A System for Computer Aided Detection of Diseases Patterns in High Resolution CT Images of the Lungs

Zrimec, Tatjana; Busayarat, Sata

doi:10.1109/cbms.2007.13

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…Prasad and Sowmya 24 used several morphological image processing methods to segment the lungs in CT images. Zrimec and Busayarat 25 proposed the use of morphological image processing methods and active contours to achieve segmentation of the lungs. Brown et al 26 proposed a method including the use of seeded region growing, edge tracking, and knowledge-based segmentation for the assessment of differential left and right lung function.…”

Section: Segmentation Of the Lungsmentioning

confidence: 99%

Automatic Delineation of the Diaphragm in Computed Tomographic Images

Rangayyan

Boag

2008

J Digit Imaging

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Segmentation of the internal organs in medical images is a difficult task. By incorporating a priori information regarding specific organs of interest, results of segmentation may be improved. Landmarking (i.e., identifying stable structures to aid in gaining more knowledge concerning contiguous structures) is a promising segmentation method. Specifically, segmentation of the diaphragm may help in limiting the scope of segmentation methods to the abdominal cavity; the diaphragm may also serve as a stable landmark for identifying internal organs, such as the liver, the spleen, and the heart. A method to delineate the diaphragm is proposed in the present work. The method is based upon segmentation of the lungs, identification of the lower surface of the lungs as an initial representation of the diaphragm, and the application of least-squares modeling and deformable contour models to obtain the final segmentation of the diaphragm. The proposed procedure was applied to nine X-ray computed tomographic (CT) exams of four pediatric patients with neuroblastoma. The results were evaluated against the boundaries of the diaphragm as identified independently by a radiologist. Good agreement was observed between the results of segmentation and the reference contours drawn by the radiologist, with an average mean distance to the closest point of 5.85 mm over a total of 73 CT slices including the diaphragm.

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Section: Segmentation Of the Lungsmentioning

confidence: 99%

Automatic Delineation of the Diaphragm in Computed Tomographic Images

Rangayyan

Boag

2008

J Digit Imaging

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“…where k is the number of channels in the image of input. The first layer performs 1 n convolution on the input, and the size of each convolution kernel is 11 cc  . We can obtain the output of the first layer as 1 n feature maps.…”

Section: Methodsmentioning

confidence: 99%

Projection super-resolution based on convolutional neural network for computed tomography

Tang

Zhang

et al. 2019

15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine

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The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projectiondomain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of low-and high-resolution projection. The network label is high-resolution projection and the input is its corresponding interpolation data after down sampling. FDK algorithm is utilized for three-dimensional image reconstruction and one slice of reconstruction image is taken as an example to evaluate the performance of the proposed method. Qualitative and quantitative results show that the proposed method is potential to improve the resolution of projection and enables the reconstructed image with higher quality.

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“…The model-based methods are based on learning the lung anatomy and developing a model of the lung. Based on this idea, Zrimec and Busayarat (2007) first extracted the anatomical features and landmarks. This information, together with the structure and regions of the lung were used in detecting disease patterns.…”

Section: Introductionmentioning

confidence: 99%