Abstract
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.
Highlights
Positron emission tomography is a non-invasive nuclear medicine afunctional imaging method that images the distribution of biologically targeted radiotracers with high sensitivity
Great effort has been devoted to the study of image enhancement techniques; wavelet-contourlet transform [2], iterative denoising and partial volume correction [3], iterative deconvolution [4] have been among them
Recognition is determining where the targeted object is in the image, while the second process is defining the spatial extent of the recognized region [5]
Summary
Positron emission tomography is a non-invasive nuclear medicine afunctional imaging method that images the distribution of biologically targeted radiotracers with high sensitivity. Image contrast enhancement is an essential pre-processing stage in image segmentation [1]. Segmentation can be thought of as two consecutive processes: recognition and delineation. Recognition is determining where the targeted object is in the image, while the second process is defining the spatial extent of the recognized region [5]. Regions of high uptake of tracer are identified either manually or automatically [8]. Regions of high uptake of tracer2aorfe identified either manually or automatically [8]. TThhiiss ppaappeerr pprreesseennttss tthhee iimmpplleemmeennttaattiioonn ooff aann uunnssuuppeerrvviisseedd aauuttoommaattiicc PPEETT iimmaaggee sseeggmmeennttaattiioonn ssyysstteemm ttoo ddeetteecctt aa ttuummoorr. SSeeccttiioonn 22 pprreesseennttss tthhee mmaatthheemmaattiiccaall ffoorrmmuullaattiioonn aanndd iimmpplleemmeennttaattiioonn ooff pprrooppoosseedd aapppprrooaacchhwwhhicihchcocnotnatianisn,sc,ocnotrnatsrtasetnheannhcaenmceemntesnutpseurppiexreplisx, ealnsd, aPnCdAPfColAlowfoeldlobwyekd-mbyeakn‐s mclueasntesrinclgutsoterreicnoggntoizerethcoegcnainzceertshuepecrapnicxeerls.suSpecetripoinxe3liss. FigurFei1g.uImrep1le. mImenptlaetmioennotvaetirovniewov. ePrEvTi:ePwo.siPtrEoTn:ePmoissistiroonntoemmoisgsriaopnhyto; mPCoAg:rpaprihnyci;pPaCl cAom: pproinecniptalnalysis. component analysis
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