Anatomical Structure Segmentation in Liver MRI Images
This work addresses the problem of anatomical structure segmentation in liver MRI for medical diagnosis, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared three segmentation methods—Level Set Method, Fuzzy Level Information C-Means Clustering Algorithm, and Gradient Vector Flow Snake Algorithm—for labeling voxels in liver MRI images to aid in diagnosing disorders like Hepatitis and Cirrhosis, with results evaluated based on number of pixels correctly classified and percentage of area segmented.
Segmentation of medical images is a challenging task owing to their complexity. A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labeling voxels according to their tissue type. Image segmentation provides volumetric quantification of liver area and thus helps in the diagnosis of disorders, such as Hepatitis, Cirrhosis, Jaundice, Hemochromatosis etc.This work deals with comparison of segmentation by applying Level Set Method,Fuzzy Level Information C-Means Clustering Algorithm and Gradient Vector Flow Snake Algorithm.The results are compared using the parameters such as Number of pixels correctly classified, and percentage of area segmented.