On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits o...On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.展开更多
This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculatio...This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculations from a multitude of lines corresponding to the normal fibrous breast tissue.Following this rationale,this paper introduces a novel approach for detecting the speculated lesions in digital mammograms based on multi-scale SIFT(scale-invariant feature transform)orientations.The proposed method starts by estimating a set of key points that best represent the image mammography in a scale space.We then benefit from SIFT algorithm to locally characterize each key point by assigning a consistent orientation.Thereafter,a set of three features are extracted for each pixel in the image mammogram based on these orientations.The extracted features are fed into BDT(binary decision tree)in order to perform per pixel classification and decide whether the pixel is normal or abnormal.We evaluate the proposed system on BCDR(breast cancer digital repository)database and the experimental results show that our method is accurate with 97.95%recognition rate,while it is robust to illumination changes,rotation and scale variations.展开更多
基金supported by the National High Technology Research and Development Program (863 Program) (2010AA7080302)
文摘On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.
文摘This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculations from a multitude of lines corresponding to the normal fibrous breast tissue.Following this rationale,this paper introduces a novel approach for detecting the speculated lesions in digital mammograms based on multi-scale SIFT(scale-invariant feature transform)orientations.The proposed method starts by estimating a set of key points that best represent the image mammography in a scale space.We then benefit from SIFT algorithm to locally characterize each key point by assigning a consistent orientation.Thereafter,a set of three features are extracted for each pixel in the image mammogram based on these orientations.The extracted features are fed into BDT(binary decision tree)in order to perform per pixel classification and decide whether the pixel is normal or abnormal.We evaluate the proposed system on BCDR(breast cancer digital repository)database and the experimental results show that our method is accurate with 97.95%recognition rate,while it is robust to illumination changes,rotation and scale variations.
