANFIS. The neural network works on the principal of
learning and adaptation, imprecise and unclear situations are dealt with
fuzzy logic, genetic algorithms are used for searching and optimization,
and ANFIS combines the features of fuzzy logic with neural network.
• The encouragement to authors for using soft computing methods comes
from the fact that these techniques have close resemblance with human
like decision making and will outperform by learning from human experts
followed by rigorous testing and training.
• Soft Computing approach acts as a perfect imitator of biological and
physical processes and so it is also known as nature inspired strategies.
14
16. Results of existing Fusion methods- MAX, PCA, MEAN
Existing methods of
fusion
16
17. The Institute of Cancer Research (ICR), UK: The Radiotherapy
Treatment Planning team works on novel methods for targeting tumours
with external beam radiation. The necessary imaging technology,
calculation of dose distributions and optimization of individualized
treatment plans are developed. Techniques such as intensity-modulated
radiation therapy, volumetric modulated arc therapy and image-guided
radiotherapy are continually being improved.
Olivia Newton-John Cancer Wellness & Research Centre, Australia:
The focus is on targeting and molecular imaging of tumours and
exploring receptor-based signaling pathways responsible for cancer cell
growth through the development of innovative strategies for molecular
imaging of cancer
National Cancer Institute, US: National Cancer Institute’s CIP (Cancer
Imaging Program) supports research on the use of imaging techniques
to noninvasively diagnose cancer and the identification of disease
subsets in patients, among other research areas. Other opportunities in
imaging include the development of better tools for imaging tumors and
for reading and interpreting scans. 17
18. Dana-Farber Cancer Institute, US: The Center for Biomedical
Imaging in Oncology of this institute is focused to use state-of-the-art
preclinical and clinical imaging in order to accelerate translational
research and develop new diagnostic and therapeutic strategies for
patients with cancer. The Center has two primary components: the
Lurie Family Imaging Center and a clinical research program. The
Lurie Family Imaging Center is a preclinical imaging facility equipped
with a 7T MRI, micro PET/CT, ultrasound, bioluminescence, and
fluorescence imaging instruments, along with radiochemistry and
radiotherapy capabilities. Imaging Design, Evaluation, and Analysis
(IDEA) lab, a multidisciplinary functional imaging laboratory that
provides study design, imaging protocol development, PET/CT
scanner evaluation and qualification, quality control/archival of
imaging data, diagnostic review of images, quantitative image
analysis, and scientific interpretation of final imaging results for
numerous institutional, national, and global multicenter cancer
therapeutic trials.
18
19. Postgraduate Institute of Medical Education & Research,
Chandigarh : ONCENTRA
Rajiv Gandhi Cancer Institute & Research Centre, Delhi :
ECLIPSE
Delhi State Cancer Institute, Delhi : COBALT
AIIMS DELHI : MONACCO
Behgal Cancer Institute (IT & Radiation Technology),
Mohali :ONCENTRA
19
20. The research gaps, as per the literature survey are as
follows:
The appropriate decomposition levels are required to find
the coarse details of the image.
• A Fusion method is required which should be capable of
differentiating between the edge and non-edge regions.
Unlike many existing fusion methods, the new proposed
method will consider the neighboring pixels also.
• Contouring: Treatment planning involves contouring and
it will determine the success of fusion process. If the
fusion is carried out properly, the tumor area will be
maximally covered.
20
22. Sr.
No.
Paper Title AUTHOR METHOD
MODALITIES
ANALYSED
SOURCE
Evaluation
Metrics Used
1
Image fusion using
hierarchical PCA.
