Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms

Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could be due to the use of an undersized device, or to vascular anatomy and clinical condition of the patient. We report a machine learning model based on over 100 clinical and imaging features that predict the outcome of wide-neck bifurcation aneurysm treatment with an intravascular embolization device. We combine clinical features with a diverse set of common and novel imaging measurements within a random forest model. We also develop neural network segmentation algorithms in 2D and 3D to contour the sac in angiographic images and automatically calculate the imaging features. These deliver 90% overlap with manual contouring in 2D and 83% in 3D. Our predictive model classifies complete vs. partial occlusion outcomes with an accuracy of 75.31%, and weighted F1-score of 0.74.Comment: 10 page

Ontoloji tabanlı tıbbi görüntü betimleme ve erişimi

Bu tez kapsamında, mamografi incelemelerinde kullanılmak üzere yeni bir ontoloji tabanlı tıbbi resim betimleme ve geri getirim sistemi önerilmiştir. Bu amaçla ilk olarak mamografi incelemelerde kullanılmak üzere ortak bir kelime haznesi sağlayan yeni bir mamografi betimleme ontolojisi geliştirdik. Daha sonrasında, veri setimizi oluşturmak amacıyla, ontolojimiz ile uyumlu, yeni bir ontoloji tabanlı mamografi betimleme ve geri getirim uygulaması geliştirdik. Sonrasında, her bir meme kitlesinin üç farklı seviyede öznitelik (yüksek, orta ve düşük) ile temsil edildiği içerik tabanlı resim geri getirim modelimizi geliştirdik. İlgili modelin matematiksel modeli SQWRL ve XQuery kullanılarak uygulamaya geçirilmesine ilişkin detaylar tez içersinde verilmiştir. İçerik tabanlı resim geri getirim sistemimizi test etmek amacıyla bir grup sorguyu veri setimiz üzerinde çalıştırdık. Ayrıca, mamografi incelemeleri sırasında ortaya çıkabilen belirsiz durumları modellemek üzere yeni bir yaklaşım önerdik ve verilen bir meme kitlesinin BI RADS skorunu belirmek için SQWRL kuralları geliştirdik. Yapılan deneyler sonucunda, mamografi incelemeleri sırasında BI RADS skorlarının belirlenmesi aşamasında bir belirsizlik durumunun olduğu ve formüle edilen belirsizlik seviyesinin kesin mantık için bizim yaklaşımımızdan açık bir şekilde daha yüksek olduğu görülmüştür. Ek olarak, içerik tabanlı resim geri getirim sistemlerinde, düşük seviyeli özniteliklerin, yüksek ve orta seviyeli öznitelikler ile birlikte kullanılması, sistem performansını iyileştirmiştir. Bu iyileştirme istatistiksel olarak anlamlı bulunmuştur (p küçüktür 0.001). In this thesis, we proposed a new ontology based medical image annotation and retrieval system for mammographic examinations. For that purpose, we have first developed a mammography annotation ontology (MAO) which is a domain ontology and it provides shared vocabulary for mammography interpretation. Then we have developed a new ontology based mammography annotation and retrieval tool (MART) to create our mammography dataset. Then, we have developed a content based image retrieval system where a breast mass is described with three sets of features: low, mid and high-level feature. Mathematical model of similarity calculation between two breast lesions and implementation of the model with SQWRL and XQuery explained in detail. To test our CBIR system, we performed set of queries on the DEMS. Furthermore, we present an approach to model uncertainty in mammography, and perform SQWRL rules to infer BI RADS scores for a given mass instance. Experimentation results showed that uncertainty exists in interpretation of BI RADS scoring in mammography and average level of uncertainty for crisp logic is clearly greater than our approach. Additionally, we show that using low level features together with high and mid level features in the content based image retrieval of breast masses improves the overall system performance and it is found statistically significant (p is lower than 0.001)

Comparison of 3D segmentation algorithms for medical imaging

In this paper we present an evaluation of four different 3D segmentation algorithms with respect to their performance on three different CT Data Sets. The segmentation algorithms evaluated in this study are seeded region growing, volumetric segmentation using WEIBULL E SD fields, automatic multilevel thresholding by using OTSU method and unseeded region growing. The main results gained from our experimentation and implementation details are presented

Ontology-based mammography annotation and Case-based Retrieval of breast masses

This paper describes ontology-based annotation of mammography and a Case-based Retrieval approach for breast masses from digital mammography archive. We first present our Mammography Annotation Ontology focusing on its main concepts and relationships, as well as the annotation tool. Then, we propose a model for similarity calculation between breast masses based on their high, mid and low-level features. We use Semantic Query-enhanced Web Rule Language (SQWRL) to process retrieval of similar masses from annotated mammography collection in OWL We give both retrieving process and results we obtained from experimentations, in detail. (C) 2012 Elsevier Ltd. All rights reserved

Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring

This paper presents an ontology-based annotation system and BI-RADS (Breast Imaging Reporting and Data System) score reasoning with Semantic Web technologies in mammography. The annotation system is based on the Mammography Annotation Ontology (MAO) where the BI-RADS score reasoning works. However, ontologies are based on crisp logic and they cannot handle uncertainty. Consequently, we propose a Bayesian-based approach to model uncertainty in mammography ontology and make reasoning possible using BI-RADS scores with SQWRL (Semantic Query-enhanced Web Rule Language). First, we give general information about our system and present details of mammography annotation ontology, its main concepts and relationships. Then, we express uncertainty in mammography and present approaches to handle uncertainty issues. System is evaluated with a manually annotated dataset DEMS (Dokuz Eylul University Mammography Set) and DDSM (Digital Database for Screening Mammography). We give the result of experimentations in terms of accuracy, sensitivity, precision and uncertainty level measures. (c) 2013 Elsevier Ltd. All rights reserved

Deride renk farkı formülleri üzerine bir araştırma

Nearly for thirty years. However, tolerance limits of this system may vary from the visual system. New color difference formulas showing similar tolerance properties like the visual system, are being developed. In this research, color differences of finished leathers have been calculated with new formulas like CMC, CIE94, CIE 2000 and the results have been compared to CIELab formula. These formulas found to be statistically different from the CIELab system and had smaller averages. The differences of formulas were found to be more evident for colors like yellow, red and less evident for brown, mustard colors.CIE tarafından 1976 yılında sunulan CIELab renk farkı formülü, endüstri tarafından kabul görmüş ve otuz yıla yakın süredir yaygın olarak kullanılmaktadır. Ancak bu sistemin tolerans sınırlan, görme sistemine göre farklılık gösterebilmektedir. Görme sistemine benzer tolerans özellikleri gösteren yeni renk farkı formülleri geliştirilmektedir. Bu araştırmada, finisaj işlemi uygulanmış mamul deriler üzerindeki renk farklılıkları CMC, C1E94 ve CIE2000 gibi yeni formüllerle hesaplanmış ve klasik CIELab formülü sonuçlan ile karşılaştınlmıştır. Bu formüllerin CIELab sisteminden farklı sonuç verdikleri, daha düşük ortalamaya sahip oldukları; formüller arasındaki farkın san, kınnızı gibi renklerde daha belirgin olduğu; kahve, hardal gibi renklerde ise farkın azaldığı bulunmuştur

Low Level Feature Selection for a Content Based Digital Mammography Image Retrieval System

Content Based Image Retrieval (CBIR) systems enables to retrieve images from large image archieves based on its contents as well as external attributes associated to each image. This study aims at extracting low level attributes to be used in a CBIR model that enables the utilization of low, level image based attributes together with high level concepts. The contribution of this study is to develop an infrastructure for the selection of best low level attribute set to be used in the CBIR method by considering model performance. Within the scope of this study: segmentation of mammogram images, development of a mammogram database, low level attribute extraction from the segmented images and breast type estimation by means of machine learning algorithms are realized

Space Weather Studies Of Ionolab Group

IONOLAB is an interdisciplinary research group dedicated for handling the challenges of near earth environment on communication, positioning and remote sensing systems. IONOLAB group contributes to the space weather studies by developing state-of-the-art analysis and imaging techniques. On the website of IONOLAB group, www.ionolab.org, four unique space weather services, namely, IONOLAB-TEC, IRI-PLAS-2015, IRI-PLAS-MAP and IRI-PLAS-STEC, are provided in a user friendly graphical interface unit. Newly developed algorithm for ionospheric tomography, IONOLAB-CIT, provides not only 3-D electron density but also tracking of ionospheric state with high reliability and fidelity. The algorithm for ray tracing through ionosphere, IONOLAB-RAY, provides a simulation environment in all communication bands. The background ionosphere is generated in voxels where IRI-Plas electron density is used to obtain refractive index. One unique feature is the possible update of ionospheric state by insertion of Total Electron Content (TEC) values into IRI-Plas. Both ordinary and extraordinary paths can be traced with high ray and low ray scenarios for any desired date, time and transmitter location. 2-D regional interpolation and mapping algorithm, IONOLAB-MAP, is another tool of IONOLAB group where automatic TEC maps with Kriging algorithm are generated from GPS network with high spatio-temporal resolution. IONOLAB group continues its studies in all aspects of ionospheric and plasmaspheric signal propagation, imaging and mapping.Wo