Detection of Abnormality in Endoscopic Images using Endoscopic Technique

Medical imaging has been undergoing a revolution in the past decade with the advent of faster, more accurate and less invasive devices. This has driven the need for corresponding software development which in turn has provided a major impetus for new algorithms in signal and image processing. Digital image processing is important for many biomedical applications. The medical images analyzed, used as diagnostic tools and quite often provide insight into the inner working of the process under study. The commonly found abnormalities in endoscopic images are cancer tumors, ulcers, bleeding due to internal injuries, etc. The segmented method is used to segment the tumor, abnormal regions and cancerous growth in the human esophagus. In our proposed work, a method for detecting possible presence of abnormality in the endoscopic images is presented. An algorithm is to develop to perform the segmentation, classification and analysis of medical images, especially the endoscopic images for the identification of commonly occurring abnormalities in it

Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance

International audienceAbstract. Esophageal adenocarcinoma arises from Barrett’s esophagus, which is the most serious complication of gastroesophageal reflux disease. Strategies for screening involve periodic surveillance and tissue biopsies. A major challenge in such regular examinations is to record and track the disease evolution and re-localization of biopsied sites to provide targeted treatments. In this paper, we extend our original inter-operativerelocalization framework to provide a constrained image based search for obtaining the best view-point match to the live view. Within this context we investigate the effect of, (a) the choice of feature descriptors and color-space, (b) filtering of uninformative frames, (c) endoscopic modality, for view-point localization. Our experiments indicate an improvement in the best view-point retrieval rate to [92%, 87%] from [73%, 76%] (in our previous approach) for NBI and WL

Metodologia para criação de descritores de Capsicum ssp através de imagens

Trabalho de Conclusão de Curso (Graduação)As texturas em imagens proporcionam diversas informações sobre os objetos em estudo. Diversas técnicas existentes na literatura utilizam essas texturas em imagens para descrevê-las e classificá-las. Neste trabalho foram analisados os descritores HOG, LBP, SIFT, SURF e estatísticas de primeira ordem para imagens de pimentas (Capsicum ssp). Foram avaliadas as uniões dos descritores analisados e obteve-se uma acurácia acima de 90% com uma quantidade reduzida de descritores. Para melhor validação do método proposto, utilizou-se as bases de imagens públicas Brodatz, Vistex, Outex e UIUCTex e, o método proposto novamente conseguiu resultados superiores aos da literatura

Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

Information geometry

This Special Issue of the journal Entropy, titled “Information Geometry I”, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience