Biomedical subjects

Alain Tiedeu

Publications and source records attributed to Alain Tiedeu.

3 recordsLinked to original sources

PubMed2006

Automated detection of masses in mammograms by local adaptive thresholding.

In this paper, an algorithm for detection of suspicious masses from mammographic images is presented. The proposed algorithm was tested on a database of 61 mammograms on which masses had previously been marked by experienced radiologists. Results show that the proposed method exhibits for mass detection, a sensitivity of 95.91%. The area under receiver operating characteristic (ROC) Az was 0.946 when enhancement of the original image was performed before detection and 0.938 otherwise. Furthermore in some cases, we could detect some masses that the radiologists were not able to mark out.

Algorithms↗

PubMed2005

Correspondences between microcalcification projections on two mammographic views acquired with digital systems.

In this paper, we have proposed an algorithm for automatic matching of MC projections viewed on two mammograms of the same breast. The implemented algorithm consists in three steps. From five morphological features of the MC, a similarity function was built between each MC of the first image of the pair, and each MC of the second image. These values quantified the resemblance between each pair of MC and permitted, for a given MC of the first image, to sort the MC of the second image which could be matched to it. From the geometry of the system providing the pairs of images being analysed, we derived some geometrical constraints that must be satisfied by corresponding MC. In order to take into account the fact that due to breast deformation, the corresponding MC is often off the classical epipolar line constructed on the basis of stereovision assumption, we instead consider an epipolar strip on both side of the approximated epipolar line. The MC of the second image which was out of that strip was eliminated. Then a coefficient was applied to the remaining MC that took into account their distances to the approximated epipolar line. Finally, the selection procedure was used to pick out the right pair. In order to test our algorithm, we compared the result it yielded with those coming from two operators who matched a number of MC with confidence. A concordance of 78.43% was obtained between confident manual matching and automatic matching. Since this algorithm was designed to be part of a tool for 3D reconstruction of microcalcification clusters, we reconstructed some clusters with manual matching and automatic matching and compared the shapes of the clusters obtained in these two ways. The resemblance in some cases was very good and average in a number of others. This suggests that our algorithm should be improved. Therefore, apart from ongoing effort to introduce other constraints, we believe that taking into account the third view provided by the imaging system could be of great help. We shall soon explore this possibility. Overall, we believe that despite the failure of our tool in some cases it can already at this stage be used with some confidence.

Algorithms↗

PubMed2005

3-D reconstruction of microcalcification clusters using stereo imaging: algorithm and mammographic unit calibration.

The three-dimensional (3-D) shape of microcalcification clusters is an important indicator in early breast cancer detection. In fact, there is a relationship between the cluster topology and the type of lesion (malignant or benign). This paper presents a 3-D reconstruction method for such clusters using two 2-D views acquired during standard mammographic examinations. For this purpose, the mammographic unit was modeled using a camera with virtual optics. This model was used to calibrate the acquisition unit and then to reconstruct the clusters in the 3-D space after microcalcification segmentation and matching. The proposed model is hardware independent since it is suitable for digital mammographic units with different geometries and with various physical acquisition principles. Three-dimensional reconstruction results are presented here to prove the validity of the method. Tests were first performed using a phantom with a well-known geometry. The latter contained X-ray opaque glass balls representing microcalcifications. The positions of these balls were reconstructed with a 16.25-microm mean accuracy. This very high inherent algorithm accuracy is more than enough for a precise 3-D cluster representation. Further validation tests were carried out using a second phantom including a spherical cluster. This phantom was built with materials simulating the behavior of both mammary tissue and microcalcifications toward Xrays. The reconstructed shape was effectively spherical. Finally, reconstructions were carried out for real clusters and their results are also presented.

Algorithms↗