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JAVA system for mammographic CADx managing

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Author(s):
Bruno Roberto Nepomuceno Matheus
Total Authors: 1
Document type: Doctoral Thesis
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD)
Defense date:
Examining board members:
Homero Schiabel; Leonardo Vidal Batista; Antonio Adilton Oliveira Carneiro; Marco Antônio Gutierrez; Regina Bitelli Medeiros
Advisor: Homero Schiabel
Abstract

Studies show that most diagnostic errors are linked to classification difficulties and not detection (MEYER, EBERLEIN, et al., 1990; KARSSEMEIJER, 2011; SCHIABEL, 2014). A possible solution for this problem is the construction of a CAD (Computer aided Diagnosis), a computational system that analyses the available information e tries to present a diagnosis based on the data offered by the processed image. This thesis presents a complete and functional mammographic CADe/Dx scheme for radiologist use. The software is designed to function in any operational system, or even online, allowing any interested radiologist to access the software as a second opinion. The formation of a JAVA library also allows any future developers can use all tools developed for this system, easing future improvements in the CADx scheme. Several modules of the scheme previously developed for the CADx-LAPIMO prototype had to be rebuilt or completely developed, generating new results that are analyzed in here, as are their advantages and limitations. Those modules are divided in two parts, the preprocessing, that includes the scanner\'s characteristic curve based correction, detailed tested in this thesis and the processing itself, including detection of microcalcifications, and detections and classification of masses. The CADx scheme developed here was separated in two versions (each one with a corresponding online version): one is a CADe/Dx scheme that involves both detection and classification of the found structures and the other is a CADx semi-automatic scheme, where the classified regions are previously marked by the user. The main results obtained in this thesis are associated with the microcalcifications detector and the mass classification. The microcalcifications detector obtained a 89% sensibility with 1,4 false-positives per image when used in digital FFDM systems and 99% sensibility with 5,4 false-positives per image in digitalized images of dense breasts. The mass classification module presented a 72% accuracy, using only 4 attributes associated to contour, density and texture, resulting in a robust system and of easy training. (AU)