Procesamiento de Señales e Imágenes Digitales

URI permanente para esta colecciónhttp://54.81.141.168/handle/123456789/31440

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20131

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Autorsearch.filters.applied.operator.equals: search.filters.author.Díaz Rojas, Kristians EdgardoTemasearch.filters.applied.operator.equals: search.filters.subject.Reconocimiento de imágenes

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    Desarrollo y comparación de diversos mapas de probabilidades en 3D del cáncer de próstata a partir de imágenes de histología
    (Pontificia Universidad Católica del Perú, 2013-12-04) Díaz Rojas, Kristians Edgardo; Castañeda Aphan, Benjamín
    Understanding the spatial distribution of prostate cancer and how it changes according to prostate specific antigen (PSA) values, Gleason score, and other clinical parameters may help comprehend the disease and increase the overall success rate of biopsies. This work aims to build 3D spatial distributions of prostate cancer and examine the extent and location of cancer as a function of independent clinical parameters. The border of the gland and cancerous regions from whole-mount histopathological images are used to reconstruct 3D models showing the localization of tumor. This process utilizes color segmentation and interpolation based on mathematical morphological distance. 58 glands are deformed into one prostate atlas using a combination of rigid, a ne, and b-spline deformable registration techniques. Spatial distribution is developed by counting the number of occurrences in a given position in 3D space from each registered prostate cancer. Finally a di erence between proportions is used to compare di erent spatial distributions. Results show that prostate cancer has a significant di erence (SD) in the right zone of the prostate between populations with PSA greater and less than 5 ng=ml. Age does not have any impact in the spatial distribution of the disease. Positive and negative capsule-penetrated cases show a SD in the right posterior zone. There is SD in almost all the glands between cases with tumors larger and smaller than 10% of the whole prostate. A larger database is needed to improve the statistical validity of the test. Finally, information from whole-mount histopathological images could provide better insight into prostate cancer.