Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension

Citation:
Anter, A. M., A. E. Hassanien, and G. Schaefer, "Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension", 2nd IAPR Asian Conference on Pattern Recognition (ACPR), 2013 , Okinawa, Japan. , 5 Nov. , 2013.

Date Presented:

5 Nov.

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Abnormalities in the liver include masses which can be benign or malignant. Due to the presence of these abnormalities, the regularity of the liver structure is altered, which changes its fractal dimension. In this paper, we present a computer aided diagnostic system for classifying liver abnormalities from abdominal CT images using fractal dimension features. We integrate different methods for liver segmentation and abnormality classification and propose an attempt that combines different techniques in order to compensate their individual weaknesses and to exploit their strengths. Classification is based on fractal dimension, with six different features being employed for extracted regions of interest. Experimental results confirm that our approach is robust, fast and able to effectively detect the presence of abnormalities in the liver.