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: Image features are the properties of image which can be used in image registration, image segmentation, target recognition and other fields of image processing. It plays an important role in image processing. In this paper, we first propose the gray scale potential (GSP) of image according to the relative position among the pixels. Second, we highlight the definition of GSP and how to calculate the GSP by taking the example of the binary images. Then we point out that the GSP can reflect an intrinsic feature of image. As for binary image, it reflects the relative distances of pixels to a baseline or to a reference point, and if the image is gray image, it reflects not only the distances but also the gray level feature. The GSP has obvious advantage in representing the sparse image, because it can reduce the computational work and storage. Finally, some experimental results are given to illustrate the characters of GSP. It shows that the GSP of image is a steady feature and can be used for target recognition.Neurocomputing 09/2013; 116:112-121. DOI:10.1016/j.neucom.2011.12.061 · 2.01 Impact Factor
Radioengineering 04/2013; 22(1):100-113. · 0.80 Impact Factor
Conference Paper: 3D DCT supervised segmentation applied on liver volumes[Show abstract] [Hide abstract]
ABSTRACT: Liver segmentation from computer tomography scans is a topic of research interest, as the acquisition and inter-patient variability make the automatic segmentation difficult. The current trend is to improve the accuracy and to reduce the computational complexity of the segmentation, as this is essential for the diagnostic and for 3D rendering. We propose a new computationally efficient approach for 3D liver segmentation, based on the 3D Discrete Cosine Transform applied on volume blocks for feature extraction, followed by a support vector machine classification of volume blocks. The segmentation is refined in a post-processing step through a 3D median filtering, 3D morphological operations, and 3D connected components analysis. This new method has been applied on real liver volumes and provided promising results, on the level of the state of the art, with a significant reduction in the data to be processed and in the operations involved as compared to other approaches.Telecommunications and Signal Processing (TSP), 2012 35th International Conference on; 01/2012