Automatic Annotation of Liver CT Image: ImageCLEFmed 2015
Imane Nedjar, Saïd Mahmoudi, Mohammed Amine Chikh, Khadidja Abi-Yad, Zouheyr Bouafia · ORBi UMONS · 2015
In this paper, we present the methods that we have proposed and used in the liver image annotation task of ImageCLEF 2015.This challenge entailed the annotation of liver CT scans to generate a structured report. To meet this challenge we have proposed two methods for annotating the liver image. The first one uses a classification approach, which is composed of two main phases. The first step consists of a pre-processing, where a texture and shape based fea- tures vector is extracted, in the second phase a classification process is achieved by using random forest classifier with two different sets of features. Our second method uses a specific signature of the liver. Indeed, we have taken a slice from 3D liver CT scans, thereafter we have normalized it into a rectangular block with constant dimensions to account for imaging inconsistencies, and then we have divided the block into small blocks. After applying the 1D Log-Gabor fil- ters transformation, the dominant phase data of each block was extracted and quantized to four levels to encode the unique pattern of the liver into a bit-wise template. The Hamming distance was employed for retrieval. We submitted 3 runs to the liver image annotation task of ImageCLEF 2015 and we obtained the following scores (90.4%, 90.2%, and 91%).