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.
Conference Proceeding: Efficient Liver Segmentation Based on the Spine.[show abstract] [hide abstract]
ABSTRACT: The first significant process for liver diagnosis of the computed tomography is to segment the liver structure from other abdominal organs. In this paper, we propose an efficient liver segmentation algorithm using the spine as a reference point without the reference image and training data. A multi-modal threshold method based on piecewise linear interpolation extracts ranges of regions of interest. Spine segmentation is performed to find the reference point providing geometrical coordinates. C-class maximum a posteriori decision using the reference point selects the liver region. Then binary morphological filtering is processed to provide better segmentation and boundary smoothing. In order to evaluate automatically segmented results of the proposed algorithm, the area error rate and rotational binary region projection matching method are applied. Evaluation results suggest proposed liver segmentation has strong similarity performance as the manual method of a medical doctor.Advances in Information Systems, Third International Conference, ADVIS 2004, Izmir, Turkey, October 20-22, 2004, Proceedings; 01/2004
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ABSTRACT: The segmentation and three-dimensional representation of the liver from a computed tomography (CT) scan is an important step in many medical applications, such as in the surgical planning for a living-donor liver transplant and in the automatic detection and documentation of pathological states. A method is being developed to automatically extract liver structure from abdominal CT scans using a priori information about liver morphology and digital image-processing techniques. Segmentation is performed sequentially image-by-image (slice-by-slice), starting with a reference image in which the liver occupies almost the entire right half of the abdomen cross section. Image processing techniques include gray-level thresholding, Gaussian smoothing, and eight-point connectivity tracking. For each case, the shape, size, and pixel density distribution of the liver are recorded for each CT image and used in the processing of other CT images. Extracted boundaries of the liver are smoothed using mathematical morphology techniques and B-splines. Computer-determined boundaries were compared with those drawn by a radiologist. The boundary descriptions from the two methods were in agreement, and the calculated areas were within 10%.Medical Physics 01/1993; 20(1):71-8. · 2.91 Impact Factor