Friday, November 13, 2009
A new Ph.D. student joins the CIALAB
Daniya Zamalieva, MS has joined the clinical image analysis group as a new Ph.D. student majoring in Computer Science and Engineering program. She received her B.S. degree in Computer Engineering in 2007 from Hacettepe University and M.S. degree in 2009 from Bilkent University, Turkey. During her graduate study, she was interested in Computer Vision and Pattern Recognition and she worked on processing and analysis of remotely sensed images. During her PhD studies, she will focus on computer-assisted diagnosis and creation of imaging biomarkers.
Friday, October 16, 2009
OAI meniscus segmentation work accepted for publication in Osteoarthritis and Cartilage Journal
A method for the semi-automated segmentation of the meniscus in OAI images developed by the CIALAB Osteoarthritis Research Group has been accepted for publication in the Osteoarthritis and Cartilage Journal. The authors of the paper were Mark Swanson, a Medical Student with Roessler Scholarship, Jeff Prescott, an MD/PhD student in the lab, Thomas Best MD/PhD, Kimberly Powell, Rebecca Jackson MD, Furqan Haq PhD, and Metin Gurcan PhD. The abstract of the manuscript appears below.
Abstract:
Objective: The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA).
Method: The segmentation method was developed then evaluated on 10 baseline magnetic resonance images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manually choosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, and range constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operation reevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was evaluated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five human readers.
Results: The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity index over 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee osteoarthritis with Osteoarthritis Research International Society International (OARSI) joint space narrowing scores of 0,1, and 2 respectively.
Conclusion: The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared to manual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thickness, and intensity characteristics at different stages of OA.
Abstract:
Objective: The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA).
Method: The segmentation method was developed then evaluated on 10 baseline magnetic resonance images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manually choosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, and range constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operation reevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was evaluated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five human readers.
Results: The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity index over 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee osteoarthritis with Osteoarthritis Research International Society International (OARSI) joint space narrowing scores of 0,1, and 2 respectively.
Conclusion: The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared to manual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thickness, and intensity characteristics at different stages of OA.
Wednesday, October 14, 2009
OAI quadriceps segmentation work accepted for publication in the Journal of Digital Imaging
A method for the segmentation of the quadriceps muscles in OAI images developed by the CIALAB Osteoarthritis Research Group has been accepted for publication in the Journal of Digital Imaging. The authors of the paper were Jeff Prescott, an MD/PhD student in the lab, Thomas Best MD/PhD, Mark Swanson, Furqan Haq PhD, Rebecca Jackson MD, and Metin Gurcan PhD. The abstract of the manuscript appears below.
Abstract: In this paper we present a semi-automated segmentation method for magnetic resonance (MR) images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback-Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were: rectus femoris, 0.78 ± 0.12; vastus intermedius, 0.79 ± 0.10; vastus lateralis, 0.82 ± 0.08; vastus medialis, 0.69 ± 0.16.
Abstract: In this paper we present a semi-automated segmentation method for magnetic resonance (MR) images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback-Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were: rectus femoris, 0.78 ± 0.12; vastus intermedius, 0.79 ± 0.10; vastus lateralis, 0.82 ± 0.08; vastus medialis, 0.69 ± 0.16.
Monday, October 12, 2009
Meniscus research featured on docguide.com
New research on the meniscus performed by the Osteoarthritis Research Group in the CIALAB and presented at the OARSI conference was highlighted in an article on docguide.com.
Wednesday, October 7, 2009
CIALAB members Dr. Kamel Belkacem-Boussaid and Jeff Prescott have had their recent work accepted for presentation at SPIE 2010
CIALAB members Dr. Kamel Belkacem-Boussaid and Jeff Prescott have had their recent work accepted for presentation at SPIE 2010, taking place February 13-18 in San Diego, California. The accepted papers are:
1- K. Belkacem-Boussaid, J. Prescott, G. Lozanski, and M. Gurcan, “ Segmentation of follicular regions on H&E slides using a matching filter and active contour model”
ABSTRACT: Follicular Lymphoma (FL) accounts for 20-25% of non-Hodgkin lymphomas in the US. The first step in follicular lymphoma grading is the identification of follicles. The goal of this paper is to develop a technique to segment follicular regions in H&E stained images. The method is based on a robust active contour model in which the centroid of the expanded curve is selected manually by the user. The novel aspect of this method is the introduction of matched filter for the flattening of background in the L channel of the Lab color space. The performance of the algorithm was tested by comparing it against the manual segmentations of trained readers using the Zijbendos similarity index. The mean accuracy of the final segmentation compared to the manual ground truth is 0.71 and with a standard deviation of 0.12.
2- J. Prescott, F. Haq, T.M. Best, R. Jackson, and M. Gurcan, “An analysis of methods for the selection of atlases for use in medical image segmentation”
ABSTRACT: The use of atlases has been shown to be a robust method for segmentation of medical images. In this paper we explore different methods of selection of atlases for the segmentation of the quadriceps muscles in MR images, although the results are pertinent for a wide range of applications. First, a set of readers were assigned the task of selecting atlases from a training population of images which were felt to be representative subgroups of the total population. This task was performed with no external knowledge besides the end goal of segmentation. Second, the same readers were given a subset of the training population stratified into modes of the population from which to select templates. Finally, every image in the training set was employed as an atlas, with no input from the readers, and the atlas which had the best initial registration, judged by an appropriate registration metric, was used in the final segmentation procedure. The results show that, for four out of five readers, the inclusion of modal information into the atlas selection process improved the final segmentation. However, the use of every image in the training set as an atlas far outperformed the manual atlas selection method, whether with or without modal information.
The CIALAB members would like to thank all their collaborators for their help in producing these papers.
1- K. Belkacem-Boussaid, J. Prescott, G. Lozanski, and M. Gurcan, “ Segmentation of follicular regions on H&E slides using a matching filter and active contour model”
ABSTRACT: Follicular Lymphoma (FL) accounts for 20-25% of non-Hodgkin lymphomas in the US. The first step in follicular lymphoma grading is the identification of follicles. The goal of this paper is to develop a technique to segment follicular regions in H&E stained images. The method is based on a robust active contour model in which the centroid of the expanded curve is selected manually by the user. The novel aspect of this method is the introduction of matched filter for the flattening of background in the L channel of the Lab color space. The performance of the algorithm was tested by comparing it against the manual segmentations of trained readers using the Zijbendos similarity index. The mean accuracy of the final segmentation compared to the manual ground truth is 0.71 and with a standard deviation of 0.12.
2- J. Prescott, F. Haq, T.M. Best, R. Jackson, and M. Gurcan, “An analysis of methods for the selection of atlases for use in medical image segmentation”
ABSTRACT: The use of atlases has been shown to be a robust method for segmentation of medical images. In this paper we explore different methods of selection of atlases for the segmentation of the quadriceps muscles in MR images, although the results are pertinent for a wide range of applications. First, a set of readers were assigned the task of selecting atlases from a training population of images which were felt to be representative subgroups of the total population. This task was performed with no external knowledge besides the end goal of segmentation. Second, the same readers were given a subset of the training population stratified into modes of the population from which to select templates. Finally, every image in the training set was employed as an atlas, with no input from the readers, and the atlas which had the best initial registration, judged by an appropriate registration metric, was used in the final segmentation procedure. The results show that, for four out of five readers, the inclusion of modal information into the atlas selection process improved the final segmentation. However, the use of every image in the training set as an atlas far outperformed the manual atlas selection method, whether with or without modal information.
The CIALAB members would like to thank all their collaborators for their help in producing these papers.
Dr. Gurcan has been awarded an ACS Grant
Dr. Gurcan has been awarded an American Cancer Society Ohio Division Supported Pilot Grant for beginning researchers to engage in basic, clinical, or behavioral/cancer control research. For this project entitled "Computer-aided clinical image analysis for cutaneous lymphomas," Dr. Gurcan will work with Drs. Porcu, Wong and Pennell to develop a system to analyze cutaneous lymphomas.
http://www.cancer.org/docroot/COM/content/div_OH/COM_7_2x_Ohio_Grant_Recipients.asp.
This project is part of Dr. Gurcan’s ongoing efforts to produce accurate and reproducible tools to distinguish discrete subsets of human cancers and to standardize measurements of response after therapy, which are essential for translational and clinical research.
http://www.cancer.org/docroot/COM/content/div_OH/COM_7_2x_Ohio_Grant_Recipients.asp.
This project is part of Dr. Gurcan’s ongoing efforts to produce accurate and reproducible tools to distinguish discrete subsets of human cancers and to standardize measurements of response after therapy, which are essential for translational and clinical research.
Friday, September 11, 2009
Biomedical Imaging Specialist joins the Clinical Image Lab
Dr. Sufyan Ababneh has joined the clinical image analysis group as a Biomedical Imaging Specialist. Dr. Ababneh received his BS degree in Electrical and Computer Engineering (ECE) from Jordan University of Science and Technology and his MS Degree in ECE from the University of Alabama in Huntsville. In 2008, he received his PhD degree in ECE from the University of Illinois in Chicago. Prior to that, he was a professional working for several well known companies in the private sector. Prior to joining the Ohio State University, he worked from 2002 to 2006, as a senior developer at Toshiba Medical Research Institute USA Inc and Bio-Imaging Research Inc developing CT-Scan imaging systems. From 1998 to 2002, he served as a senior software engineer at Motorola Inc. From 1997 to 1998, he worked as a Development Consultant for Bio-Imaging Research Inc. From 1995 to 1997, he worked as an Algorithms Developer designing embedded-systems applications at Circuit City.
Dr. Ababneh's research interests include image analysis, segmentation, classification, 2-D and 3-D compression with applications to medical images and telemedicine, image informatics and computer-aided diagnosis. He spent five years developing high performance CT-scan bio-imaging systems in distributed and embedded environments. In addition, he conducted research on watermarking-based multimedia content authentication.
Dr. Ababneh's research interests include image analysis, segmentation, classification, 2-D and 3-D compression with applications to medical images and telemedicine, image informatics and computer-aided diagnosis. He spent five years developing high performance CT-scan bio-imaging systems in distributed and embedded environments. In addition, he conducted research on watermarking-based multimedia content authentication.
Subscribe to:
Posts (Atom)