Patil et al.[9]
Multi-resolution
analysis
MRI, CT www.fusion.org
Quantitative
quality and
subjective
quality analysis
2
Union Laplacian pyramid
with multiple features for
medical image fusion
J. Du et al. [13]
Multi-resolution
analysis
MRI-CT, MRI-
PET,PET-
SPECT
Whole brain
website of
Harvard
medical school
Quantitative
quality and
subjective
quality analysis,
Histogram
analysis
3
Medical Image Fusion
with Laplacian Pyramids
A. Sahu [14]
Multi-resolution
analysis
MRI-T2, MR-
PD, CT
-
Quantitative and
qualitative
analysis
4
Fusion of Medical
Sensors Using Adaptive
Cloud Model in Local
Laplacian Pyramid
Domain
W.Li et al.[17]
Multi-resolution
analysis
MRI, PET,
SPECT
Real time
database
Quantitative and
subjective
analysis
5
Multi-Modality Medical
Image Fusion using
Discrete Wavelet
Transform
Bhavana V. et
al.[19]
Multi-scale
geometric
analysis/Wavel
et Transform
MRI, PET
Whole brain
website of
Harvard
medical school
Quantitative
Analysis22
23. Sr. No. Paper Title AUTHOR METHOD
MODALITIES
ANALYSED
SOURCE
Evaluation
Metrics Used
6
Medical image fusion by
wavelet transform
modulus maxima
G. Qu et al. [20]
Multi-scale
geometric
analysis/Wavel
et Transform
CT, MRI -
Mutual
information (MI)
7
Pixel based medical
image fusion techniques
using discrete wavelet
transform and Stationary
wavelet transform
K. P. Indira et
al. [22]
Multi-scale
geometric
analysis/Wavel
et Transform
CT, PET
Real time
database
Objective
analysis
8
Fusion of multimodal
medical images using
Daubechies complex
wavelet transform - A
multiresolution approach
R. Singh et al.
[31]
Multi-scale
geometric
analysis/Wavel
et Transform
CT, MRI, MR-
T1, MRA
www.imagefusi
on.org
Quantitative
and subjective
analysis
9
Edge Preserving Image
Fusion Based on
Contourlet Transform
A. Khare et al.
[41]
Multi-scale
geometric
analysis/Wavel
et Transform
Multifocus and
medical images
Standard
database
Objective
analysis
23
24. Sr. No. Paper Title AUTHOR METHOD
MODALITIES
ANALYSED
SOURCE
Evaluation
Metrics Used
10
PET and MRI brain image
fusion using wavelet
transform with structural
information adjustment &
spectral information patching
Huang et al.[44]
Color based
Method
PET, MRI
www.med.harva
rd.edu
Objective
analysis
11
MRI and PET image
fusion by combining IHS
and retina-inspired
models
Daneshvar et
al.[42]
Color based
Method
PET, MRI
HARVARD
WEBSITE
Visual analysis,
statistical
assessment
12
Filter for biomedical
imaging and image
processing
[45]
Filter based
Method
MRI, PET
Real time
database
Quantitative
and subjective
analysis
13
Medical Image Fusion
Based on Rolling
guidance filter and
Spiking Cortical Model
L. Shuaiqi et
al.[46]
Filter based
Method
CT, MRI,
ULTRASOUND,
SPECT
Real time
database
Quantitative
and subjective
analysis
14
Image Fusion based on
Pixel Significance using
Cross Bilateral Filter
Kumar et al.
[47]
Filter based
Method
IR-VISIBILE,
MULTIFOCUS,
,MEDICAL
www.imagefusi
on.org
Quantitative
and subjective
analysis
24
26. S.No. Paper Authors Level of fusion Technique Verification
methods
1 Pixel-level image fusion with
simultaneous orthogonal
matching pursuit
B. Huang et al. [57] Pixel level Signal sparse
representation
theory
Objective metrices
2 Hybrid Pixel-Based Method for
Cardiac Ultrasound Fusion Based
on Integration of PCA and DWT
S. Mazaheri et
al.[58]
Pixel level Hybrid – PCA and
DWT
Quantitative
analysis and
subjective analysis
3 MRI and PET images fusion
based on human retina model
D. Sabalan et
al.[59]
Feature level Retina based model Objective metrices
4 Simultaneous image fusion and
super-resolution using sparse
representation
H. Yin et al. [60] Pixel level Sparse
representation
Objective metrices
and subjective
analysis
26
27. S.No. Paper Authors Level of fusion Technique Verification
methods
5 Pixel-level image fusion
scheme based on steerable
pyramid wavelet transform
using absolute maximum
selection fusion rule
O. Prakash et al.
[62]
