RESUMEN
This paper presents a method for global feature extraction and the application of the boostmetric distance metric method for medical image retrieval. The global feature extraction method used the low frequency subband coefficient of the wavelet decomposition based on the non-tensor product coefficient for piecewise Gaussian fitting. The local features were extracted after semi-automatic segmentation of the lesion areas in the images in the database. The experimental verification of the method using 1688 CT images of the liver containing lesions of liver cancer, liver angioma, and liver cyst confirmed that this feature extraction method improved the detection rate of the lesions with good image retrieval performance.
Asunto(s)
Humanos , Algoritmos , Sistemas de Administración de Bases de Datos , Bases de Datos Factuales , Almacenamiento y Recuperación de la Información , Métodos , Neoplasias Hepáticas , Diagnóstico por Imagen , Interpretación de Imagen Radiográfica Asistida por Computador , Métodos , Sistemas de Información Radiológica , Tomografía Computarizada por Rayos XRESUMEN
This paper presents a new 3-D image registration method based on the principal component analysis (PCA). Compared with intensity-based registration methods using the whole volume intensity information, our approach utilizes PCA to estimate the centroid and principal axis, and completes the registration by aligning the centroid and principal axis. We evaluated the effectiveness of this approach by applying it to simulated and actual brain image data (MR, CT, PET, and SPECT). The experimental results indicate that the algorithm is effective, especially for registration of 3-D medical images.