A method for extracting shape feature of an object in a Computer Tomography system comprising: acquiring slice data of luggage under inspection with the Computer Tomography system (S41, S101); detecting, in the acquired slice data, at least one 3-Dimensional, 3D, object in the luggage; generating, from the slice data, 3D volume data of the at least one object in the luggage (S42, S103); obtaining, based on the 3D volume data, a first depth projection image (I 0) of the object in a direction perpendicular to a horizontal plane, wherein the horizontal plane is a plane perpendicular to the direction of positive acceleration due to gravity, and second (I 1), third (I 2) and fourth (I 3) depth projection images of the object in second, third and fourth directions (S43, S103), wherein the projection direction of the fourth depth projection image is orthogonal to the projection directions of the second and third depth projection images, wherein the pixels of the depth projection images each have grey values; calculating a probability that the first depth projection image might contain a horizontal liquid surface (S44, S104), wherein said calculating a probability that the first depth projection image might contain a horizontal liquid surface comprises determining a histogram of the grey values of the first depth projection image; determining the grey value corresponding to the maximum of the histogram; and calculating a proportion of pixels in the first projection image having a grey value within a given threshold of the grey value corresponding to the maximum of the histogram, as an indication of the probability; calculating a metric of symmetry for each of the first, second, third, and fourth depth projection images (S45, S105), wherein the metric of symmetry is calculated for each of the first, second, third, and fourth depth projection images based on an average of the difference, for each pixel position i,j, of the grey value of the pixel at position i,j in the respective depth projection image and the grey value of the pixel at position i,j in the respective depth projection image turned upside-down; and generating a shape feature parameter for the object at least based on the probability and the respective metrics of symmetry of the first to fourth depth projection images (S46, S106). acquiring slice data of luggage under inspection with the Computer Tomography system (S41, S101); detecting, in the acquired slice data, at least one 3-Dimensional, 3D, object in the luggage; generating, from the slice data, 3D volume data of the at least one object in the luggage (S42, S103); obtaining, based on the 3D volume data, a first depth projection image (I 0) of the object in a direction perpendicular to a horizontal plane, wherein the horizontal plane is a plane perpendicular to the direction of positive acceleration due to gravity, and second (I 1), third (I 2) and fourth (I 3) depth projection images of the object in second, third and fourth directions (S43, S103), wherein the projection direction of the fourth depth projection image is orthogonal to the projection directions of the second and third depth projection images, wherein the pixels of the depth projection images each have grey values; calculating a probability that the first depth projection image might contain a horizontal liquid surface (S44, S104), wherein said calculating a probability that the first depth projection image might contain a horizontal liquid surface comprises determining a histogram of the grey values of the first depth projection image; determining the grey value corresponding to the maximum of the histogram; and calculating a proportion of pixels in the first projection image having a grey value within a given threshold of the grey value corresponding to the maximum of the histogram, as an indication of the probability; calculating a metric of symmetry for each of the first, second, third, and fourth depth projection images (S45, S105), wherein the metric of symmetry is calculated for each of the first, second, third, and fourth depth projection images based on an average of the difference, for each pixel position i,j, of the grey value of the pixel at position i,j in the respective depth projection image and the grey value of the pixel at position i,j in the respective depth projection image turned upside-down; and generating a shape feature parameter for the object at least based on the probability and the respective metrics of symmetry of the first to fourth depth projection images (S46, S106).