Name
Title | ||
|---|---|---|
Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ACPR.2013.172 | ACPR |
Keywords | Field | DocType |
fractal dimension,abdominal ct image,different technique,classifying liver abnormality,liver structure,abnormality classification,liver segmentation,different feature,different method,automatic segmentation,fractal dimension feature,image segmentation,image classification,fractals | Computer vision,Scale-space segmentation,Fractal dimension,Segmentation,Medical imaging,Computer-aided,Fractal,Image segmentation,Artificial intelligence,Contextual image classification,Mathematics | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed M. Anter | 1 | 44 | 7.37 |
| Aboul Ella Hassanien | 2 | 1610 | 192.72 |
| Gerald Schaefer | 3 | 98 | 8.37 |