Analysis of the liver in CT images using an improved region growing technique

This paper presents an improved region growing algorithm to enhance the segmentation of the liver from abdominal CT images. The abdominal CT images are characterized by poor contrast and blurred edges which increase the complexity of liver segmentation. Initially, the images are subjected to preprocessing which involves de-noising, thresholding and non-linear mapping. Then, the improved region growing algorithm is applied to the preprocessed liver images. Post processing is performed using a combination of morphological operations. The results of the improved algorithm are compared with the traditional region growing algorithm and the k-means clustering algorithm to show the effectiveness of the proposed method. Performance validation is also done by comparing the results with the ground truth. Similarity measures namely the Dice similarity, Sokal and Sneath-I similarity, Sokal and Sneath-II similarity and Tanimoto similarity are used for the comparison. The results obtained using the improved method give an accuracy of 97%. The average Dice similarity measure for the considered images was found to be 0.86. The average correlation coefficient between the ground truth and the segmented result are also high in the improved algorithm. The obtained results seem to be clinically relevant.