Mr. Sailendra Kumar Profile

Sailendra Kumar

Sr. Software Engineer at Philips Electronics India Ltd

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Proceedings Article | 23 March 2010 Paper

Anatomy guided automated SPECT renal seed point estimation

Shekhar Dwivedi, Sailendra Kumar

Proceedings Volume 7622, 76222M (2010) https://doi.org/10.1117/12.844162

KEYWORDS: Kidney, Bladder, Image segmentation, Single photon emission computed tomography, Error analysis, Data modeling, Image processing, Image processing algorithms and systems, Optical computing, Computed tomography

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Quantification of SPECT(Single Photon Emission Computed Tomography) images can be more accurate if correct segmentation of region of interest (ROI) is achieved. Segmenting ROI from SPECT images is challenging due to poor image resolution. SPECT is utilized to study the kidney function, though the challenge involved is to accurately locate the kidneys and bladder for analysis. This paper presents an automated method for generating seed point location of both kidneys using anatomical location of kidneys and bladder. The motivation for this work is based on the premise that the anatomical location of the bladder relative to the kidneys will not differ much. A model is generated based on manual segmentation of the bladder and both the kidneys on 10 patient datasets (including sum and max images). Centroid is estimated for manually segmented bladder and kidneys. Relatively easier bladder segmentation is followed by feeding bladder centroid coordinates into the model to generate seed point for kidneys. Percentage error observed in centroid coordinates of organs from ground truth to estimated values from our approach are acceptable. Percentage error of approximately 1%, 6% and 2% is observed in X coordinates and approximately 2%, 5% and 8% is observed in Y coordinates of bladder, left kidney and right kidney respectively. Using a regression model and the location of the bladder, the ROI generation for kidneys is facilitated. The model based seed point estimation will enhance the robustness of kidney ROI estimation for noisy cases.

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