This document provides a bibliography of 15 publications in peer-reviewed journals related to image reconstruction and computer-aided diagnosis completed by the author(s). The publications span the years 1993 to 2006 and cover topics such as image reconstruction from projections, time-frequency distribution inversion of the Radon transform, region-of-interest tomography, classification of breast masses and tissue, detection of microcalcifications, and evaluation of computer-aided diagnosis systems.
Thyroid Cancer Detection for Myanmar Review and Recommendationijtsrd
This document summarizes a research paper that reviewed methods for detecting thyroid cancer in Myanmar. It began by introducing the authors and journal publication details. It then provided background on thyroid cancer detection systems and reviewed several existing studies that used techniques like image processing, segmentation, classification and neural networks. The studies showed accuracy rates from 70-99% but had limitations like small datasets or lack of validation. The document compared the methods and recommended developing techniques tailored for Myanmar, including using deep learning neural networks, clustering, and collaborating with local hospitals to validate results. It concluded by stating future work could apply these recommended methods to a Myanmar-specific dataset involving radiologists to help thyroid cancer patients.
The Advantages of Two Dimensional Techniques (2D) in Pituitary Adenoma TreatmentIOSR Journals
The purpose of the study is to evaluate the two dimensional dose distribution techniques in pituitary adenoma patient treatment in order to provide 2D dose coverage to the target volume while sparing organs at risk (OARs). The CT simulator was used to radiograph 300 patients of pituitary adenomas to conform 2D dose distribution planning inside the tumour bed , and its structures were delineated; including gross target volume (GTV), clinical target volume (CTV), and planning target volume (PTV)], as well as organs at risks (OARs) . Dose distribution analysis was edited to provide 2D dose coverage to the target while sparing organs at risk. The main results of the study were, 2D dose distribution plans increases the unnecessary dose to the critical organs according to their geographical location from the pituitary adenoma site, and the present study , concludes that when the tumour dose increases from 45 to 55 Gy there is a linear proportional increment of dose to the organs at risks, and when the dose is about 60 Gy in 2D, the increment of unnecessary dose to temporal lobe is 0.31 Gy, and to eye is0.34Gy, and to optic chiasm is 0.42 Gy respectively .New techniques, which will lessen the unnecessary dose to OARs, needed to be developed .
Informativni model verjetnosti | An informative probability modelMIDEAS
An informative probability model enhancing real time
echobiometry to improve fetal weight estimation accuracy
G. Cevenini Æ F. M. Severi Æ C. Bocchi Æ
F. Petraglia Æ P. Barbini
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
Studies on the breast ultrasound technique showed that it is effective at detecting the difference between benign and cancerous tumors, and researchers recommend it as a "tool of choice" for evaluating palpable lumps in women under 40.
This article compared robot-assisted vasovasostomy (RAVV) and robot-assisted vasoepididymostomy (RAVE) to traditional microsurgical vasovasostomy (MVV) and microsurgical vasoepididymostomy (MVE) using data from 123 cases. RAVV achieved a higher patency rate of 96% compared to 80% for MVV. RAVV also had a shorter median operative duration of 90 minutes versus 120 minutes for MVV. Additionally, the sperm count recovery was significantly higher for RAVV than RAVE. The authors concluded that robotic techniques may offer advantages over traditional microsurgery for vasectomy reversal based on improved outcomes. However, further
Performance Evaluation using Supervised Learning Algorithms for Breast Cancer...IRJET Journal
The document discusses using supervised machine learning algorithms to evaluate the performance of breast cancer diagnosis. It evaluates algorithms like perceptron, cascade-forward backpropagation, and feed-forward backpropagation on a breast cancer dataset from the Wisconsin Breast Cancer Diagnosis database. The algorithms are used to develop a process for diagnosis and prediction of breast cancer that could help physicians diagnose the disease more accurately.
The editorial discusses how collaborations between academic medical centers and industry have advanced MRI through various examples in the magazine issue. It highlights how such partnerships have leveraged MRI's contrast mechanisms and improved image quality, examination speed, and patient comfort. The growing healthcare needs require continued diagnostic imaging advances. The issue features articles demonstrating innovations in motion-robust imaging, rapid protocols, quantitative mapping, and noise reduction.
Thyroid Cancer Detection for Myanmar Review and Recommendationijtsrd
This document summarizes a research paper that reviewed methods for detecting thyroid cancer in Myanmar. It began by introducing the authors and journal publication details. It then provided background on thyroid cancer detection systems and reviewed several existing studies that used techniques like image processing, segmentation, classification and neural networks. The studies showed accuracy rates from 70-99% but had limitations like small datasets or lack of validation. The document compared the methods and recommended developing techniques tailored for Myanmar, including using deep learning neural networks, clustering, and collaborating with local hospitals to validate results. It concluded by stating future work could apply these recommended methods to a Myanmar-specific dataset involving radiologists to help thyroid cancer patients.
The Advantages of Two Dimensional Techniques (2D) in Pituitary Adenoma TreatmentIOSR Journals
The purpose of the study is to evaluate the two dimensional dose distribution techniques in pituitary adenoma patient treatment in order to provide 2D dose coverage to the target volume while sparing organs at risk (OARs). The CT simulator was used to radiograph 300 patients of pituitary adenomas to conform 2D dose distribution planning inside the tumour bed , and its structures were delineated; including gross target volume (GTV), clinical target volume (CTV), and planning target volume (PTV)], as well as organs at risks (OARs) . Dose distribution analysis was edited to provide 2D dose coverage to the target while sparing organs at risk. The main results of the study were, 2D dose distribution plans increases the unnecessary dose to the critical organs according to their geographical location from the pituitary adenoma site, and the present study , concludes that when the tumour dose increases from 45 to 55 Gy there is a linear proportional increment of dose to the organs at risks, and when the dose is about 60 Gy in 2D, the increment of unnecessary dose to temporal lobe is 0.31 Gy, and to eye is0.34Gy, and to optic chiasm is 0.42 Gy respectively .New techniques, which will lessen the unnecessary dose to OARs, needed to be developed .
Informativni model verjetnosti | An informative probability modelMIDEAS
An informative probability model enhancing real time
echobiometry to improve fetal weight estimation accuracy
G. Cevenini Æ F. M. Severi Æ C. Bocchi Æ
F. Petraglia Æ P. Barbini
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
Studies on the breast ultrasound technique showed that it is effective at detecting the difference between benign and cancerous tumors, and researchers recommend it as a "tool of choice" for evaluating palpable lumps in women under 40.
This article compared robot-assisted vasovasostomy (RAVV) and robot-assisted vasoepididymostomy (RAVE) to traditional microsurgical vasovasostomy (MVV) and microsurgical vasoepididymostomy (MVE) using data from 123 cases. RAVV achieved a higher patency rate of 96% compared to 80% for MVV. RAVV also had a shorter median operative duration of 90 minutes versus 120 minutes for MVV. Additionally, the sperm count recovery was significantly higher for RAVV than RAVE. The authors concluded that robotic techniques may offer advantages over traditional microsurgery for vasectomy reversal based on improved outcomes. However, further
Performance Evaluation using Supervised Learning Algorithms for Breast Cancer...IRJET Journal
The document discusses using supervised machine learning algorithms to evaluate the performance of breast cancer diagnosis. It evaluates algorithms like perceptron, cascade-forward backpropagation, and feed-forward backpropagation on a breast cancer dataset from the Wisconsin Breast Cancer Diagnosis database. The algorithms are used to develop a process for diagnosis and prediction of breast cancer that could help physicians diagnose the disease more accurately.
The editorial discusses how collaborations between academic medical centers and industry have advanced MRI through various examples in the magazine issue. It highlights how such partnerships have leveraged MRI's contrast mechanisms and improved image quality, examination speed, and patient comfort. The growing healthcare needs require continued diagnostic imaging advances. The issue features articles demonstrating innovations in motion-robust imaging, rapid protocols, quantitative mapping, and noise reduction.
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction fo...CSCJournals
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
The document discusses the five modalities that make up the medical radiation technology field: nuclear medicine, radiation therapy, magnetic resonance, ultrasound, and radiological technology. It provides examples of the types of images and procedures performed by technologists in each specialty area. The document promotes careers in medical radiation technology and describes opportunities to work at a large, urban teaching hospital with state-of-the-art equipment.
This document discusses the use of deep learning to improve the diagnosis of breast cancer from pathology images. It describes a study where a deep learning model was trained on a large dataset of pathology slides to detect regions of breast cancer metastases. The model was able to detect cancer metastases with an accuracy of over 99%, significantly outperforming pathologists. It also reduced the time needed for analysis from hours to minutes. This demonstrates the potential for deep learning to help pathologists more accurately and efficiently diagnose cancer from digital pathology images.
1) A digital therapeutic called reSET was the first to receive FDA approval as a prescription digital treatment for substance abuse disorders like alcohol, cocaine, and marijuana addiction. Clinical trials showed patients using reSET had statistically significant increased odds of abstinence compared to a control group.
2) The study evaluated older adults ages 60-85 who played the multitasking video game NeuroRacer. Those who received multitasking training showed reduced multitasking costs compared to control groups, performing better than untrained 20-year-olds. The training also improved neural signatures of cognitive control and benefits extended to untrained cognitive abilities.
3) Digital therapeutics can deliver evidence-based treatments through software to prevent, manage
4D radiotherapy aims to adapt treatment plans based on organ and tumor motion over time. This requires 4D data management systems to record treatment delivery and portal images over time. Image processing tools like deformable registration and model-based segmentation can help automate identifying organ motion between 3D scans. Adaptive planning approaches could modify plans at intervals of multiple fractions, daily, or intra-fraction to account for changes. Determining if daily replanning is practical requires considering workload, data management, and the incremental clinical benefits versus costs.
Quantitative Image Analysis for Cancer Diagnosis and Radiation TherapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
A survey on enhancing mammogram image saradha arumugam academiaPunit Karnani
This document summarizes research on enhancing mammogram images to improve the detection of breast cancer. It discusses how mammogram images have low contrast and are noisy, making it difficult to identify microcalcifications that could indicate cancer. Various image enhancement techniques are reviewed that aim to improve contrast, reduce noise, and sharpen edges to make microcalcifications more visible. The techniques discussed include nonlinear unsharp masking, wavelet-based enhancement, adaptive contrast enhancement, and integrated wavelet decompositions. Evaluation of the techniques suggests they can improve cancer diagnosis by enhancing image details and increasing radiologist performance.
This document discusses digital pathology, which focuses on managing pathology data from digitized glass slides. Key points include:
- Glass slides are converted into high-resolution digital slides that can be viewed and analyzed on computer monitors.
- Digital pathology offers advantages like telepathology, improved accuracy and speed, and potential roles in clinical research.
- While the global digital pathology market is growing, limitations include high costs, lack of standards, and need for stable technology.
- Digital pathology could offer significant benefits in developing countries by reducing costs and improving patient care, but high equipment and software costs present challenges.
- As technology advances, digital pathology may become mainstream, but pathologists will still need microscope skills and issues around
1) The study developed a computational system called C-Path to automatically quantify over 6,600 morphological features from breast cancer epithelium and stroma in histology slides.
2) When applied to two independent patient cohorts (n=248 and n=328), a prognostic model based on the quantified features was strongly associated with patient survival, independent of other factors.
3) Three stromal features were significantly associated with survival, even more so than epithelial features, implicating tumor stroma morphology as a previously unrecognized prognostic factor for breast cancer.
Emerson et al-2013-journal_of_ultrasound_in_medicinelinhnguyen1927
Ultrasound imaging has not been fully integrated into PACS systems compared to other modalities like CT and MRI. A survey of ultrasound radiologists found that:
1) Only 53.2% rated their PACS experience for ultrasound as high, significantly lower than for CT (85.2%), MRI (84.4%), and radiography (83.2%).
2) Ultrasound-specific functions like displaying 3D volumes (0.9% rating) and managing data (29.8% rating) received much lower scores than basic functions like viewing black-and-white images (92% rating).
3) Most respondents felt ultrasound-specific functions needed to be implemented or improved in P
This seminar paper discusses radiation therapy and its use in cancer treatment. It defines radiation therapy and its goals, which include curing early-stage cancer, preventing metastasis, and treating symptoms from advanced cancer. The paper describes the mechanism of action of radiotherapy by explaining how it causes double-stranded DNA breaks in cells. It also outlines the different types of radiation therapy including photon and particle radiation. Additionally, it discusses the principles of radiation therapy such as precisely locating the tumor, immobilizing the patient, accurately aiming the radiation beams, shaping the beams, and delivering an optimal therapeutic dose.
computer aided detection of pulmonary nodules in ct scansWookjin Choi
The document discusses computer aided detection of pulmonary nodules in CT scans. It introduces lung cancer as a major health problem and describes how detecting nodules early can improve survival rates. It then provides an overview of pulmonary nodule detection CAD systems, describing their general structure and evaluating various approaches in the literature. Key contributions are genetic programming and shape-based classifiers and a hierarchical block analysis method that achieved high performance on a publicly available lung image database.
Microwave imaging shows promise for breast cancer screening by taking advantage of the dielectric property differences between normal and malignant breast tissues. A microwave imaging system was developed at Dartmouth that incorporates patient anatomical information from MRI to improve the spatial resolution of the reconstructed microwave images. Initial phantom and clinical studies demonstrate that including structural information enhances the ability to detect and characterize abnormalities. Further research is still needed including bilateral breast imaging and customized MRI coils to enable viable 3D microwave imaging.
Hunting for Elusive Targets, Markers, Signals & PathwaysJim Smurro
This document summarizes Jim Smurro's presentation at MassBio in 2013 on hunting for elusive targets, markers, signals, and pathways with various imaging and data analysis techniques.
The presentation discusses using multi-modal, multi-scalar imaging and data integration through visual neural networking and clinical cognitive vismemes. This allows for fusion of anatomic, cellular, and molecular imaging with other omics data streams. Tools like computer-assisted detection and augmented pattern recognition can further enhance analysis by multi-disciplinary expert teams.
Shared semantic tags and annotated images enriched with quantitative data can be exchanged over collaborative networks. This facilitates earlier detection, diagnosis, treatment and monitoring of chronic disease by combining phenotypic and genomic information for personalized precision
This document provides a summary of Chenguang Wang's background and qualifications. It includes his contact information, academic training, professional experience, published articles, statistical software developed, book chapters, and recent presentations. Wang received his Ph.D. in Statistics from the University of Florida in 2010. He is currently an Assistant Professor at Johns Hopkins University with a research focus on Bayesian methods for clinical trials and missing data problems.
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction fo...CSCJournals
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
The document discusses the five modalities that make up the medical radiation technology field: nuclear medicine, radiation therapy, magnetic resonance, ultrasound, and radiological technology. It provides examples of the types of images and procedures performed by technologists in each specialty area. The document promotes careers in medical radiation technology and describes opportunities to work at a large, urban teaching hospital with state-of-the-art equipment.
This document discusses the use of deep learning to improve the diagnosis of breast cancer from pathology images. It describes a study where a deep learning model was trained on a large dataset of pathology slides to detect regions of breast cancer metastases. The model was able to detect cancer metastases with an accuracy of over 99%, significantly outperforming pathologists. It also reduced the time needed for analysis from hours to minutes. This demonstrates the potential for deep learning to help pathologists more accurately and efficiently diagnose cancer from digital pathology images.
1) A digital therapeutic called reSET was the first to receive FDA approval as a prescription digital treatment for substance abuse disorders like alcohol, cocaine, and marijuana addiction. Clinical trials showed patients using reSET had statistically significant increased odds of abstinence compared to a control group.
2) The study evaluated older adults ages 60-85 who played the multitasking video game NeuroRacer. Those who received multitasking training showed reduced multitasking costs compared to control groups, performing better than untrained 20-year-olds. The training also improved neural signatures of cognitive control and benefits extended to untrained cognitive abilities.
3) Digital therapeutics can deliver evidence-based treatments through software to prevent, manage
4D radiotherapy aims to adapt treatment plans based on organ and tumor motion over time. This requires 4D data management systems to record treatment delivery and portal images over time. Image processing tools like deformable registration and model-based segmentation can help automate identifying organ motion between 3D scans. Adaptive planning approaches could modify plans at intervals of multiple fractions, daily, or intra-fraction to account for changes. Determining if daily replanning is practical requires considering workload, data management, and the incremental clinical benefits versus costs.
Quantitative Image Analysis for Cancer Diagnosis and Radiation TherapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
A survey on enhancing mammogram image saradha arumugam academiaPunit Karnani
This document summarizes research on enhancing mammogram images to improve the detection of breast cancer. It discusses how mammogram images have low contrast and are noisy, making it difficult to identify microcalcifications that could indicate cancer. Various image enhancement techniques are reviewed that aim to improve contrast, reduce noise, and sharpen edges to make microcalcifications more visible. The techniques discussed include nonlinear unsharp masking, wavelet-based enhancement, adaptive contrast enhancement, and integrated wavelet decompositions. Evaluation of the techniques suggests they can improve cancer diagnosis by enhancing image details and increasing radiologist performance.
This document discusses digital pathology, which focuses on managing pathology data from digitized glass slides. Key points include:
- Glass slides are converted into high-resolution digital slides that can be viewed and analyzed on computer monitors.
- Digital pathology offers advantages like telepathology, improved accuracy and speed, and potential roles in clinical research.
- While the global digital pathology market is growing, limitations include high costs, lack of standards, and need for stable technology.
- Digital pathology could offer significant benefits in developing countries by reducing costs and improving patient care, but high equipment and software costs present challenges.
- As technology advances, digital pathology may become mainstream, but pathologists will still need microscope skills and issues around
1) The study developed a computational system called C-Path to automatically quantify over 6,600 morphological features from breast cancer epithelium and stroma in histology slides.
2) When applied to two independent patient cohorts (n=248 and n=328), a prognostic model based on the quantified features was strongly associated with patient survival, independent of other factors.
3) Three stromal features were significantly associated with survival, even more so than epithelial features, implicating tumor stroma morphology as a previously unrecognized prognostic factor for breast cancer.
Emerson et al-2013-journal_of_ultrasound_in_medicinelinhnguyen1927
Ultrasound imaging has not been fully integrated into PACS systems compared to other modalities like CT and MRI. A survey of ultrasound radiologists found that:
1) Only 53.2% rated their PACS experience for ultrasound as high, significantly lower than for CT (85.2%), MRI (84.4%), and radiography (83.2%).
2) Ultrasound-specific functions like displaying 3D volumes (0.9% rating) and managing data (29.8% rating) received much lower scores than basic functions like viewing black-and-white images (92% rating).
3) Most respondents felt ultrasound-specific functions needed to be implemented or improved in P
This seminar paper discusses radiation therapy and its use in cancer treatment. It defines radiation therapy and its goals, which include curing early-stage cancer, preventing metastasis, and treating symptoms from advanced cancer. The paper describes the mechanism of action of radiotherapy by explaining how it causes double-stranded DNA breaks in cells. It also outlines the different types of radiation therapy including photon and particle radiation. Additionally, it discusses the principles of radiation therapy such as precisely locating the tumor, immobilizing the patient, accurately aiming the radiation beams, shaping the beams, and delivering an optimal therapeutic dose.
computer aided detection of pulmonary nodules in ct scansWookjin Choi
The document discusses computer aided detection of pulmonary nodules in CT scans. It introduces lung cancer as a major health problem and describes how detecting nodules early can improve survival rates. It then provides an overview of pulmonary nodule detection CAD systems, describing their general structure and evaluating various approaches in the literature. Key contributions are genetic programming and shape-based classifiers and a hierarchical block analysis method that achieved high performance on a publicly available lung image database.
Microwave imaging shows promise for breast cancer screening by taking advantage of the dielectric property differences between normal and malignant breast tissues. A microwave imaging system was developed at Dartmouth that incorporates patient anatomical information from MRI to improve the spatial resolution of the reconstructed microwave images. Initial phantom and clinical studies demonstrate that including structural information enhances the ability to detect and characterize abnormalities. Further research is still needed including bilateral breast imaging and customized MRI coils to enable viable 3D microwave imaging.
Hunting for Elusive Targets, Markers, Signals & PathwaysJim Smurro
This document summarizes Jim Smurro's presentation at MassBio in 2013 on hunting for elusive targets, markers, signals, and pathways with various imaging and data analysis techniques.
The presentation discusses using multi-modal, multi-scalar imaging and data integration through visual neural networking and clinical cognitive vismemes. This allows for fusion of anatomic, cellular, and molecular imaging with other omics data streams. Tools like computer-assisted detection and augmented pattern recognition can further enhance analysis by multi-disciplinary expert teams.
Shared semantic tags and annotated images enriched with quantitative data can be exchanged over collaborative networks. This facilitates earlier detection, diagnosis, treatment and monitoring of chronic disease by combining phenotypic and genomic information for personalized precision
This document provides a summary of Chenguang Wang's background and qualifications. It includes his contact information, academic training, professional experience, published articles, statistical software developed, book chapters, and recent presentations. Wang received his Ph.D. in Statistics from the University of Florida in 2010. He is currently an Assistant Professor at Johns Hopkins University with a research focus on Bayesian methods for clinical trials and missing data problems.
The Clinical use of Artificial Intelligence in the Analysis of Chest Radiogra...semualkaira
Computer-assisted detection (CAD) systems
based on artificial intelligence (AI) utilizing convolutional neural
networks (CNN) have demonstrated successful outcomes in diagnosing lung lesions in several studies. This study aims to report
the clinical implication in a real clinical practice, demonstrating
efficacy in commonly encountered cases and categorizing the lung
nodules that they can effectively detect.
The Clinical use of Artificial Intelligence in the Analysis of Chest Radiogra...semualkaira
Computer-assisted detection (CAD) systems
based on artificial intelligence (AI) utilizing convolutional neural
networks (CNN) have demonstrated successful outcomes in diagnosing lung lesions in several studies. This study aims to report
the clinical implication in a real clinical practice, demonstrating
efficacy in commonly encountered cases and categorizing the lung
nodules that they can effectively detect.
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.
Performance enhancement of machine learning algorithm for breast cancer diagn...IJECEIAES
Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.
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.
IRJET- Breast Cancer Detection from Histopathology Images: A ReviewIRJET Journal
This document provides a review of techniques for detecting breast cancer from histopathology images. It discusses how histopathology examines tissue samples under a microscope to study diseases at a microscopic level. Detecting cell nuclei is an important first step, as is identifying mitosis (cell division) and metastasis (cancer spreading). The document reviews several techniques that use convolutional neural networks to automatically analyze histopathology images and detect breast cancer, including techniques for nuclei detection and segmentation. These automatic methods aim to assist pathologists by improving efficiency and reducing human error compared to manual analysis.
CNN applications in medical image analysis have grown significantly. CNNs have been used for tasks like medical image segmentation, classification, detection, and retrieval. Challenges remain around data availability, model interpretability, and generalizability to new domains and pathologies. Continued research is working to address these challenges through improved network architectures, training methods, and incorporation of domain expertise.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
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Discussion Questions
1. Of the levels of theory discussed in this chapter, what level would be most appropriate for evaluation of electronic health records? Would the level of theory be different if the intervention was for an application targeting a new scheduling system in a clinic? Why?
2. What is the difference between program evaluation.
Precision Radiotherapy: Tailoring Treatment for Individualised Cancer Care.pptxDr. Rituparna Biswas
Precision radiotherapy, also known as precision radiation therapy or targeted radiotherapy, is a cutting-edge approach in the field of radiation oncology that aims to deliver highly focused and accurate doses of radiation to cancerous cells while minimizing damage to surrounding healthy tissues.
Precision Radiotherapy: Tailoring Treatment for Individualised Cancer Care.pptx
Journal Papers
1. BIBLIOGRAPHY
Completed publications in peer-reviewed journals
[1] Sahiner B, Yagle AE, "Image reconstruction from projections under wavelet constraints,"
IEEE Trans Signal Proc., 1993, 41:3579-3584.
[2] Sahiner B, Yagle AE, "A fast algorithm for backprojection with linear interpolation,"
IEEE Trans Image Proccess, 1993, 2:547-550.
[3] Sahiner B, Yagle AE, "Time-frequency distribution inversion of the Radon transform,"
IEEE Trans Image Process, 1993, 2:539-543.
[4] Sahiner B, Yagle AE, "Reconstruction from projections under time-frequency
constraints," IEEE Trans Med Imaging, 1995, 14:193-204.
[5] Sahiner B, Yagle AE, "Region-of-interest tomography using exponential radial sampling,
"IEEE Trans Image Process, 1995, 4:1120-1127.
[6] Chan HP, Wei D, Helvie MA, Sahiner B, Adler DD, Goodsitt MM, Petrick N,
"Computer-aided classification of mammographic masses and normal tissue: Linear
discriminant analysis in texture feature space," Phys Med Biol, 1995, 40:857-876.
[7] Wei D, Chan HP, Helvie MA, Sahiner B, Petrick N, Adler DD, Goodsitt MM,
"Classification of mass and normal breast tissue on digital mammograms: Multiresolution
texture analysis," Med Phys, 1995, 22:1501-1513.
[8] Chan HP, Lo SCB, Sahiner B, Lam KL, Helvie MA, "Computer-aided detection of
mammographic microcalcifications: Pattern recognition with an artificial neural
network," Med Phys, 1995, 22:1555-1567.
[9] Petrick N, Chan HP, Sahiner B, Wei D, "An adaptive density weighted contrast
enhancement filter for mammographic breast mass detection," IEEE Trans Med Imaging,
1996, 15:59-68.
[10] Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA, Adler DD, Goodsitt MM, "Image
feature selection by a genetic algorithm: Application to classification of mass and normal
breast tissue on mammograms," Med Phys, 1996, 23:1671-1684.
[11] Petrick N, Chan HP, Wei D, Sahiner B, Helvie MA, Adler DD, "Automated detection of
breast masses on mammograms using adaptive contrast enhancement and tissue
classification," Med Phys, 1996, 23:1685-1696.
[12] Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA, Adler DD, Goodsitt MM,
"Classification of mass and normal breast tissue: A convolution neural network classifier
with spatial domain and texture images," IEEE Trans Med Imaging, 1996, 15:598-610.
[13] Chan HP, Sahiner B, Petrick N, Helvie MA, Lam KL, Adler DD, Goodsitt MM,
"Computerized classification of malignant and benign microcalcifications on
mammograms: texture analysis using an artificial neural network," Phys Med Biol, 1997,
42:549-567.
[14] Wei D, Chan HP, Petrick N, Sahiner B, Helvie MA, Adler DD, Goodsitt MM, "False-
positive reduction technique for detection of masses on digital mammograms: global and
local multiresolution texture analysis." Med Phys, 1997, 24:903-914.
[15] Sahiner B, Chan HP, Petrick N, Helvie MA, Goodsitt MM, "Design of a high-sensitivity
classifier based on a genetic algorithm: Application to computer-aided diagnosis," Phys
Med Biol, 1998, 43:2853-2871.
Rev: 06/01/2016
2. [16] Chan HP, Sahiner B, Lam KL, Petrick N, Helvie MA, Goodsitt MM, Adler DD,
"Computerized analysis of mammographic microcalcifications in morphological and
texture feature spaces," Med Phys, 1998, 25:2007-2019.
[17] Sahiner B, Chan HP, Petrick N, Helvie MA, Goodsitt MM, "Computerized
characterization of masses on mammograms: the rubber band straightening transform and
texture analysis," Med Phys, 1998, 25:516-526.
[18] Chan HP, Sahiner B, Helvie MA, Petrick N, Roubidoux MA, Wilson TE, Adler DD,
Paramagul C, Newman JS, Gopal SS, “Improvement of radiologists’ characterization of
mammographic masses by computer-aided diagnosis: an ROC study,” Radiology, 1999,
212:817-827.
[19] Petrick N, Chan HP, Sahiner B, Helvie MA, “Combined adaptive enhancement and
region-growing segmentation of breast masses on digitized mammograms,” Med Phys,
1999, 26:1642:1654.
[20] Chan HP, Sahiner B, Wagner RF, Petrick N, “Classifier design for computer-aided
diagnosis: Effects of finite sample size on the mean performance of classical and neural
network classifiers,” Med Phys, 1999, 26:2654:2668.
[21] Gopal SS, Chan HP, Wilson TE, Helvie MA, Petrick N, Sahiner B, “A regional
registration technique for automated interval change analysis of breast lesions on
mammograms,” Med Phys, 1999, 26:2669:2679.
