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Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering

1
Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Burgundy, 21000 Dijon, France
2
Erasmus+ Joint Master Program in Medical Imaging and Applications, UNICLAM, 03043 Cassino FR, Italy
3
Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Girona, 17004 Girona, Spain
4
Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
*
Author to whom correspondence should be addressed.
Methods Protoc. 2018, 1(1), 7; https://doi.org/10.3390/mps1010007
Received: 28 October 2017 / Revised: 21 December 2017 / Accepted: 8 January 2018 / Published: 19 January 2018
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation has remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method using superpixels. Principal component analysis is applied on the superpixels and their average value. The distance vector of each superpixel from the average is computed in the principal components coordinate system. Finally, k-means clustering is applied on the distance vector to recognize tumor and non-tumor superpixels. The proposed approach is implemented in MATLAB 2016A, and promising accuracy with execution time of 2.35 ± 0.26 s is achieved. Fast execution time is achieved since the number of superpixels, and the size of distance vector on which clustering was done are low compared to the number of pixels in the image. View Full-Text
Keywords: k-means; positron emission tomography; principal component analysis; segmentation; superpixels k-means; positron emission tomography; principal component analysis; segmentation; superpixels
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MDPI and ACS Style

Hagos, Y.B.; Minh, V.H.; Khawaldeh, S.; Pervaiz, U.; Aleef, T.A. Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering. Methods Protoc. 2018, 1, 7. https://doi.org/10.3390/mps1010007

AMA Style

Hagos YB, Minh VH, Khawaldeh S, Pervaiz U, Aleef TA. Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering. Methods and Protocols. 2018; 1(1):7. https://doi.org/10.3390/mps1010007

Chicago/Turabian Style

Hagos, Yeman B., Vu H. Minh, Saed Khawaldeh, Usama Pervaiz, and Tajwar A. Aleef 2018. "Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering" Methods and Protocols 1, no. 1: 7. https://doi.org/10.3390/mps1010007

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