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

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.

[1]  S. Samnick,et al.  11C-Methionine PET of Myocardial Inflammation in a Rat Model of Experimental Autoimmune Myocarditis , 2016, The Journal of Nuclear Medicine.

[2]  P. Grigsby,et al.  Delineation of FDG-PET tumors from heterogeneous background using spectral clustering. , 2012, European journal of radiology.

[3]  Lihi Zelnik-Manor,et al.  What Makes a Patch Distinct? , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Jianru Xue,et al.  A supervoxel‐based segmentation method for prostate MR images , 2017, Medical physics.

[5]  Shanq-Jang Ruan,et al.  Dynamic contrast enhancement based on histogram specification , 2005, IEEE Transactions on Consumer Electronics.

[6]  Qian Fan,et al.  SUPERPIXEL-BASED UNSUPERVISED CHANGE DETECTION USING MULTI-DIMENSIONAL CHANGE VECTOR ANALYSIS AND SVM-BASED CLASSIFICATION , 2012 .

[7]  Ulas Bagci,et al.  Improved PET image quantification via iterative denoising and partial volume correction , 2015 .

[8]  Jayaram K. Udupa,et al.  Co-segmentation of Functional and Anatomical Images , 2012, MICCAI.

[9]  Jinman Kim,et al.  Use of anatomical priors in the segmentation of PET lung tumor images , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[10]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Jayaram K. Udupa,et al.  Scale-Based Diffusive Image Filtering Preserving Boundary Sharpness and Fine Structures , 2002, IEEE Trans. Medical Imaging.

[12]  A. Kono,et al.  Tumor Segmentation on 18 F FDG-PET Images Using Graph Cut and Local Spatial Information , 2015 .

[13]  Habib Zaidi,et al.  PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[14]  Ulas Bagci,et al.  A review on segmentation of positron emission tomography images , 2014, Comput. Biol. Medicine.

[15]  Zhiqiang Tian,et al.  Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging , 2016, SPIE Medical Imaging.

[16]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

[17]  Jayaram K. Udupa,et al.  Scale-based diffusive image filtering preserving boundary sharpness and fine structures , 2001, IEEE Transactions on Medical Imaging.

[18]  K. Raja,et al.  Segmentation of Alzheimers Disease in PET scan datasets using MATLAB , 2012, ArXiv.

[19]  Donald A. Jackson.  STOPPING RULES IN PRINCIPAL COMPONENTS ANALYSIS: A COMPARISON OF HEURISTICAL AND STATISTICAL APPROACHES' , 1993 .

[20]  W. Pieczynski,et al.  PET/CT image denoising and segmentation based on a multi observation and a multi scale Markov tree model , 2013, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).