[22] Hadjiiski LM, Sahiner B, Chan HP, Petrick N, Helvie MA, “Classification of malignant
and benign masses based on hybrid ART2LDA approach,” IEEE Trans Med Imaging,
1999, 18: 1178-1187.
[23] Sahiner B, Chan HP, Petrick N, Wagner RF, Hadjiiski LM, “Feature selection and
classifier performance in computer-aided diagnosis: The effect of finite sample size,”
Med Phys, 2000, 27: 1509-1522.
[24] Hadjiiski LM, Chan HP, Sahiner B, Petrick N, Helvie MA, “Automated registration of
breast lesions in temporal pairs of mammograms for interval change analysis—local
affine transformation for improved localization,” Med Phys, 2001, 28(6): 1070-1079.
[25] Zhou C, Chan HP, Petrick N, Helvie MA, Goodsitt MM, Sahiner B, Hadjiiski LM,
“Computerized image analysis: Estimation of breast density on mammograms,” Med
Phys, 2001, 28(6): 1056-1069.
[26] Chan HP, Helvie MA, Petrick N, Sahiner B, Adler DD, Paramagul C, Roubidoux MA,
Blane CE, Joynt LK, Wilson TE, Hadjiiski LM, Goodsitt MM, “Digital mammography:
Observer performance study of effects of pixel size on radiologists' characterization of
malignant and benign microcalcifications,” Acad Radiol, 2001, 8:454-466.
[27] Sahiner B, Chan HP, Petrick N, Helvie MA, Hadjiiski LM, “Improvement of
mammographic mass characterization using spiculation measures and morphological
features,” Med Phys, 2001, 28(7): 1455-1465.
[28] Gurcan MN, Sahiner B, Chan HP, Hadjiiski LM, Petrick N, “Selection of an optimal
neural network architecture for computer-aided diagnosis: Comparison of automated
optimization techniques,” Med Phys, 2001, 28(9): 1937-1948.
[29] Sahiner B, Petrick N, Chan HP, Hadjiiski LM, Paramagul C, Helvie MA, Gurcan MN,
“Computer-aided characterization of mammographic masses: Accuracy of mass
segmentation and its effects on characterization,” IEEE Trans Med Imaging, 2001,
20(12): 1275-1284.
[30] Hadjiiski LM, Sahiner B, Chan HP, Petrick N, Helvie MA, Gurcan MN, “Analysis of
temporal changes of mammographic features: Computer-aided classification of malignant
and benign breast masses,” Med Phys, 2001, 28(11): 2309-2317.
Rev: 06/01/2016
3. [31] Paquerault S, Petrick N, Chan HP, Sahiner B, Helvie MA, “Improvement of
computerized mass detection on mammograms: Fusion of two-view information,” Med
Phys, 2002, 29(2): 238-247.
[32] Gurcan MN, Chan HP, Sahiner B, Hadjiiski LM, Petrick N, Helvie MA, “Optimal neural
network architecture selection: Improvement in computerized detection of
microcalcifications,” Acad Radiol, 2002, 9:420-429.
[33] Petrick N, Sahiner B, Chan HP, Helvie MA, Paquerault S, Hadjiiski LM, “Breast cancer
detection: Evaluation of a CAD mass detection algorithm with independent
mammographic cases,” Radiology, 2002, 224(1): 217-224.
[34] Gurcan MN, Sahiner B, Petrick N, Chan HP, Kazerooni EA, Cascade PN, Hadjiiski LM,
"Lung nodule detection on thoracic computed tomography images - Preliminary
evaluation of a computer-aided diagnosis system,” Med Phys, 2002, 29(11): 2552-2558.
[35] Chan HP, Goodsitt MM, Hadjiiski LM, Bailey JE, Klein K, Darner K, Sahiner B, "Effects
of magnification and zooming on depth perception in digital stereomammography: An
observer performance study" Phys Med Biol, 2003, 48(22): 3721-3734.
[36] Chan HP, Sahiner B, Hadjiiski LM, Petrick N. “Effect of imaging parameters on
computer analysis of mammograms,” Seminars in Breast Imaging, 2003; 5: 211-216.
[37] Sahiner B, Chan HP, Roubidoux MA, Helvie MA, Hadjiiski LM, Ramachandran A,
LeCarpentier GL, Nees A, Paramagul C, Blane CE, "Computerized characterization of
breast masses on 3-D ultrasound volumes," Med Phys, 2004, 31(4): 744–754.
[38] Wei J, Chan HP, Helvie MA, Roubidoux MA, Sahiner B, Hadjiiski LM, Zhou C,
Paquerault S, Chenevert T, Goodsitt MM, "Correlation between mammographic density
and volumetric fibroglandular tissue estimated on breast MR images," Med Phys, 2004,
31(4): 933–942.
[39] Helvie MA, Hadjiiski LM, Makariou E, Chan HP, Petrick N, Sahiner B, Lo SCB,
Freedman M, Adler D, Bailey J, Blane C, Hoff D, Hunt K, Joynt L, Klein K, Paramagul
C, Patterson S, Roubidoux MA, "A non-commercial CAD system for breast cancer
detection in screening mammograms achieves high sensitivity – A pilot clinical trial,"
Radiology 2004, 231: 208-214.
[40] Dodd LE, Wagner RF, Armato SG, McNitt-Gray MF, Beiden S, Chan HP, Gur D.
McLennan G, Metz CE, Petrick N, Sahiner B, Sayre J, “Assessment methodologies and
statistical issues for computer-assisted detection of lung nodules in computed
tomography: contemporary research topics relevant to the Lung Image Database
Consortium,” Academic Radiology, 2004, 11: 462-475.
[41] Goodsitt MM, Chan HP, Lydick JT, Gandra CR, Chen NG, Helvie MA, Bailey J,
Roubidoux MA, Paramagul C, Blane CE, Sahiner B, Petrick NA, “An observer study
comparing spot imaging regions selected by radiologists and a computer for an
automated stereo spot mammography technique,” Med Phys, 2004 31:1558-1567.
[42] Zhou C, Chan HP, Paramagul C, Roubidoux MA, Sahiner B, Hadjiiski LM, Petrick N,
“Computerized nipple identification for multiple image analysis in computer-aided
diagnosis,” Med Phys, 2004, 31: 2871-2882.
[43] Hadjiiski L, Chan HP, Sahiner B, Helvie MA, Roubidoux MA, Blane C, Paramagul C,
Petrick N, Bailey J, Klein K, Foster M, Patterson S, Adler D, Nees A, Shen J,
“Improvement of radiologists’ characterization of malignant and benign breast masses in
serial mammograms by computer-aided diagnosis: An ROC study,” Radiology, 2004,
233:255-265.
Rev: 06/01/2016
4. [44] Filev P, Hadjiiski LM, Sahiner B, Chan HP, Helvie MA, “Comparison of similarity
measures for the task of template matching of masses on serial mammograms,” Med
Phys, 2005, 32:515-529.
[45] Chan HP, Goodsitt MM, Helvie MA, Hadjiiski LM, Lydick JT, Roubidoux MA, Bailey
JE, Nees A, Blane CE, Sahiner B, “ROC study of the effect of stereoscopic imaging on
assessment of breast lesions,” Med Phys, 2005, 32: 1001-1009.
