A method for inspecting luggage in a CT system, comprising: obtaining slice data of the inspected luggage in the CT system; generating 3D volume data of at least one object in the luggage from the slice data; calculating first, second and third depth projection images of the object in three directions based on the 3D volume data, wherein projection direction of the third depth projection image is orthogonal to those of the first and second depth projection images; calculating a metric of symmetry, and a duty ratio and aspect ratio for each of the first, second, and third depth projection images, and a metric of similarity between each two of the first, second, and third depth projection images, the aspect ratio is calculated by obtaining a rectangular box enclosing the object and calculating a ratio of a length to a height of the rectangular box, and the duty ratio is obtained by calculating a number of non-zero pixels in the rectangular box, and dividing the number of non-zero pixels by an area of the rectangular box; generating a shape feature parameter of the object at least based on the metrics of symmetry, the metrics of similarity, the duty ratios and aspect ratios for the first to third depth projection images; classifying the shape feature parameter with a classifier based on shape feature to obtain a quantifier description expressing the shape of the object; and outputting a semantic description including at least the quantifier description of the object; wherein said generating 3D volume data of at least one object in the luggage from the slice data comprises: interpolating the slice data to generate 3D volume data of the luggage; performing unsupervised segmentation on the 3D volume data of the luggage to obtain a plurality of segmental regions; performing isosurface extraction on the plurality of segmental regions to obtain corresponding isosurfaces; and performing 3D surface segmentation on the isosurfaces to form a 3D model for the objects in the luggage; wherein the unsupervised segmentation is performed by using a Statistical Region Merging SRM which is extended into 3D processing by joining an atomic number and density of the 3D volume data into a vector so as to represent a 3D voxel as two vectors and replacing a grey difference in a conventional SRM with a module of a difference vector between the two vectors, replacing a 2D gradient in the conventional SRM with a 3D gradient, and replacing an area of a pixel in a 2D region in the conventional SRM with a volume of a voxel. obtaining slice data of the inspected luggage in the CT system; generating 3D volume data of at least one object in the luggage from the slice data; calculating first, second and third depth projection images of the object in three directions based on the 3D volume data, wherein projection direction of the third depth projection image is orthogonal to those of the first and second depth projection images; calculating a metric of symmetry, and a duty ratio and aspect ratio for each of the first, second, and third depth projection images, and a metric of similarity between each two of the first, second, and third depth projection images, the aspect ratio is calculated by obtaining a rectangular box enclosing the object and calculating a ratio of a length to a height of the rectangular box, and the duty ratio is obtained by calculating a number of non-zero pixels in the rectangular box, and dividing the number of non-zero pixels by an area of the rectangular box; generating a shape feature parameter of the object at least based on the metrics of symmetry, the metrics of similarity, the duty ratios and aspect ratios for the first to third depth projection images; classifying the shape feature parameter with a classifier based on shape feature to obtain a quantifier description expressing the shape of the object; and outputting a semantic description including at least the quantifier description of the object; wherein said generating 3D volume data of at least one object in the luggage from the slice data comprises: interpolating the slice data to generate 3D volume data of the luggage; performing unsupervised segmentation on the 3D volume data of the luggage to obtain a plurality of segmental regions; performing isosurface extraction on the plurality of segmental regions to obtain corresponding isosurfaces; and performing 3D surface segmentation on the isosurfaces to form a 3D model for the objects in the luggage; interpolating the slice data to generate 3D volume data of the luggage; performing unsupervised segmentation on the 3D volume data of the luggage to obtain a plurality of segmental regions; performing isosurface extraction on the plurality of segmental regions to obtain corresponding isosurfaces; and performing 3D surface segmentation on the isosurfaces to form a 3D model for the objects in the luggage; wherein the unsupervised segmentation is performed by using a Statistical Region Merging SRM which is extended into 3D processing by joining an atomic number and density of the 3D volume data into a vector so as to represent a 3D voxel as two vectors and replacing a grey difference in a conventional SRM with a module of a difference vector between the two vectors, replacing a 2D gradient in the conventional SRM with a 3D gradient, and replacing an area of a pixel in a 2D region in the conventional SRM with a volume of a voxel.