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Estimating the tumor-breast volume ratio from mammograms

Estimating the tumor-breast volume ratio from mammograms. Jorge Rodríguez*, Pedro Linares*, Eduardo Urra*, Daniella Laya*, Felipe Saldivia  , Aldo Reigosa  *Multidisciplinary Center of Visualization and Scientific Computing Faculty of Science and Technology – University of Carabobo

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Estimating the tumor-breast volume ratio from mammograms

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  1. Estimating the tumor-breast volume ratio from mammograms Jorge Rodríguez*, Pedro Linares*, Eduardo Urra*, Daniella Laya*, Felipe Saldivia, Aldo Reigosa  *Multidisciplinary Center of Visualization and Scientific Computing Faculty of Science and Technology – University of Carabobo Biotechnological and Medical Research Center Faculty of Heath Sciences Oncology Hospital “Miguel Pérez Carreño” January 2007

  2. Introduction • One woman in eight either has or will develop breast cancer in her lifetime. • Once a tumor has been detected we have to decide about the surgery. • Breast conservation surgery (lumpectomy, quadrantectomy) must be applied when it is possible. • One of the main factors to decide which type of surgery must be used (lumpectomy, quadrantectomy or total mastectomy) is the tumor-breast volume ratio WSCG 07

  3. Introduction • Mammograms are among the best early detection methods • A correct decision depends on the specialist's ability to imagine the 3D reconstruction from the mammograms • This intuitive estimation means high probability of wrong decisions. WSCG 07

  4. The Problem ¿It would be possible to build a tool to compute the tumor-breast volume ratio from mammograms only? WSCG 07

  5. Overview • Digitalizing lateral and cranio-caudal mammograms. • Segmentation of the breast and tumor from digital mammograms. • Mapping the mammograms over 3D planes and establishing the spatial matching. between the projections. • Surface reconstruction of the tumor and breast. • Computing the breast-tumor volume ratio. WSCG 07

  6. Segmentation • Filter based strategies failed to segment the irregular and noisy tumor contour • A semiautomatic and interactive strategy was chosen in order to profit the specialist experience. • The “Intelligent Scissors” technique was implemented WSCG 07

  7. Segmentation with “intelligent scissors” We will detect the best way between two point on the boundary. WSCG 07

  8. ¿How to Chose the best path? WSCG 07

  9. Segmentation with “intelligent scissors” The image is a discrete grid of pixels, so the contour is a piecewise of curves. WSCG 07

  10. Segmentation with “intelligent scissors” The best way is the set of pieces of curves which fit better the boundary. WSCG 07

  11. Segmentation with “intelligent scissors” • The well fitted to the boundary is achieved by: • The gradient magnitude • The gradient Direction • The second derivative zero cross (Laplacian) WSCG 07

  12. Segmentation with “intelligent scissors” Let the image be a graph G=(V, E) where V is the set of pixels and there is and edge between two pixels when these pixels belong to the same 8-neighborhood. Also, each edge has a cost defined by a function which depends on the three previous components. WSCG 07

  13. Segmentación usando “intelligent scissors” The local cost function L(p,q) from a pixel p to its 8-neighbor q, is defined as: L(p,q)=wZ·fZ(q) + wG·fG(q) + wD·fD(p,q) Where, wZ, wG and wD are weights. The optimal cost is given by the minimum accumulated cost that results from adding the local costs in a path from the initial to the end pixel. WSCG 07

  14. Segmentation with “intelligent scissors” WSCG 07

  15. Mapping mammograms and 3D matching Mapping mammograms over the 3D planes Mapping contours over 3D planes Matching between mapped planes (interactive process) WSCG 07

  16. Surface Reconstruction Projecting curves over XY plane (breast base). Defining the semi-elliptical base of the breast. Swept the plane forward and repeat. WSCG 07

  17. Surface Reconstruction Once all the contours has been generated, a simple contour connection algorithm is applied. No branching is present, so the correspondence between adjacent contours is trivial. Tumor is overestimated using a bounding volume (box or sphere). WSCG 07

  18. Computing the volume ratio Breast Volume = Ai*step Ai: Ellipse i Area Step: Step between adjacent ellipses Tumor volume < Bounding volume Volumetric Ratio = Tumor volume / Breast volume WSCG 07

  19. Results WSCG 07

  20. Results WSCG 07

  21. Results WSCG 07

  22. Results WSCG 07

  23. Results WSCG 07

  24. Conclusions and Future Work • We have designed and implemented an useful software tool to estimate he breast-tumor volume ratio from two conventional mammograms • The surface reconstruction of the breast has an acceptable appearance. • Clinical validation of the software must be achieved in advance. • Mammograms digitalizing device must be integrated to the application. • The lateral-oblique mammograms must be incorporated to the software in order to achieve more accuracy reconstruction of the tumor and breast. WSCG 07

  25. Thanks!!! WSCG 07

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