Texture-learning-based system for three-dimensional segmentation of renal parenchyma in abdominal CT images

Cong-Qi Peng; Yuan-Hsiang Chang; Li-Jen Wang; Yon-Choeng Wong; Yang-Jen Chiang; Yan-Yau Jiang

doi:10.1117/12.809808

13 May 2009 Texture-learning-based system for three-dimensional segmentation of renal parenchyma in abdominal CT images

Cong-Qi Peng, Yuan-Hsiang Chang, Li-Jen Wang, Yon-Choeng Wong, Yang-Jen Chiang, Yan-Yau Jiang

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72593E (2009) https://doi.org/10.1117/12.809808
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Abdominal CT images are commonly used for the diagnosis of kidney diseases. With the advances of CT technology, processing of CT images has become a challenging task mainly because of the large number of CT images being studied. This paper presents a texture-learning based system for the three-dimensional (3D) segmentation of renal parenchyma in abdominal CT images. The system is designed to automatically delineate renal parenchyma and is based on the texturelearning and the region-homogeneity-based approaches. The first approach is achieved with the texture analysis using the gray-level co-occurrence matrix (GLCM) features and an artificial neural network (ANN) to determine if a pixel in the CT image is likely to fall within the renal parenchyma. The second approach incorporates a two-dimensional (2D) region growing to segment renal parenchyma in single CT image slice and a 3D region growing to propagate the segmentation results to neighboring CT image slices. The criterion for the region growing is a test of region-homogeneity which is defined by examining the ANN outputs. In system evaluation, 10 abdominal CT image sets were used. Automatic segmentation results were compared with manually segmentation results using the Dice similarity coefficient. Among the 10 CT image sets, our system has achieved an average Dice similarity coefficient of 0.87 that clearly shows a high correlation between the two segmentation results. Ultimately, our system could be incorporated in applications for the delineation of renal parenchyma or as a preprocessing in a CAD system of kidney diseases.

Citation Download Citation

Cong-Qi Peng, Yuan-Hsiang Chang, Li-Jen Wang, Yon-Choeng Wong, Yang-Jen Chiang, and Yan-Yau Jiang "Texture-learning-based system for three-dimensional segmentation of renal parenchyma in abdominal CT images", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593E (13 May 2009); https://doi.org/10.1117/12.809808

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KEYWORDS

Image segmentation

Computed tomography

Kidney

3D image processing

Spine

Image processing

Computing systems

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