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Proceedings ArticleDOI

Fully automatic segmentation of liver from multiphase liver CT

08 Mar 2007-Vol. 6512, Iss: 31, pp 993-1000
TL;DR: In this paper, a CT image was first smoothed by geometric diffusion method; the smoothed image was segmented by thresholding operators and then morphological operators were applied to fill the holes in the generated binary image and to disconnect the liver from other unwanted adjoining structures.
Abstract: Multidetector row CT, multiphase CT in particular, has been widely accepted as a sensitive imaging modality in the detection of liver cancer. Segmentation of liver from CT images is of great importance in terms of accurate detection of tumours, volume measurement, pre-surgical planning. The segmentation of liver, however, remains to be an unsolved problem due to the complicated nature of liver CT such as imaging noise, similar intensity to its adjacent structures and large variations of contrast kinetics and localised geometric features. The purpose of this paper is to present our newly developed algorithm aiming to tackle this problem. In our method, a CT image was first smoothed by geometric diffusion method; the smoothed image was segmented by thresholding operators. In order to gain optimal segmentation, a novel method was developed to choose threshold values based on both the anatomical knowledge and features of liver CT. Then morphological operators were applied to fill the holes in the generated binary image and to disconnect the liver from other unwanted adjoining structures. After this process, a so-called "2.5D region overlapping" filter was introduced to further remove unwanted regions. The resulting 3D region was regarded as the final segmentation of the liver region. This method was applied to venous phase CT data of 45 subjects (30 patient and 15 asymptomatic subjects). Our results show good agreement with the annotations delineated manually by radiologists and the overlapping ratio of volume is 87.7% on average and the correlation coefficient between them is 98.1%.
Citations
More filters
Proceedings ArticleDOI
11 Jun 2009
TL;DR: A liver segmentation method based on region growing approach is proposed, and the superior liver region is extracted by applying the morphologic operation on a variety of CT images.
Abstract: Accurate liver segmentation on computed tomography (CT) images is a challenging task because of inter and intra- patient variations in liver shapes, similar intensity with its nearby organs. We proposed a liver segmentation method based on region growing approach. First of all, basic theory of region growing approach is introduced. Secondly, a pre-processing method using anisotropic filter and Gaussian function is employed to form liver likelihood images for the following segmentation. Thirdly, an improved slice-to-slice region growing method combined with centroid detection and intensity distribution analysis is proposed. Finally, the superior liver region is extracted by applying the morphologic operation. Experiments on a variety of CT images show the effectiveness and efficiency of the proposed method.

30 citations

Proceedings Article
28 Mar 2010
TL;DR: A fully automatic method to segment the liver from abdominal CT data with no interaction from user is presented and promising result shows that sensitivity and specificity for automatic liver segmentation are 95% and 99% respectively.
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.

20 citations

Proceedings ArticleDOI
03 Jul 2012
TL;DR: This work proposes 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.
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.

15 citations

Proceedings ArticleDOI
06 Nov 2007
TL;DR: An automatic approach for an efficient brain extraction from CT head scans using domain knowledge, including Hounsfield unit ranges, anatomy, and image acquisition parameters, is applied.
Abstract: We present an automatic approach for an efficient brain extraction from CT head scans. Regions of interest are first set in each slice by applying thresholding and region growing. Next, the brain candidates are extracted by using three‐dimensional region growing with a variable, anatomy‐dependent structuring element. Domain knowledge, including Hounsfield unit ranges, anatomy, and image acquisition parameters, is applied. The proposed method has been applied automatically to 27 CT normal and pathological scans and has shown promising results. The average sensitivity, specificity and Dice's index for 5 cases are 99.6%, 99.4% and 98.7%, respectively.

2 citations

01 Jan 2014
TL;DR: A liver cancerregion segmentation method by analysis of computed tomography images is presented, which mainly consist of three stages, first pre-processing is done in order to enhance the image quality by using median filter, then some morphological operations are applied and finally the segmented image is analyzed for the detection of cancer.
Abstract: Now days computer aided diagnosis is widely used. Image processing techniques which are widely used, are the most significant and integral part of such diagnosis, in several medical areas. It is an important tool for diagnosis and treatment planning. Medical image segmentation can be used for several different tasks such as liver, brain tumor diagnosis. Liver cancer is one of the most commonly diagnosed cancer types. In this paper we present a liver cancerregion segmentation method by analysis of computed tomography images. It mainly consist of three stages, first pre-processing is done in order to enhance the image quality by using median filter. Second by using region growing technique image segmentation is done and some morphological operations are applied in order to get proper liver region. Finally we analyze the segmented image for the detection of cancer.

2 citations