Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing.
ABSTRACT Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has
a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this
paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images
and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As
the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver
segmentation using histogram process has similar performance as the manual method by medical doctor.
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ABSTRACT: As a mean for liver investigation, abdominal CT images have been widely studied in the recent years. Processing CT image includes the automatic diagnosis of liver pathologies, such as detecting lesions and following vessels ramification, and its 3D volume rendering. The first step in all these studies is the automatic liver segmentation. This paper presents a fully automatic method to segment the liver from abdominal CT data with no interaction from user. A statistical model-based approach is used to distinguish roughly liver tissue from other abdominal organs. It is followed by applying force-driven optimized active contour (snake) in order to obtain a smoother and finer liver contour. The new segmentation technique has been evaluated on fifteen datasets, by comparing the automatically detected liver contour to the liver boundaries manually traced by an expert. Tests are reported on 15 datasets and promising result shows that sensitivity and specificity for automatic liver segmentation are 95% and 99% respectively.Informatics and Systems (INFOS), 2010 The 7th International Conference on; 04/2010
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ABSTRACT: Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentation suffers from the large shape variability of this organ, and from structures of similar appearance that juxtapose the liver. The technique presented in this paper is devoted to combine a statistical analysis of the data with a reconstruction model from sparse information: only the most reliable information in the image is used, and the rest of the liver's shape is inferred from the model and the sparse observation. The resulting process is more efficient than standard segmentation since most of the workload is concentrated on the critical points, but also more robust, since the interpolated volume is consistent with the prior knowledge statistics. The experimental results on liver datasets prove the sparse information model has the same potential as PCA, if not better, to represent the shape of the liver. Furthermore, the performance assessment from measurement statistics on the liver's volume, distance between reconstructed surfaces and ground truth, and inter-observer variability demonstrates the liver is efficiently segmented using sparse information.Information processing in medical imaging: proceedings of the ... conference 02/2007; 20:38-49.