A fuzzy approach for feature extraction of brain tissues in Non-Contrast CT

Abstract

In neuroimaging, brain tissue segmentation is a fundamental part of the techniques that seek to automate the detection of pathologies, the quantification of tissues or the evaluation of the progress of a treatment. Because of its wide availability, lower cost than other imaging techniques, fast execution and proven efficacy, Non-contrast Cerebral Computerized Tomography (NCCT) is the most used technique in emergency room for neuroradiology examination, however, most research on brain segmentation focuses on MRI due to the inherent difficulty of brain tissue segmentation in NCCT. In this work, three brain tissues were characterized: white matter, gray matter and cerebrospinal fluid in NCCT images. Feature extraction of these structures was made based on the radiological attenuation index denoted by the Hounsfield Units using fuzzy logic techniques. We evaluated the classification of each tissue in NCCT images and quantified the feature extraction technique in images from real tissues with a sensitivity of 92% and a specificity of 96% for images from cases with slice thickness of 1 mm, and 96% and 98% respectively for those of 1.5 mm, demonstrating the ability of the method as feature extractor of brain tissues. RESUMENEn neuroimagen, la segmentación de tejidos cerebrales es una parte fundamental de las técnicas que buscan automatizar la detección de patologías, la cuantificación de tejidos o la evaluación del progreso de un tratamiento. Debido a su amplia disponibilidad, menor costo que otras técnicas de imagen, rápida ejecución y eficacia probada, la tomografía computarizada cerebral sin contraste (TCNC) es la técnica mayormente utilizada en emergencias para el examen neurorradiológico, sin embargo, la dificultad inherente que representa la segmentación de los tejidos cerebrales, hace que la mayoría de las investigaciones sobre la segmentación del cerebro se centren en la resonancia magnética. En este trabajo se realizó la caracterización de tres tejidos cerebrales: sustancia blanca, sustancia gris y líquido cefalorraquídeo en imágenes TCNC. Dichas estructuras fueron caracterizadas con base en el índice de atenuación radiológica denotadas por las Unidades Hounsfield utilizando técnicas de lógica difusa. Se evaluó la caracterización de cada tejido en diversos cortes de TCNC y se cuantificó la técnica de extracción de características en imágenes a partir de tejidos reales con una sensibilidad de 92% y una especificidad de 96% para tejidos en cortes de 1 mm de grosor y 96% y 98% para los de 1.5 mm demostrando la habilidad del método como extractor de características de los tejidos cerebrales.

Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: A fuzzy approach

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A Gray-Level Dynamic Range Modification Technique for Image Feature Extraction Using Fuzzy Membership Function

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