Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- A novel dataset and deep learning method for automatic exposure correction in endoscopic imaging(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12-01) García Vega, Carlos Axel; Falcón Morales, Luis Eduardo; puemcuervo, emipsanchez; Daul, Christian; González Mendoza, Miguel; Roshan Biswal, Rajesh; School of Engineering and Sciences; Campus Estado de México; Ochoa Ruiz, GilbertoEndoscopy is such an important medical practice that one of the most common type of cancer worldwide, cause of many deaths, can be diagnosed and treated since through this imaging technique clinicians can diagnose cancerous lesions in hollow organs. Nonetheless, endo- scopic images are often affected by sudden illumination changes which entail regions with overexposure, underexposure or even both errors, in accordance with the light source pose and the lumen texture of the inner walls. These poor light conditions can carry several negative consequences either for the examination itself or on the performance of Computed-assisted Diagnosis (CAD) or Computed-aided Surgery (CAS). However, almost no effort has been done for deploy endoscopic image enhancement methods that can perform adequately (even when both errors appear simultaneously) and in real-time. The contribution of the present work in overall aims to enhance the quality of Field-of-View (FoV) from endoscopic ex- aminations and Computed-assisted Diagnosis through real-time Deep Learning techniques, however, for achieving this general objective, we first built a reliable reference-based dataset Endo4IE, evaluates and validated by experts, to be an standard dataset for IE purposes, due to the lack of this dataset in the literature. Afterwards, we evaluated IE methods on our dataset to find out a prospect method for our case-of-study, in this case LMSPEC originally introduced to enhance images from natural scenes. We made adaptations over the objective function of the prospect method to obtain better performance regarding to structure and less artifacts in the enhanced frame. Finally, we tested on the Endo4IE dataseta and evaluate with state-of- the-art metrics against the baseline method, thus the proposed implementation has yielded a significant improvement over LMSPEC reaching a SSIM increase of 4.40% and 4.21% for overexposed and underexposed images, respectively. Regarding PSNR, an improvement of 3.83% for over-exposed and just 0.01% below with respect to LMSPEC.