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Meeting ReportInstrumentation & Data Analysis: Data Analysis & Management

Evaluation of ten automatic thresholding methods for segmentation of PET images

Elena Prieto, Josep Marti-Climent, Pablo Lecumberri, Izaskun Bilbao, Margarita Ecay, Miguel Pagola, Ivan Peñuelas and Marisol Gomez-Fernandez
Journal of Nuclear Medicine May 2011, 52 (supplement 1) 2107;
Elena Prieto
1Nuclear Medicine, Clinica Universidad de Navarra, Pamplona, Spain
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Josep Marti-Climent
1Nuclear Medicine, Clinica Universidad de Navarra, Pamplona, Spain
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Pablo Lecumberri
2Mathematics, Universidad Publica de Navarra, Pamplona, Spain
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Izaskun Bilbao
3Small Animal Imaging Research Unit, Center for Applied Medical Research (CIMA), Pamplona, Spain
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Margarita Ecay
3Small Animal Imaging Research Unit, Center for Applied Medical Research (CIMA), Pamplona, Spain
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Miguel Pagola
4Automatics and Computation, Universidad Publica de Navarra, Pamplona, Spain
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Ivan Peñuelas
1Nuclear Medicine, Clinica Universidad de Navarra, Pamplona, Spain
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Marisol Gomez-Fernandez
2Mathematics, Universidad Publica de Navarra, Pamplona, Spain
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Abstract

2107

Objectives An automatic method for tumor segmentation over PET images has not yet been standardized. Our objective is to test several automatic thresholding methods, standard for other 2D image applications, for segmentation of small spheres acquired on a MicroPET.

Methods Six spheres of different diameters (8-25mm) filled with FDG were scanned separately in a Philips Mosaic MicroPET. Ten automatic thresholding methods were studied, which calculate an optimum threshold for each image based on the exploitation of: histogram shape (Ramesh), space clustering (Otsu, Ridler, Lloyd, Yanni), histogram entropy (Sahoo, Pun) or image attribute information (Hertz, Tsai, Huang). These algorithms were implemented for 3D images and compared to a fixed threshold of 40% of maximum (Matlab). Six spheres with the same diameters were simulated to be used as gold standard. Evaluation was performed quantitatively using 2 metrics, misclassification error (ME) and volume error (VE), taking values between 0 (perfect segmentation) and 1.

Results Segmented images were analyzed visually and the sphere was detected in all cases. Table summarizes the quantitative evaluation of each method, with results arranged in ascending order of average of metrics. According to this average, the six first methods are superior to a standard threshold of 40%. Tsai method showed the best performance.

Conclusions The automatic thresholding methods Tsai, Otsu, Ramesh, Ridler, Hertz and Lloyd (which calculated an optimum threshold for each image) provided superior results than the fixed-threshold of 40% and are ideally suitable for precise tumor segmentation for better diagnosis and therapy planning

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Journal of Nuclear Medicine
Vol. 52, Issue supplement 1
May 2011
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Evaluation of ten automatic thresholding methods for segmentation of PET images
Elena Prieto, Josep Marti-Climent, Pablo Lecumberri, Izaskun Bilbao, Margarita Ecay, Miguel Pagola, Ivan Peñuelas, Marisol Gomez-Fernandez
Journal of Nuclear Medicine May 2011, 52 (supplement 1) 2107;

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Evaluation of ten automatic thresholding methods for segmentation of PET images
Elena Prieto, Josep Marti-Climent, Pablo Lecumberri, Izaskun Bilbao, Margarita Ecay, Miguel Pagola, Ivan Peñuelas, Marisol Gomez-Fernandez
Journal of Nuclear Medicine May 2011, 52 (supplement 1) 2107;
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