An Artificial Immune-Activated Neural Network Applied to Brain 3D MRI Segmentation

Younis, Akmal A.; Ibrahim, Mohamed O.; Kabuka, Mansur R.; John, Nigel

doi:10.1007/s10278-007-9081-0

Cited by 19 publications

(15 citation statements)

References 79 publications

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“…There are number of algorithms being proposed in the field of medical image segmentation [7]. These techniques are broadly classified into four categories: methods based on gray level features, methods based on texture features, model-based segmentation methods, and atlas-based segmentation methods [8][9][10][11][12][13].…”

Section: Skull Stripping Of Mr Brain Imagesmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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Section: Skull Stripping Of Mr Brain Imagesmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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“…En algunos casos [11] no es necesaria esta etapa; en otros, por ejemplo [25], los artefactos presentes en las imágenes médicas suelen eliminarse manualmente, algunos de estos son derivados de los aparatos con los que se toman las muestras [21,26]. Muchos autores coinciden en que una de las técnicas más importantes en esta etapa es el filtrado de imágenes [27], que ayuda en el mejoramiento de una imagen con el fin de adquirir información; dentro de la literatura citada se dividen en 2 grupos: 1) De acuerdo a su dominio de frecuencia: Este tipo de filtrado es usado comúnmente en los EEG, por ejemplo usando la transformada de Fourier [21,24,28,29], se elimina la presencia de ruido ocasionados por parpadeos oculares, actividad muscular, etc. [30].…”

Section: Pre-procesamiento De Los Datosunclassified

Estado del arte y elementos del reconocimiento automático de imágenes del cerebro

Cortés-Martínez¹,

Mejía-Lavalle²

2017

RCS

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Resumen. El área de la Inteligencia Artificial ha incursionando en el ámbito médico con el propósito de apoyar en el reconocimiento automático de imágenes del cerebro. Una de las enfermedades que ha ido en aumento en la última década es la Enfermedad Cerebro Vascular, cuyo conjunto de enfermedades, afectan los vasos sanguíneos cerebrales. Dados los efectos que puede causar este tipo de enfermedad, es importante caracterizar las fuentes que generan imágenes médicas y los procedimientos empleados para mejorarlas e interpretarlas. El documento describe los avances reportados en la literatura en relación a las fuentes que obtienen las imágenes y las etapas seguidas para su reconocimiento. El trabajo considera que existe la oportunidad de mejorar o desarrollar más algoritmos avanzados, que ofrezca un nivel más alto en la detección de enfermedades en imágenes del cerebro. Palabras clave: enfermedad cerebro vascular, reconocimiento de imágenes médicas, inteligencia artificial, técnicas de visión artificial. State of the Art and Elements of Automatic Image Recognition of the Brain Abstract. Artificial Intelligence area has penetrated the medical field with the purpose of supporting the automatic recognition of brain imaging. One of the diseases that has been increasing in the last decade are Brain Stroke, whose set of diseases, affect the cerebral blood vessels. Given that effects that can cause this type of disease, it is important to characterize the sources that generate medical images and the methods used to improve and interpret them. This paper describes the advances reported in the literature in relation to the sources that obtain the images and the stages followed for their recognition. Moreover considers that there is an opportunity to improve and development of advanced algorithms, which will offer a higher level in the detection of diseases in brain images.

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“…For any input MRI studies, the square w × w blocks are selected automatically to tightly cover the smallest possible diameter as a function of the given pixel sizes calculated from the input resolution and field of view. For the size We used our preprocessing technique used before in [23] that starts with applying contrast-brightness correction to maximize the intersection between the histogram of the training and segmentation datasets followed by using 3D anisotropic filter to avoid empty histogram bins. The position features are the slice relative location with reference to the bottom slice and the radial Euclidean distance between the block top pixel and the center of the slice normalized by dividing it by the longest diameter of the slice.…”

Section: Textural Based Detection Of Initial Ms Lesions Regionsmentioning

confidence: 99%

Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

Abdullah¹,

Younis²,

Pattany³

et al. 2011

OJMI

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In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI). The technique uses textural features to describe the blocks of each MRI slice along with position and neighborhood features. A trained support vector machine (SVM) is used to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions based on mainly the textural features with aid of the other features. The MRI slice blocks' classification is used to provide an initial segmentation. A comprehensive post processing module is then utilized to refine and improve the quality of the initial segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated process without the need to manually define regions of interest (ROIs). In addition, the post processing module is generic enough to be applied to the results of any other MS segmentation technique to improve the segmentation quality. This technique is evaluated using ten real MRI data-sets with 10% used in the training of the textural-based SVM. The average results for the performance evaluation of the presented technique were 0.79 for dice similarity, 0.68 for sensitivity and 0.9 for the percentage of the detected lesion load. These results indicate that the proposed method would be useful in clinical practice for the detection of MS lesions from MRI.

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An Artificial Immune-Activated Neural Network Applied to Brain 3D MRI Segmentation

Cited by 19 publications

References 79 publications

Methods on Skull Stripping of MRI Head Scan Images—a Review

Methods on Skull Stripping of MRI Head Scan Images—a Review

Estado del arte y elementos del reconocimiento automático de imágenes del cerebro

Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

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