J Med Syst - Classification of normal and diseased liver shapes based on Spherical Harmonics coefficients.

Tópicos

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Resumo

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.

Resumo Limpo

livershap analysi quantif still open research subject quantit assess liver clinic import various procedur diagnosi treatment plan monitor livershap classif clinic import correspond intrasubject intersubject studi research propos novel techniqu livershap classif base spheric harmon sh coeffici propos livershap classif algorithm consist follow step preprocess includ mesh generat simplif pointset match surfac templat align b livershap parameter includ surfac normal sh expans follow paramet space registr c featur select classif includ frequenc base featur select featur space reduct princip compon analysi pca classif multistep approach novel sens registr featur select livershap classif propos implement valid normal diseas liver sh domain various group sh featur appli convent pca andor order pvalu pca employ two classifi includ support vector machin svm knearest neighbor knn presenc liver data set result show propos specif featur combin classifi outperform exist livershap classif techniqu employ liver surfac inform spatial domain avail data set propos method can success classifi normal diseas liver correct classif rate perform result averag higher convent livershap classif method sever standard metric leaveoneout crossvalid receiv oper characterist roc analysi employ experi confirm effect propos livershap classif respect convent techniqu

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