Segmentation of Liver in Low-Contrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms

Amir H. FORUZAN  Yen-Wei CHEN  Reza A. ZOROOFI  Akira FURUKAWA  Yoshinobu SATO  Masatoshi HORI  Noriyuki TOMIYAMA  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.4   pp.798-807
Publication Date: 2013/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.798
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Medical Imaging)
Category: Medical Image Processing
liver segmentation,  segmentation of low-contrast images,  geodesic active contours,  liver intensity distribution modeling,  

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In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a 'Gaussian Mixture' model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a new thresholding technique to classify image pixels. We assign a distance feature vectors to each pixel and segment the liver by a K-means clustering scheme. This initial boundary of the liver is conditioned by the Fourier transform. Then, a Geodesic Active Contour algorithm uses the boundaries to find the final surface. The novelty in our method is the proper selection and combination of sub-algorithms so as to find the border of an object in a low-contrast image. The number of parameters in the proposed method is low and the parameters have a low range of variations. We applied our method to 30 datasets including normal and abnormal cases of low-contrast/high-contrast images and it was extensively evaluated both quantitatively and qualitatively. Minimum of Dice similarity measures of the results is 0.89. Assessment of the results proves the potential of the proposed method for segmentation in low-contrast images.