Classification of normal and diseased liver shapes based on Spherical Harmonics coefficients.
Liver-shape analysis and quantification is still an open research subject. Quantitative assessment of the liver is of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Liver-shape classification is of clinical importance for corresponding intra-subject and inter-subject studies. In this research, we propose a novel technique for the liver-shape classification based on Spherical Harmonics (SH) coefficients. The proposed liver-shape classification algorithm consists of the following steps: (a) Preprocessing, including mesh generation and simplification, point-set matching, and surface to template alignment; (b) Liver-shape parameterization, including surface normalization, SH expansion followed by parameter space registration; (c) Feature selection and classification, including frequency based feature selection, feature space reduction by Principal Component Analysis (PCA), and classification. The above multi-step approach is novel in the sense that registration and feature selection for liver-shape classification is proposed and implemented and validated for the normal and diseases liver in the SH domain. Various groups of SH features after applying conventional PCA and/or ordered by p-value PCA are employed in two classifiers including Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) in the presence of 101 liver data sets. Results show that the proposed specific features combined with classifiers outperform existing liver-shape classification techniques that employ liver surface information in the spatial domain. In the available data sets, the proposed method can successful classify normal and diseased livers with a correct classification rate of above 90 %. The performed result in average is higher than conventional liver-shape classification method. Several standard metrics such as Leave-one-out cross-validation and Receiver Operating Characteristic (ROC) analysis are employed in the experiments and confirm the effectiveness of the proposed liver-shape classification with respect to conventional techniques.
Faculty of Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, 14515-775, Iran, farshid.mofrad@yahoo.com.
Farshid Babapour
FB
Mofrad
Reza Aghaeizadeh
RA
Zoroofi
Ali Abbaspour
AA
Tehrani-Fard
Shahram
S
Akhlaghpoor
Yoshinobu
Y
Sato
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008099:Liver / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D008107:Liver Diseases / Q000145:classification* / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000706:statistics & numerical data*