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 prompt assisted image enhancement model using BERT classifier and modified LMSPEC and STTN techniques for endoscopic images(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Cerriteño Magaña, Javier; Ochoa Ruiz, Gilberto; emipsanchez; Sánchez Ante, Gildardo; Alfaro Ponce, Mariel; School of Engineering and Sciences; Campus MonterreyThis document presents a research thesis for the Master in Computer Science (MCCi) degree at Tecnologico de Monterrey. The field of medical imaging, particularly in endoscopy, has seen significant advancements in image enhancement techniques aimed at improving the clarity and interpretability of captured images. Numerous models and methodologies have been developed to enhance medical images, ranging from traditional algorithms to complex deep learning frameworks. However, the effective implementation of these techniques often requires substantial expertise in computer science and image processing, which may pose a barrier for medical professionals who primarily focus on clinical practice. This thesis presents a novel prompt-assisted image enhancement model that integrates the LMSPEC and STTN techniques, augmented by BERT models equipped with added attention blocks. This innovative approach enables medical practitioners to specify desired image enhancements through natural language prompts, significantly simplifying the enhancement process. By interpreting and acting upon user-defined requests, the proposed model not only empowers clinicians with limited technical backgrounds to effectively enhance endoscopic images but also streamlines diagnostic workflows. To the best of our knowledge, this is the first dedicated prompt-assisted image enhancement model specifically tailored for medical imaging applications. Moreover, the architecture of the proposed model is designed with flexibility in mind, allowing for the seamless incorporation of future image enhancement models and techniques as they emerge. This adaptability ensures that the model remains relevant and effective as the field of medical imaging continues to evolve. The results of this research contribute to the ongoing effort to make advanced image processing technologies more accessible to medical professionals, thereby enhancing the quality of care provided to patients through improved diagnostic capabilities.
- 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.