Interactive image registration tool for positioning verification in head and neck radiotherapy

Jiang, Ching‐Fen; Lu, Ti-Cheng; Sun, Shuh-Ping

doi:10.1016/j.compbiomed.2007.07.005

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…If we employed a general-purpose monitor as a substitute for a high-resolution monitor, accuracy would be sacrificed. In the future, we intend to investigate the use of registration software [15,16] and the quality improvement of portal images with the aim of improving the detection sensitivity and reducing the time required for patient setup verification. In this study, we did not evaluate the image quality on the monitor but the procedures of visual inspection.…”

Section: Discussionmentioning

confidence: 99%

Patient setup verification procedure for a portal image in a computed radiography system with a high-resolution liquid-crystal display monitor

Fujita

Yamaguchi

Bessho

et al. 2009

Radiol Phys Technol

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In our conventional visual inspection for setup verification, we have routinely used a console monitor of a CR system and a monitor of a treatment-planning system (i.e., the separate-monitor method) in order to avoid the need for CR portal-film generation. However, the separate-monitor method provided insufficient precision in detecting setup errors. We devised a setup verification procedure that uses a high-resolution liquid-crystal display monitor (i.e., the single-monitor method). Our objective in the present study was to evaluate the precision of the single-monitor method. These two methods were compared in terms of the precision of visual inspection. The single-monitor method was significantly superior to the separate-monitor method in sensitivity and in the magnitude of the discrepancy that could not be detected. The single-monitor method provides higher precision in visual inspection than does the separate-monitor method, and is a useful verification procedure.

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Section: Discussionmentioning

confidence: 99%

Patient setup verification procedure for a portal image in a computed radiography system with a high-resolution liquid-crystal display monitor

Fujita

Yamaguchi

Bessho

et al. 2009

Radiol Phys Technol

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“…The slices with the maximal cross-sectional area (MCSA) of the head are used as the registration reference. The MCSA is selected as the anatomical feature for registering the 3D image data of the head because it is usually contained in the scan data regardless of the scanning direction and can be easily detected through image segmentation techniques [8,9]. The overall registration process is described in detail as follows.…”

Section: Methodsmentioning

confidence: 99%

“…This was carried out by aligning the vertical axes of both volumes with the z axis of the system coordinates. Prior to this procedure, the head in both volume sets was first segmented through a bi-thresholding method [8] followed by the morphologic operations to remove debris and fill the holes such that the complete binary head volume, B(x, y, z), can be found. Then the three principal directions of the B(x, y, z) can be derived according the theory of inertia matrix described in [7,9].…”

Section: A Coarse Registrationmentioning

confidence: 99%

“…The GHT was applied in order to converge the detected contours into a pair of reference points for registration [8,12]. The process includes two steps: first, an R-table was built by calculating the vector set, { i a K }, between each contour point (x i , y i ) to the center of the contour, P c (x c , y c ), in the CT image.…”

Section: B Fine-tuning Using Mcsa and Ght 1) Detection Of Mcsamentioning

confidence: 99%

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A Hybrid Registration Method for Brain Image Analysis

Jiang

Chen

2012

2012 International Symposium on Computer, Consumer and Control

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The generation of standard brain image is one of the most important subjects in brain image analysis. In this paper, we have developed a method that generates a standard brain image of transversal sectional view from a dataset of MRI (magnetic resonance imaging) brain images. First, every pair of 2 MR images in the dataset is registered by using a genetic algorithm, and the shape difference between 2 images is parameterized in this process to select the most representative standard image. Next, we generate a standard brain image by taking an average among the images that are registered to the representative standard image. It is expected that the 3D standard brain image would be also obtained similarly by extending this method.

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“…A statistic-based auto-thresholding method was used for initial segmentation [12,13]. This algorithm automatically and objectively determines intensity threshold value(s) within an image histogram that maximize(s) the variability between the resulting pixel classes; thus, it separates the image into clusters with similar intensities.…”

Section: ) Initial Segmentationmentioning

confidence: 99%

Image segmentation techniques for stem cell tracking

Jiang

Tsai

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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To grow stem cells in vitro is an important task in regenerative medicine. Cell motility that can be derived by cell tracking is a useful index to evaluate the viability and stemness of stem cells. The precision of cell tracking is highly dependent on correct detection of the cell centroids, which are usually determined by cell segmentation process. In this study, a cell segmentation method combining doublethresholding and disk-based reconstruction is proposed to solve the problems arising from shape deformation and uneven illumination during cell culture. The qualitative and quantitative comparisons show that the proposed segmentation method increases accuracy in detection the cell centroid.

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Interactive image registration tool for positioning verification in head and neck radiotherapy

Cited by 13 publications

References 17 publications

Patient setup verification procedure for a portal image in a computed radiography system with a high-resolution liquid-crystal display monitor

Patient setup verification procedure for a portal image in a computed radiography system with a high-resolution liquid-crystal display monitor

A Hybrid Registration Method for Brain Image Analysis

Image segmentation techniques for stem cell tracking

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