[46] Zhou C, Chan HP, Patel S, Cascade PN, Sahiner B, Hadjiiski L, Kazerooni EA,
“Preliminary Investigation of Computer-aided diagnosis of Pulmonary Embolism in 3D
Computed Tomographic Pulmonary Angiography (CTPA) Images,” Academic Radiology
2005, 12: 782-792.
[47] Ge Z, Sahiner B, Chan HP, Hadjiiski L, Cascade PN, Bogot N, Kazerooni EA, Wei J,
Zhou C, “Computer aided detection of lung nodules: False positive reduction using a 3D
gradient field method and 3D ellipsoid fitting,” Med Phys 2005, 32: 2443-2454.
[48] Wei J, Sahiner B, Hadjiiski L, Chan HP, Petrick N, Helvie MA, Roubidoux MA, Ge J,
Zhou C, “Computer Aided Detection of Breast Masses on Full Field Digital
Mammograms,” Med Phys 2005, 32: 2827-2838.
[49] Chan HP, Wei J, Sahiner B, Rafferty EA, Wu T, Roubidoux MA, Moore RH, Kopans
DB, Hadjiiski L, Helvie MA, “Computer-aided detection system for breast masses on
digital tomosynthesis mammograms – Preliminary experience,” Radiology, 2005, 237:
1075-1080.
[50] Hadjiiski L, Sahiner B, Chan HP, “Advances in computer-aided diagnosis for breast
cancer,” Current Opinion in Obstetrics and Gynecology 2006, 18:64–70.
[51] Sahiner B, Chan HP, Hadjiiski LM, Helvie MA, Paramagul C, Ge J, Wei J, Zhou C,
“Joint two-view information for computerized detection of microcalcifications on
mammograms,” Medical Physics 2006, 33: 2574-2585.
[52] Way TW, Hadjiiski LM, Sahiner B, Chan HP, Cascade PN, Kazerooni EA, Bogot N,
Zhou C, “Computer-aided Diagnosis of Pulmonary Nodules on CT scans: Segmentation
and Classification Using 3D Active Contours” Medical Physics 2006, 33: 2323:2337.
[53] Ge J, Sahiner B, Hadjiiski LM, Chan HP, Wei J, Helvie MA, Zhou C, “Computer Aided
Detection of Clusters of Microcalcifications on Full Field Digital Mammograms,”
Medical Physics 2006, 33: 2975-2988.
[54] Hadjiiski L, Sahiner B, Helvie MA, Chan HP, Roubidoux MA, Paramagul C, Blane C,
Petrick N, Bailey J, Klein K, Foster M, Patterson S, Adler D, Nees A, Shen J, “Computer-
Aided Diagnosis of Breast Cancer in Serial Mammograms,” Radiology 2006, 240: 343-
356.
[55] Zhang Y, Chan HP, Sahiner B, Wei J, Goodsitt MM, Hadjiiski LM, Ge J, Zhou C, “A
comparative study of limited-angle cone-beam reconstruction methods for breast
tomosynthesis,” Medical Physics 2006, 33:3781-3795.
[56] Wei J, Chan HP, Sahiner B, Hadjiiski LM, Helvie MA, Roubidoux MA, Zhou C, Ge J,
“Dual system approach to computer-aided detection of breast masses on mammograms,”
Medical Physics 2006, 33:4157-4168.
[57] Hadjiiski LM, Chan HP, Sahiner B, Helvie MA, Roubidoux MA, “Quasi-Continuous and
Discrete Confidence Rating Scales for Observer Performance Studies: Effects on ROC
Analysis”, Academic Radiology 2007, 14:38–48.
Rev: 06/01/2016
5. [58] Sahiner B, Chan HP, Roubidoux MA, Hadjiiski L, Helvie MA, Paramagul C, Bailey J,
Nees A, Blane C, “Computer-Aided Diagnosis of Malignant and Benign Breast Masses in
3D Ultrasound Volumes: Effect on Radiologists' Accuracy,” Radiology 2007, 242:716-
724.
[59] Ge J, Hadjiiski LM, Sahiner B, Chan HP, Wei J, Helvie MA, Zhou C, “Computer aided
detection system for clustered microcalcifications: Comparison of performance on full
field digital mammograms and digitized screen-film mammograms,” Physics in Medicine
and Biology 2007, 52:981:1000.
[60] Shi J, Sahiner B, Chan HP, Hadjiiski L, Zhou C, Cascade PN, Bogot N, Kazerooni EA,
Wu YT, Wei J, “Pulmonary Nodule Registration in Serial CT Scans Based on Rib
Anatomy and Nodule Template Matching,” Medical Physics 2007, 34:1336-1347.
[61] Yoon HJ, Zheng B, Sahiner B, Chakraborty DP, “Evaluating computer aided detection
(CAD) algorithms,” Medical Physics 2007, 34:2024:2038.
[62] Wei J, Hadjiiski L, Sahiner B, Chan HP, Ge J, Roubidox MA, Helvie MA, Zhou C, Wu
YT, Zhang Y, “Computer Aided Detection Systems for Breast Masses: Comparison of
Performances on Full-Field Digital Mammograms and Digitized Screen-film
Mammograms,” Academic Radiology 2007, 14:659-669.
[63] Zhang Y, Chan HP, Sahiner B, Wu YT, Zhou C, Ge J, Wei J, Hadjiiski LM, “Application
of boundary detection information in breast tomosynthesis reconstruction,” Medical
Physics 2007, 34:3603-3613.
[64] Wu YT, Wei J, Hadjiiski LM, Sahiner B, Zhou C, Ge J, Shi J, Zhang Y, Chan HP,
“Bilateral mammogram based computer-aided detection system for false positive
reduction,” Medical Physics 2007, 34: 3334-3344.
[65] Zhou C, Chan HP, Sahiner B, Hadjiiski LM, Chugtai A, Patel S, Wei J, Ge J, Cascade
PN, Kazerooni EA, “Automatic multiscale enhancement and hierarchical segmentation of
pulmonary vessels in CT pulmonary angiography (CTPA) images for CAD applications”,
Medical Physics 2007, 34: 4567-4577.
[66] Street E, Hadjiiski LM, Sahiner B, Gujar S, Ibrahim M, Mukherji SK, Chan HP,
“Automated Volume analysis of head and neck lesions on CT scans using 3D level set
segmentation,” Medical Physics 2007, 34: 4399-4408.
[67] Shi J, Sahiner B, Chan HP, Ge J, Hadjiiski LM, Helvie MA, Nees A, Wu YT, Wei J,
Zhou C, Zhang Y, Cui J, “Characterization of Mammographic Masses Based on Level
Set Segmentation with New Image Features and Patient Information,” Medical Physics
2008, 35: 280-290.
[68] Sahiner B, Chan HP, Hadjiiski LM, “Performance analysis of 3-class classifiers:
Properties of the 3D ROC surface and the normalized volume under the surface for the
ideal observer,” IEEE Transactions on Medical Imaging 2008, 27: 215-227.
[69] Sahiner B, Chan HP, Hadjiiski LM, “Classifier Performance Estimation Under the
Constraint of a Finite Sample Size: Resampling Schemes Applied to Neural Network
Classifiers,” Neural Networks 2008, 21: 476–483.
[70] Sahiner B, Chan HP, Hadjiiski LM, “Classifier performance prediction for computer-
aided diagnosis using a limited data set,” Medical Physics 2008, 35:1559-1570.
[71] Chan HP, Hadjiiski LM, Zhou C, Sahiner B, “Computer-Aided Diagnosis of Lung Cancer
and Pulmonary Embolism in Computed Tomography – A Review,” Academic Radiology
2008, 15: 535-555.
Rev: 06/01/2016
6. [72] Way TW, Chan HP, Goodsitt MM, Sahiner B, Hadjiiski LM, Zhou C, Chughtai A,
“Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by
3D active contour segmentation: a phantom study,” Physics in Medicine and Biology
2008, 53: 1295-1312.
[73] Chan HP, Wei J, Zhang Y, Helvie MA Moore RH, Sahiner B, Hadjiiski L, Kopans DB,
“Computer-aided detection of masses in digital tomosynthesis mammography:
Comparison of three approaches,” Medical Physics 2008, 35: 4087-4095.
[74] Filev P, Hadjiiski L, Chan HP, Sahiner B, Ge J, Helvie MA, Roubidoux MA, Zhou C,
“Automated regional registration and characterization of corresponding
microcalcification clusters on temporal pairs of mammograms for interval change
analysis,” Medical Physics 2008, 35: 5340-5350.
[75] Cui J, Sahiner B, Chan HP, Nees AV, Paramagul C, Hadjiiski L, Zhou C, Shi J, “A new
automated method for the segmentation and characterization of breast masses on
ultrasound images,” Medical Physics 2009, 36:1553-1565.
[76] Sahiner B, Chan HP, Hadjiiski LM, Roubidoux MA, Paramagul C, Bailey JE, Nees AV,
Blane CE, Adler DD, Patterson SK, Klein KA, Pinsky RW, Helvie MA, ”Multi-modality
CADx: ROC study of the effect on radiologists' accuracy in characterizing breast masses
on mammograms and 3D ultrasound images,” Academic Radiology 2009, 16:810–818.
[77] Zhang Y, Chan HP, Sahiner B, Wei J, Zhou C, Hadjiiski L, “Artifact reduction methods
for truncated projections in iterative breast tomosynthesis reconstruction,” Journal of
Computer Assisted Tomography 2009, 33:426-435.
[78] Way T, Sahiner B, Chan HP, Hadjiiski L, Cascade PN, Chughtai A, Bogot N, Kazerooni
E, “Computer aided diagnosis of pulmonary nodules on CT scans: Improvement of
classification performance with nodule surface features,” Medical Physics 2009, 36:3086-
3098.
[79] Zhou C, Chan HP, Sahiner B, Hadjiiski LM, Chughtai AR, Patel S, Wei J, Cascade PN,
Kazerooni EA, “Computer-aided detection of pulmonary embolism in computed
tomographic pulmonary angiography (CTPA): Performance evaluation with independent
data sets,” Medical Physics 2009, 36:3385-3396.
[80] Wei J, Chan HP, Sahiner B, Zhou C, Hadjiiski L, Roubidoux MA, Helvie MA,
“Computer-aided detection of breast masses on mammograms: Dual system approach
with two-view analysis,” Medical Physics 2009, 36:4451-4460.
[81] Shi J, Sahiner B, Chan HP, Paramagul C, Hadjiiski LM, Helvie MA, Chenevert TL,
“Treatment response assessment of breast masses on dynamic contrast-enhanced
magnetic resonance scans using fuzzy c-means clustering and level set segmentation,”
Medical Physics 2009, 36:5052-5063.
[82] Sahiner B, Chan HP, Hadjiiski LM, Cascade PN, Kazerooni EA, Chughtai AR, Poopat C,
Song T, Frank L, Stojanovska J, Attili A, “Effect of CAD on radiologists’ detection of
lung nodules on thoracic CT scans: Analysis of an observer performance study by nodule
size” Academic Radiology 2009, 16:1518-1530.
[83] Wu YT, Zhou C, Chan HP, Paramagul C, Zhang Y, Hadjiiski LM, Sahiner B, Shi J, Wei
J, “Dynamic multiple thresholding breast boundary detection algorithm for
mammograms,” Medical Physics 2010, 37:391-401.
[84] Way T, Sahiner B, Hadjiiski LM, Chan HP, “Effect of finite sample size on feature
selection and classification: a simulation study,” Medical Physics 2010, 37:907-920.
Rev: 06/01/2016
7. [85] Way T, Chan HP, Hadjiiski L, Sahiner B, Chugtai A, Song TK, Poopat C, Stojanovska J,
Frank L, Attili A, Bogot N, Cascade PN, Kazerooni EA, “Computer-aided diagnosis of
lung nodules on CT scans: An observer study of its effect on radiologists’ performance,”
Academic Radiology 2010, 17:323-332.
[86] Hadjiiski L, Mukherji SK, Gujar S, Sahiner B, Ibrahim M, Street E, Moyer J, Chan HP,
“Head and neck cancers on CT: preliminary study of treatment response assessment
based on computerized volume analysis,” American Journal of Roentgenology 2010,
194:1083-1089.
[87] Zhou C, Wei J, Chan HP, Paramagul C, Hadjiiski LM, Sahiner B, Douglas J,
“Computerized image analysis: Texture-field orientation method for pectoral muscle
identification on MLO-view mammograms,” Medical Physics 2010, 37:2289-2299.
[88] Chan HP, Wu YT, Sahiner B, Wei J, Helvie MA, Zhang Y, Moore RH, Kopans DB,
Hadjiiski LM, Way T, “Characterization of masses in digital breast tomosynthesis:
comparison of machine learning in projection views and reconstructed slices,” Medical
Physics 2010, 37: 3576-3586.
[89] Hadjiiski LM, Mukherji SK, Gujar S, Sahiner B, Ibrahim M, Street E, Moyer J, Worden
FP, Chan HP, “Treatment response assessment of head and neck cancers on CT using
computerized volume analysis,” American Journal of Neuroradiology 2010, 31:1744-
1751.
[90] Cho, HC, Hadjiiski LM, Sahiner B, Chan HP, Helvie MA, Paramagul C, Nees AV,
“Similarity evaluation in a content-based image retrieval (CBIR) CADx system for
characterization of breast masses on ultrasound images,” Medical Physics 2011, 38:
1820-1831.
[91] Wei J, Chan HP, Zhou C, Wu YT, Sahiner B, Hadjiiski LM, Roubidoux MA, Helvie MA,
“Computer-aided detection of breast masses: Four-view strategy for screening
mammography,” Medical Physics 2011, 38: 1867-1876.
[92] Samuelson F, Gallas BD, Myers KJ, Petrick N, Pinsky P, Sahiner B, Campbell G,
Pennello GA, “The Importance of ROC Data,” Academic Radiology 2011, 18: 257-258.
[93] Wei J, Chan HP, Wu YT, Zhou C, Helvie MA, Hadjiiski LM, Sahiner B, “A pilot case-
control study for analyzing the association of computerized mammographic parenchymal
pattern (MPP) with breast cancer risk,” Radiology 2011, 260:42-49.
[94] Sahiner B, Chan HP, Hadjiiski LM, Helvie MA, Wei J, Zhou C, “Computer-aided
detection of clustered microcalcifications in digital breast tomosynthesis: A 3D
approach,” Medical Physics 2012, 39:28-39.
[95] Gallas BD, Chan HP, D'Orsi CJ, Dodd LE, Giger ML, Gur D, Krupinski EA, Metz CE
Myers KJ; Obuchowski NA, Sahiner B, Toledano AY, Zuley ML, “Evaluating Imaging
and Computer-aided Detection and Diagnosis Devices at the FDA,” Academic Radiology
2012, 19:463-477.
[96] Wang SJ, McKenna MT, Nguyen TB, Burns JE, Petrick N, Sahiner B, Summers RM,
"Seeing Is Believing: Video Classification for Computed Tomographic Colonography
Using Multiple-Instance Learning," IEEE Transactions on Medical Imaging 2012,31:
1141-1153.
[97] Chen W, Petrick N, Sahiner B, “Hypothesis Testing in Noninferiority and Equivalence
MRMC ROC Studies,” Academic Radiology 2012, 9:1158-1165.
Rev: 06/01/2016
8. [98] Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N, “Benefit of Overlapping
Reconstruction for Improving the Quantitative Assessment of CT Lung Nodule Volume”,
Academic Radiology 2013, 20: 173-180.
[99] Cho, HC, Hadjiiski LM, Sahiner B, Chan HP, Helvie MA, Paramagul C, Nees AV, "A
similarity study of content-based image retrieval system for breast cancer using decision
tree," Medical Physics 2013, 40: 012901-1,13.
[100] Chen W, Samuelson FW, Gallas B, Kang L, Sahiner B, Petrick N, “On the assessment of
the added value of new predictive biomarkers,” BMC Med Res Methodol, 2013, 13:98.
[101] Huo, Z, Summers RM, Paquerault S, Lo J, Hoffmeister J, Armato SG, Friedman M, Lin J,
Lo SCB, Petrick N, Sahiner B, Fryd D, Yoshida H, Chan HP, “Quality Assurance and
Training Procedures for Computer-aided Detection and Diagnosis Systems,” Medical
Physics 2013, 40:077001.
[102] Petrick N, Sahiner B, Armato S, Bert A, Correale L, Delsanto S, Freedman M, Fryd D,
Gur D, Hadjiiski L, Hou Z, Jiang Y, Morra L, Paquerault S, Raykar V, Salganicoff M,
Samuelson F, Summers RM, Tourassi G, Yoshida H, Zheng B, Zhou C, Chan HP,
“Evaluation of Computer-Aided Detection and Diagnosis Systems,” Medical Physics
2013, 40:087001.
[103] Gavrielides MA, Li Q, Zeng R, Myers KJ, Sahiner B, Petrick N, “Minimum detectable
change in lung nodule volume in a phantom CT study,” Academic Radiology 2013,
20:1364-1370.
[104] He X, Sahiner B, Gallas, B, Chen W, Petrick N. “Computerized Characterization of Lung
Nodule Subtlety,” Physics in Medicine and Biology 2014, 59:897-910.
[105] Abbey CK, Gallas BD, Boone JM, Niklason LT, Hadjiiski LM, Sahiner B, Samuelson,
FW “Comparative statistical properties of expected utility and area under the ROC curve
for laboratory studies of observer performance in screening mammography,” Academic
Radiology 2014, 21:481-90.
[106] Samala RK, Chan H-P, Lu Y, Hadjiiski L, Wei J, Sahiner B, Helvie MA “Computer-
Aided Detection of Clustered Microcalcifications in Multiscale Bilateral Filtering
Regularized Reconstructed Digital Breast Tomosynthesis Volume,” Medical Physics
2014, 41(2):021901.
[107] He X, Samuelson F, Zeng R, Sahiner B, “Discovering intrinsic properties of human
observers' visual search and mathematical observers' scanning,” Journal of the Optical
Society of America, 2014, 31:2495-2510.
[108] Gavrielides MA, Li Q, Zeng R, Myers KJ, Sahiner B, Petrick N, “Volumetric analysis of
non-calcified lung nodules with thoracic CT: an updated review of related work over the
last 5 years,” Journal of Radiology & Radiation Therapy, 2014, 2(3): 1060.
[109] Li Q, Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N, “Volume estimation of
low-contrast lesions with CT: a comparison of performances from a phantom study,
simulations and theoretical analysis,” Physics in Medicine and Biology, 2015, 60:671-
688.
[110] Li Q, Gavrielides MA, Sahiner B, Myers KJ, Zeng R, Petrick N, “Statistical analysis of
lung nodule volume measurements with CT in a large-scale phantom study,” Medical
Physics 2015, 42:3932-3947.
[111] Pezeshk A, Sahiner B, Zeng R, Wunderlich A, Chen W, Petrick N, "Seamless insertion of
pulmonary nodules in chest CT images," IEEE Transactions on Biomedical Engineering,
2015, 62:2812-2827.
Rev: 06/01/2016
9. [112] Wang SJ, Li D, Petrick N, Sahiner B, Linguraru MG, Summers RM, "Optimizing area
under the ROC curve using semi-supervised learning," Pattern Recognition, 2015,
48:276-287.
[113] Fauzi MFA, Pennell M, Sahiner B, Chen W, Shana'ah A, Hemminger J, Kurt H, Losos
M, Joehlin-Price A, Kavran C, Smith SM, Nowacki N, Mansoor S, Lozanski G, Gurcan
MN, “Classification of Follicular Lymphoma: The Effect of Computer Aid on
Pathologists Grading,” BMC Medical Informatics and Decision Making, 2015, 15:115.
[114] Gavrielides M, Li Q, Zeng R, Myers K, Sahiner B, Petrick N, “Volume estimation of
multi-density nodules with thoracic CT,” Journal of Medical Imaging, 2016, 013504-1-9.
[115] Zeng R, Gavrielides MA, Petrick N, Sahiner B, Li Q, Myers KJ, “Estimating Local Noise
Power Spectrum (NPS) from a Few FBP-reconstructed CT scans,” Medical Physics, 2016
43:568-582
Rev: 06/01/2016
10. [112] Wang SJ, Li D, Petrick N, Sahiner B, Linguraru MG, Summers RM, "Optimizing area
under the ROC curve using semi-supervised learning," Pattern Recognition, 2015,
48:276-287.
[113] Fauzi MFA, Pennell M, Sahiner B, Chen W, Shana'ah A, Hemminger J, Kurt H, Losos
M, Joehlin-Price A, Kavran C, Smith SM, Nowacki N, Mansoor S, Lozanski G, Gurcan
MN, “Classification of Follicular Lymphoma: The Effect of Computer Aid on
Pathologists Grading,” BMC Medical Informatics and Decision Making, 2015, 15:115.
[114] Gavrielides M, Li Q, Zeng R, Myers K, Sahiner B, Petrick N, “Volume estimation of
multi-density nodules with thoracic CT,” Journal of Medical Imaging, 2016, 013504-1-9.
[115] Zeng R, Gavrielides MA, Petrick N, Sahiner B, Li Q, Myers KJ, “Estimating Local Noise
Power Spectrum (NPS) from a Few FBP-reconstructed CT scans,” Medical Physics, 2016
43:568-582
Rev: 06/01/2016