A medical image processing and analysis framework

Download
2011
Çevik, Alper
Medical image analysis is one of the most critical studies in field of medicine, since results gained by the analysis guide radiologists for diagnosis, treatment planning, and verification of administered treatment. Therefore, accuracy in analysis of medical images is at least as important as accuracy in data acquisition processes. Medical images require sequential application of several image post-processing techniques in order to be used for quantification and analysis of intended features. Main objective of this thesis study is to build up an application framework, which enables analysis and quantification of several features in medical images with minimized input-dependency over results. Intended application targets to present a software environment, which enables sequential application of medical image processing routines and provides support for radiologists in diagnosis, treatment planning and treatment verification phases of neurodegenerative diseases and brain tumors; thus, reducing the divergence in results of operations applied on medical images. In scope of this thesis study, a comprehensive literature review is performed, and a new medical image processing and analysis framework - including modules responsible for automation of separate processes and for several types of measurements such as real tumor volume and real lesion area - is implemented. Performance of the fully-automated segmentation module is evaluated with standards introduced by Neuro Imaging Laboratory, UCLA; and the fully-automated registration module with Normalized Cross-Correlation metric. Results have shown a success rate above 90 percent for both of the modules. Additionally, a number of experiments have been designed and performed using the implemented application. It is expected for an accurate, flexible, and robust software application to be accomplished on the basis of this thesis study, and to be used in field of medicine as a contributor by even non-engineer professionals.

Suggestions

PHP applications, K-wave simulations and experimental studies for medical ultrasound
Kulga, Utku Baran; Gençer, Nevzat Güneri; Department of Electrical and Electronics Engineering (2017)
This study has three parts related to theoretical calculations, numerical simulations and experimental studies in the field of medical ultrasound. The goal of the first part is to prepare computer codes for the education of biomedical engineering students. For this purpose, a series of computer applications is prepared using the hypertext preprocessor (PHP) programming language. Accessing the associated internet address, the students will be able to learn the basics of ultrasound using the interactive, visu...
Representation of human brain by mesh networks
Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
A Semantic Transformation Methodology for the Secondary Use of Observational Healthcare Data in Postmarketing Safety Studies
Pacaci, Anil; Gonul, Suat; Sinaci, A. Anil; Yuksel, Mustafa; Laleci Erturkmen, Gokce B. (Frontiers Media SA, 2018-4-30)
Background: Utilization of the available observational healthcare datasets is key to complement and strengthen the postmarketing safety studies. Use of common data models (CDM) is the predominant approach in order to enable large scale systematic analyses on disparate data models and vocabularies. Current CDM transformation practices depend on proprietarily developed Extract-Transform-Load (ETL) procedures, which require knowledge both on the semantics and technical characteristics of the source datasets an...
Nucleic acid based in-vitro diagnostic device
Balcı, Oğuz; Son, Çağdaş Devrim; Özen, Can; Department of Biotechnology (2018)
Commonly used nucleic acid based in-vitro diagnostic systems utilize either quantitative PCR (polymerase chain reaction) or conventional PCR. Quantitative PCR is a fast and reliable method; however, the prices of quantitative PCR devices are relatively expensive due to the cost of highly sensitive sensors. Equipment using conventional PCR is lower in price but have several disadvantages such as long analysis periods, contamination risk, false positivity risk, and usage of carcinogenic chemicals. The purpose...
Fault detection and diagnosis in nonlinear dynamical systems
Kılıç, Erdal; Leblebicioğlu, Mehmet Kemal; Department of Electrical and Electronics Engineering (2005)
The aim of this study is to solve Fault Detection and Diagnosis (FDD) problems occurring in nonlinear dynamical systems by using model and knowledge-based FDD methods and to give a priority and a degree about faults. For this purpose, three model-based FDD approaches, called FDD by utilizing principal component analysis (PCA), system identification based FDD and inverse model based FDD are introduced. Performances of these approaches are tested on different nonlinear dynamical systems starting from simple t...
Citation Formats
A. Çevik, “A medical image processing and analysis framework,” M.S. - Master of Science, Middle East Technical University, 2011.