Pixel level Multi resolution
steerable pyramid
wavelet transform
Quantitative and
qualitative
metrices
6 Medical images fusion by
using weighted least squares
filter and sparse representation
W. Jiang et al.
[67]
Pixel level Multi-scale edge
preserving
decomposition
and sparse
representation
Quantitative
analysis and
subjective
analysis
27
28. REF TYPE MODALIT
Y
METRIC METRIC VALUES OBTAINED
5 FATAL STROKE
ALZHEIMER
MRI CT
MRI PET
MI,PSNR, QAB/F
MI,PSNR, QAB/F
1.7048,20.2037,.5849
1.1666,25.2012,.6259
6 ALZHEIMER
SUB ACUTE
STROKE
BRAIN TUMOR
MRI PET
MRI
SPECT
MRI
SPECT
QMI, QS,QAB/F
QMI, QS,QAB/F
QMI, QS,QAB/F
1.5017,.7972,.6722
1.3740,.8907,.6278
1.9809,.8248,.6875
23 NORMAL AXIAL
NORMAL
CORONAL
ALZHEIMER
MRI PET
MRI PET
MRI PET
MSE,PSNR,AG,SD
(W=.5/.7)
.02819/.1911,63.6424/55.3184,5.6237/6.8573,8.116/2.2966
.11529/.18589,57.5131/55.4383,5.4715/7.9881,4.9116/2.63
.10509/.19144,58.0621/55.3104,6.7541/10.5855,2.3371/.3808
28 MILD
ALZHEIMER
MILD
ALZHEIMER
MILD
ALZHEIMER
MRI PET
MRI PET
MRI PET
PSNR,ENTROPY,
STD
61.8509,3.0617,3.4886
59.5109,2.9238,2.3311
62.2149,2.5149,1.9743
29 NORMAL AXIAL
NORMAL
CORONAL
ALZHEIMER
MRI PET
MRI PET
MRI PET
SD,AG(W=.5/.7) 6.7169/7.0019,5.4759/5.5285
7.5140/7.8330,6.3542/6.4355
4.9210/4.9731,5.1964/5.2169
45 NORMAL AXIAL
NORMAL
CORONAL
MRI PET
MRI PET
MRI PET
AVG,O.P,MI, 5.3603,2.3457,.6541
6.2927,1.6104,.6551
5.0353,.9765,.6230
Metrics Assessed On Harvard Database
28
29. • In the proposed research, the radiotherapy treatment
planning is improved by fusing multi-modality images
such as Magnetic Resonance Imaging (MRI), Computed
Tomography (CT) etc.
• A novel algorithm for image fusion is proposed which
helps the radiation oncologist in contouring on a single
fused image.
Images are combined such that the fused image contain
both(functional and anatomical) information, in a single
image
Fusion will directly effect the treatment execution.
29
30. Comparing the features of different modality of images.
Analysis of various image decomposition and
reconstruction techniques already available in the
literature (Multi Scale Geometric Analysis, Color based).
To study various image fusion techniques available in the
literature(PCA, Averaging method, Weighted average,
Fuzzy logic, ANN).
To propose a Fusion method which will aid Contouring.
Image Fusion Quality Assessment: Subjective as well as
Objective comparison methods are used for assessing
the quality of the output image.
30
31. Registered MRI, CT, PET, etc. images are taken as input
In decomposition phase, only Multi Decomposition
Analysis, Filter based or color based techniques are
considered.
Minor need based change depending on the outcome of
experimentaltheoretical study are made.
Fusion technique from the following, are explored:
a) Principal component analysis (PCA).
b) Averaging method
c) Weighted average
d) Wavelet Transform
e) Fuzzy logic
f) ANN
g) ANFIS 31
33. Hardware
• For implementation, operating system with standard
peripherals will be required.
• Operating system : Windows 7( 32-bit operating
system), X86 based PC
• Processor Intel(R) Core-TM i3-3110M CPU@ 2.40GHz,
2400Mhz, 2-Core(s), 4-Logical Processor/processors.
• RAM: Physical Memory installed-4.00GB
33
34. • Software
• MATLAB 7.9.0(R2009b)- 64 bit or higher version is
used for pre-processing, image decomposition and
reconstruction, and fusion i.e. the codes are designed
using MATLAB.
• Various tools available in MATLAB software are used.
• The functions from the library of other software tools
are used.
• For Graphical User Interface(GUI) - GUIDE will be
used
34
35. • The experiments are performed on imaging data taken
from Whole Brain Atlas database, available online.
• The Whole Brain Atlas is a benchmark database for
evaluating the performance of multi-modal medical image
fusion methods, which was established by Keith A.
Johnson and J. Alex Becker at Harvard Medical School.
Importantly, all the images of the database are co-
aligned.
35
36. • For the image fusion quality assessment we need to go
for subjective as well as objective comparison methods.
• In case of subjective method, visual evaluation is done.
• For the objective evaluation we have many performance
measures like entropy (EN), Average Gradient(AG),
Standard Deviation (SD), Root Mean Square Error
(RMSE), Peak Signal to Noise Ratio (PSNR), QAB/F , LAB/F
, NAB/F etc.
• The results of the research will be tested and validated
on the available images.
36
38. • The Fuzzy Logic uses fuzzy sets to deal with the
vague values logically.
• The comparison is made with wavelet based fusion
in which the three stages are followed to apply fusion.
• The first stage is decomposition, in which the
acquired images are decomposed into sub-bands
called approximate and details.
• These sub-bands from each source image are then
fused using fuzzy logic.
• The last step is reconstruction; in which inverse of
decomposition is done to finally obtain a single image
from sub-bands.
38
39. • The proposed method is implemented on CT, MRI
modalities and is based on 2 input and 1 output
fuzzy inference systems with defined fuzzy rules.
• The decomposition method is DWT and min rule
is applied on approximate sub-bands, max rule is
applied on the detailed sub-bands.
• Finally reconstruction is done by taking inverse of
DWT.
• T-S type fuzzy system is implemented. The fuzzy
rules are defined based on the pixel intensity of
each source image.
39
40. Algorithm for image fusion using fuzzy logic
// IMGCT = CT image.
// IMGMRI = MRI image.
// SBapprox. = Approximate sub-bands.
// SBdetail = Detail sub-bands.
// FUZZYout= Fuzzy inference output or Fused image
// IMGrecont= Reconstructed image.
STEP 1: Input two images (IMGCT, IMGMRI).
STEP 2: Decompose images to extract approximate and detail sub-bands.
DWT(IMGCT, IMGMRI)
//min rule to extract approximate sub-bands.
//max rule to extract detail sub-bands.
STEP 3: Fuse sub-bands obtained after decomposition.
FUZZYout = Fuzzy_Logic (SBapprox., SBdetail )
// 9 fuzzy rules are defined to obtain the result from T_S Fuzzy Inference System.
STEP 4: Reconstruction of Image.
IMGrecont = Inverse(FUZZYout)
// Inverse of decomposition method or varied reconstruction method
40
42. • The experiments were carried out on the images from Harvard database.
From the various modalities available, the CT and MRI images were the
candidate images to be fed to fuzzy logic for fusion.
• After successful implementation of FIS, the results are compared with the
fusion results obtained from wavelets.
• The evaluation is done in two ways; using metrics calculation and visual
inspection.
• The evaluation is done using Peak Signal to Noise Ratio (PSNR), Signal to
Noise Ratio (SNR) and Mean Square Error (MSE) metrics, taking reference
image to be MRI.
• After this, in the next step, CT image is taken as reference image. The
table shows PSNR value 8.5366 with reference image MRI, PSNR value
10.8427 with reference image CT.
• Similarly the SNR is 4.2605 with reference image MRI and with reference
image CT, SNR is 6.4822. The MSE is 9.1079e+04 with reference to MRI
and with reference to CT, MSE is 1.2185e+04.
42
46. • Scale as well as orientation based decomposition is performed before the
fusion process begins. To fetch the low frequency component low pass filter is
performed and to fetch high frequency sub-bands, high pass filters is applied.
• Hence a complete representation of the image in the decomposed parts is
obtained where smoothing is a process of convolution of image with
Uniform/Gaussian kernel.
• The Cross Bilateral Filter has the edge preservation ability which makes it a
likely acceptable candidate for extraction of features in the decomposed sub-
bands. The medical images require strict attention at the boundaries as well as
the volume within, the need for an algorithm to serve the same purpose arise.
• In the CBF, two factors radiometric and geometric sigma are used to fine
tune the cbf components. The Cross bilateral filter accomplish edge preserved
smoothing by modifying the kernel based on the indigenous contents which is
impossible to achieve using Gaussian kernel. Using cross-bilateral filter based
decomposition; detailed coefficients are obtained.
46
47. • The Cross Bilateral Filter (CBF) is widely used by authors for fusion. CBF is
used for decomposing the images, which is a pre-fusion requirement.
• On applying CBF, image is decomposed into 2 components namely, cbf
component and detail component. Subtracting the cbf component from
original image gives detail component.
• This detail component is used for further processing. The detail component
of each modality is given as input to ANFIS for fusion.
• CBF is used in order to enhance the multi-modality medical image fusion
results by providing edge preservation.
• The proposed work is compared with the techniques available in MATLAB
Toolbox.
• The purpose is to make improvements in the fusion which will ease the
oncologist to make decisions on the resultant image obtained. 47
48. • The CBF component is calculated for each input image (ACBF,
BCBF) while tuning the radiometric sigma and geometric sigma.
