Quantitative description of 3D vascularity images: texture-based approach and its verification through cluster analysis

El propósito de este artículo es describir la poética de Teillier durante los años 60 y 70 en poemas breves, muchos de los cuales se revelan como haikús. Este aspecto de la obra de Teillier es poco atendido por la crítica y no solamente se verifica a través en textos que en gran medida se asimilan al haikú japonés clásico. Así este autor encuentra una consonancia más profunda y con el término “morada irreal” de Basho, que expresa la fragmentariedad de lo real, al menos de esa parte del mundo circundante que revela insospechadas conexiones con otro tiempo y lugar. The objective of this article is to describe Teillier's poetics during the 1960s and 1970s in short poems, many of which are revealed as haiku. This aspect of Teillier's work is poorly served by criticism and is not only verified through texts that are largely assimilated to classical Japanese haiku. Thus this author finds a deeper consonance and with the term "morada irreal" of Basho, which expresses the fragmentarity of the real, at least of that part of the surrounding world that reveals unsuspected connections with another time and place.El propòsit d'aquest article es descriure la poètica de Teillier durant els anys 60 y 70 en poemes breus, molts dels quals es revelen com haikus. Aquest aspecte de l'obra de Teillier és poc atès per la crítica i no solament es verifica a través de textos que en gran mesura s'asimilen al haiku japonès clàssic. Així aquest autor troba una consonància més profunda i amb el terme “morada irreal” de Basho, que expressa la fragmentarietat d'allò real, almenys d'aquella part del món circumdant que revela insospitades connexions amb un altre temps i lloc

Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135074/1/mp4558.pd

Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation

Purpose: Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries / veins classi cation are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases. Methods: We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A non-local total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins. Results: The proposed segmentation method yields competitive results on three pub- lic datasets (STARE, DRIVE, and IOSTAR), and it has superior performance when com- pared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to ve public databases 1 (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries / veins classi cation based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively. Conclusions: The experimental results show that the proposed framework has e ectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology recon- struction. The vascular topology information signi cantly improves the accuracy on arteries / veins classi cation

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed

Analysis of Retinal Image Data to Support Glaucoma Diagnosis

Fundus kamera je široce dostupné zobrazovací zařízení, které umožňuje relativně rychlé a nenákladné vyšetření zadního segmentu oka – sítnice. Z těchto důvodů se mnoho výzkumných pracovišť zaměřuje právě na vývoj automatických metod diagnostiky nemocí sítnice s využitím fundus fotografií. Tato dizertační práce analyzuje současný stav vědeckého poznání v oblasti diagnostiky glaukomu s využitím fundus kamery a navrhuje novou metodiku hodnocení vrstvy nervových vláken (VNV) na sítnici pomocí texturní analýzy. Spolu s touto metodikou je navržena metoda segmentace cévního řečiště sítnice, jakožto další hodnotný příspěvek k současnému stavu řešené problematiky. Segmentace cévního řečiště rovněž slouží jako nezbytný krok předcházející analýzu VNV. Vedle toho práce publikuje novou volně dostupnou databázi snímků sítnice se zlatými standardy pro účely hodnocení automatických metod segmentace cévního řečiště.Fundus camera is widely available imaging device enabling fast and cheap examination of the human retina. Hence, many researchers focus on development of automatic methods towards assessment of various retinal diseases via fundus images. This dissertation summarizes recent state-of-the-art in the field of glaucoma diagnosis using fundus camera and proposes a novel methodology for assessment of the retinal nerve fiber layer (RNFL) via texture analysis. Along with it, a method for the retinal blood vessel segmentation is introduced as an additional valuable contribution to the recent state-of-the-art in the field of retinal image processing. Segmentation of the blood vessels also serves as a necessary step preceding evaluation of the RNFL via the proposed methodology. In addition, a new publicly available high-resolution retinal image database with gold standard data is introduced as a novel opportunity for other researches to evaluate their segmentation algorithms.

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks