Abstract
The article presents an innovative method of 3D computer tomography (CT) image reconstruction of kidney. Diagnosis based on CT scanning allows to obtain projections of multi-dimensional object, made from different directions in order to create cross-sectional (2D) slices. Standard techniques for identifying kidneys in CT images analyze each 2D slice separately. It causes different reconstruction accuracy for the same object at its different heights. This is the main problem of a machine-learning systems. Reconstruction error of end-slices of the kidney model is often greater than the error of the kidney's middle part. The main idea of the technique presented in this paper is to analyze the largest coherent 3D spatial-areas. This technique allows to increase the accuracy of kidney detection as well as to decrease the FP (false positive) error. An additional advantage of the developed algorithm is the possibility of obtaining a precise model representing the 3D view of an entire kidney.
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