Euclidean distance calculation is done to consider the
neighboring pixels as well. When these CBF components are
subtracted from their respective original images, detailed
components are obtained, having equation:
ADETAIL=A-ACBF
BDETAIL=B-BCBF
48
51. • Input: A (MR-T1) , B (MR-T2)
• Decomposition: Deducing kernel weights from one image and applying it on the
second image and hence ACBF, BCBF are produced.
• Fetching the detailed image: For this, output obtained from the above step is
subtracted from original image to get the details ADETAIL, BDETAIL.
• Wavelet Selection: From the family of wavelets, Biorthogonal wavelet (bior 2.2)
transform is applied on the source images A, B.
• Fusion Strategy: Fuzzy inference system and average rule is performed on the
decomposed parts. Fuzzy Logic is applied on the detailed components.
• To deal with the approximate components, the average rule is followed to fuse
the low-low, high-low and low-high subbands. The details obtained from the CBF
is fed to the Fuzzy Inference system of Mamdani type for fusion with two input
variables and one output variable with Gauss membership function for each input
and output is defined.
• 25 Fuzzy rules are defined to fuse the pixels with min as AndMethod, max as
OrMethod. For implication, min is used, max rule is used for aggregation and
centroid for defuzzification.
• Reconstruction: This is the last step in which inverse wavelet transform is
performed to reconstruct the final fused image. Fused subcomponents are
combined into a single image which is expected to be more informative for
radiotherapy treatment planning.
51
53. METRIC
USED
CONVENTIONAL EVALUATION METRICS
Proposed
method
Image fusion
based on pixel
significance using
cross bilateral
filter
An efficient adaptive
fusion scheme for
multifocus images in
wavelet domain using
statistical properties
of neighborhood
A modified
statistical approach
for image fusion
using wavelet
transform
Multifocus and
multispectral image
fusion based on pixel
significance using
multiresolution
decomposition
A novel multifocus
image fusion scheme
based on pixel
significance using
wavelet transform
API 48.2381 54.7351 46.3165 36.4330 40.1711 44.1301
SD 63.6862 57.6902 52.3071 51.3242 46.8869 51.3010
FS 1.9995 1.6142 1.6899 1.7651 1.7126 1.6880
CC 0.7182 0.6565 0.6374 0.5563 0.6185 0.6011
53
Evaluation of Results
54. METRIC
USED
OBJECTIVE EVALUATION METRICS
Proposed
method
Image fusion based
on pixel
significance using
cross bilateral
filter.
An efficient adaptive
fusion scheme for multi
focus images in wavelet
domain using statistical
properties of
neighborhood .
A modified statistical
approach for image
fusion using wavelet
transform.
Multi focus and
multispectral image
fusion based on pixel
significance using
multi-resolution
decomposition.
A novel multi focus
image fusion scheme
based on pixel
significance using
wavelet transform.
QAB/F 0.8940 0.8932 0.8065 0.6900 0.7760 0.7300
LAB/F 0.0929 0.0961 0.1856 0.2776 0.2137 0.2531
NAB/F 0.0131 0.0950 0.0735 0.2172 0.0924 0.1310
SUM 1 1 1 1 1 1
54
Evaluation of Results
55. • Adaptive Neuro-Fuzzy Inference System (ANFIS) is a class of adaptive
networks.
• This class of networks in ANFIS is functionally equivalent to Fuzzy
Inference Systems (FIS).
• The T-S(Takagi-Sugeno) is fine-tuned using hybrid learning method.
• For modeling training data set, combination of least-squares and back-
propagation gradient descent methods are used.
• The basic structure of ANFIS is depicted having two inputs with five
membership functions for each input, set of 25 rules and single output i.e.
fused image. ANFIS contains adaptive networks with fuzzy rules.
55
73. Comparison of fusion algorithms based on conventional metrics for Case
1 to Case 5
73
74. Comparison of fusion algorithms based on objective metrics (Case1
to Case5)
74
Comparison of fusion algorithms based on objective metrics for Case 1 to
Case 5
75. Graph-1 Average Pixel Intensity (API) calculation. Graph-2 Average Gradient (AG) calculation.
75
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86
91. 91
• The research work included modeling of a system to fuse
the modalities, making it competent for analysis and
treatment.
• The verification includes mathematical calculation and
visual inspection by oncologist and radiation safety
officer.
• The experts validated the performance of the proposed
method in terms of presence of information, noise
removal and edge preservation
• In future, the performance of proposed method can be
upgraded by tuning the ANFIS.
• New metrics can be developed to measure the
performance of fusion methods.