Name
Abstract | ||
|---|---|---|
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns. |
Year | Venue | Keywords |
|---|---|---|
2004 | Intell. Data Anal. | feature selection,surface object classification,different technique,suitable pattern classification technique,best result,shape analysis,simulated shape data,neuroanatomic structure,surface shape,different combination,powerful shape description method,real hippocampal data,statisti- cal pattern recognition,classification,medical image analysis,classification result,surface-based approach,surface parameterization,receiver operator characteristic,support vector machine,leave one out cross validation,spherical harmonic,medical diagnosis,point distribution model,pattern recognition |
Field | DocType | Volume |
Point distribution model,Data mining,Normalization (statistics),Feature selection,Computer science,Data type,Artificial intelligence,Discriminative model,Pattern recognition,Support vector machine,Curse of dimensionality,Machine learning,Shape analysis (digital geometry) | Journal | 8 |
Issue | Citations | PageRank |
6 | 31 | 1.71 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Shen | 1 | 863 | 102.99 |
| James Ford | 2 | 227 | 16.26 |
| Fillia Makedon | 3 | 1676 | 201.73 |
| Saykin Andrew J | 4 | 631 | 66.57 |