Conference Paper

Multi-mode Narrow-band Thresholding with Application in Liver Segmentation from Low-contrast CT Images

Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan
DOI: 10.1109/IIH-MSP.2009.78 Conference: Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
Source: IEEE Xplore

ABSTRACT Segmentation of liver in CT images is regarded as a challenge in image processing due to low-contrast of datasets, variety of liver shape, and its non-uniform texture; especially for abnormal cases. In this paper, we deal with normal and abnormal datasets as images containing two or more Gaussian components. We threshold a slice in a narrow band of each mode, find liver pixels based on a priori knowledge, prepare a probability map, and threshold the map to find initial liver border. Final boundary of liver is obtained through a few iterations of `Geodesic Active Contour'. The proposed method was tested on 30 normal and 17 abnormal datasets each containing 159-263 slices; acquired from different CT machines. The results for normal and abnormal datasets are completely acceptable, according to the evaluation done by a specialist. However, for severely abnormal datasets, the proposed method is regarded as a promising algorithm for liver segmentation.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Recently a growing interest has been seen in minimally invasive treatments with open configuration magnetic resonance (Open-MR) scanners. Because of the lower magnetic field (0.5T), the contrast of Open-MR images is very low. In this paper, we address the problem of liver segmentation from low-contrast Open-MR images. The proposed segmentation method consists of two steps. In the first step, we use K-means clustering and a priori knowledge to find and identify liver and non-liver index pixels, which are used as “object” and “background” seeds, respectively, for graph-cut. In the second step, a graph-cut based method is used to segment the liver from the low-contrast Open MR images. The main contribution of this paper is that the object (liver) and background (non-liver) seeds (regions) in every low-contrast slice of the volume can be obtained automatically by K-means clustering without user interaction.
    Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II; 01/2010
  • [Show abstract] [Hide abstract]
    ABSTRACT: Successful liver surgery requires an understanding of the patient's particular liver characteristics, including shape and vessel distribution. In clinical medicine, there is a high demand for surgical assistance systems to assess individual patients. Our aims in this study were to segment the liver based on computed tomography volume data and to develop surgical plans for individual patients. The hepatic vessels were semi-automatically extracted from the segmented liver images, and the 3D shape of the liver and extracted vessel distribution were visualized using a surgical simulation system. The 3D visualization of the liver allowed easy recognition of vessel and tumor location and selection of these structures with the 3D pointing device. The surgeon's prior knowledge and clinical experience were integrated into the visualization system to create a practical virtual surgery, leading to improved functionality and accuracy of information recognition in the surgical simulation system. The 3D visualization demonstrated details of individual liver structure, resulting in better understanding and practical surgical simulation.
    Journal of Gastrointestinal Surgery 06/2013; · 2.36 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In clinical routine of liver surgery there are a multitude of risks such as vessel injuries, blood loss, incomplete tumor resection, etc. In order to avoid these risks the surgeons perform a planning of a surgical intervention. A good graphical representation of the liver and its inner structures is of great importance for a good planning. In this work we introduce a new planning system for liver surgery, which is meant for computer tomography (CT) data analysis and graphical representation. The system is based on automatic and semiautomatic segmentation techniques as well as on a simple and intuitive user interface and was developed with the intention to help surgeons by planning an operation and increasing the efficiency in open liver surgery.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:1882-5.

Full-text (2 Sources)

Available from
May 17, 2014