Spatial based Expectation Maximizing (EM)

<p>Abstract</p> <p>Background</p> <p>Expectation maximizing (EM) is one of the common approaches for image segmentation.</p> <p>Methods</p> <p>an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.</p> <p>Results</p> <p>the findings show that the proposed algorithm produces higher similarity index.</p> <p>Conclusions</p> <p>experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.</p

Automatic MRI 2D Brain Segmentation using Graph SearchingTechnique

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stabilit

Comparative study of clustering algorithms in order to virtual histology (VH) image segmentation

Atherosclerosis is the deadliest type of heart disease caused by soft or “vulnerable” plaque (VP) formation in the coronary arteries. Recently, Virtual Histology (VH) has been proposed based on spectral analysis of Intravascular Ultrasound (IVUS) provides color code of coronary tissue maps. Based on pathophysiological studies, obtaining information about existence and extension of confluent pool’s component inside plaque is important. In addition, plaque components’ localization respect to the luminal border has major role in determining plaque vulnerability and plaque–stent interaction. Computational methods were applied to prognostic the pattern's structure of each component inside the plaque. The first step for post-processing of VH methodology to get further information of geometrical features is segmentation or decomposition. The medical imaging segmentation field has developed to assist cardiologist and radiologists and reduce human error in recent years as well. To perform color image clustering, several strategies can be applied which include traditional hierarchical and nonhierarchical. In this paper, we applied and compared four nonhierarchical clustering methods consists of Fuzzy C-means (FCM), Intuitionistic Fuzzy C-means (IFCM), K-means and SOM artificial neural networks in order to automate segmentation of the VH-IVUS images

Formulating efficient software solution for digital image processing system

© 2015 John Wiley & Sons, Ltd. Digital image processing systems are complex, being usually composed of different computer vision libraries. Algorithm implementations cannot be directly used in conjunction with algorithms developed using other computer vision libraries. This paper formulates a software solution by proposing a processor with the capability of handling different types of image processing algorithms, which allow the end users to install new image processing algorithms from any library. This approach has other functionalities like capability to process one or more images, manage multiple processing jobs simultaneously and maintain the manner in which an image was processed for later use. It is a computational efficient and promising technique to handle variety of image processing algorithms. To promote the reusability and adaptation of the package for new types of analysis, a feature of sustainability is established. The framework is integrated and tested on a medical imaging application, and the software is made freely available for the reader. Future work involves introducing the capability to connect to another instance of processing service with better performance

A method for body fat composition analysis in abdominal magnetic resonance images via self-organizing map neural network

Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in axial magnetic resonance (MR) images of the abdomen. Materials and Methods: A self-organizing map (SOM) neural network was designed to segment the adipose tissue from other tissues in the MR images. The segmentation of SAT and VAT was accomplished using a new level set method called distance regularized level set evolution (DRLSE). To evaluate the suggested method, the whole-body abdominal MRI was performed on 23 subjects, and three slices were selected for each case. Results: The results of the automatic segmentation were compared with those of the manual segmentation and previous artificial intelligent methods. According to the results, there was a significant correlation between the automatic and manual segmentation results of VAT and SAT. Conclusion: As the findings indicated, the suggested method improved detection of body fat. In this study, a fully automated abdominal adipose tissue segmentation algorithm was suggested, which used the SOM neural network and DRLSE level set algorithm. The proposed methodology was concluded to be accurate and robust with a significant advantage over the manual and previous segmentation methods in terms of speed and accuracy. © 2018, Mashhad University of Medical Sciences

Segmentación Automática del Cerebro mediante Técnicas de Tratamiento de Imagen

Dada la gran importancia del cerebro para los seres humanos, se están realizando una serie de investigaciones mediante el tratamiento digital de la imagen, que facilitan a los médicos la detección de enfermedades cerebrales. Este proyecto se considera el primer paso para muchas de estas investigaciones, debido a que se basa en segmentar automáticamente el cerebro, y extraerlo del cráneo. En este proyecto se han investigado diferentes formas de realizar este paso, todas ellas realizando una variación del método watershed. Para concluís este proyecto, se comparan resultados y se obtiene que el método que presenta la mejor solución es el método llamado watershed pseudoestocástico.Cabanilles Mengual, P. (2014). Segmentación Automática del Cerebro mediante Técnicas de Tratamiento de Imagen. http://hdl.handle.net/10251/37996.Archivo delegad

Diseño e implementación de un prototipo para el control de gestión de inventario del producto terminado en la Fábrica de Cueros El AL-CE basado en inteligencia artificial

El presente trabajo describe las actividades desarrolladas para el diseño e implementación de un prototipo basado en inteligencia artificial para el control del inventario de productos terminados en la fábrica de cueros el AL-CE, con la finalidad de mejorar el manejo del inventario y automatizar el control de los ingresos y egresos del almacén. Mediante listas de comprobación, entrevistas y visitas de observación se determinó la situación del control de inventarios vigente, identificando sus debilidades y fortalezas. Los resultados de esta revisión permitieron reconocer las necesidades de la empresa y con ello los requerimientos que debe cumplir el nuevo control de inventarios. Posterior a la calificación de posibles alternativas de control a implementar con el método de ponderación de factores, se optó por el siguiente conjunto de elementos: aplicación web de control de inventarios, aplicación con inteligencia artificial para la predicción de ventas y un autómata encargado de transportar el producto a su ubicación en el almacén. Al final, se evaluaron los componentes del prototipo obteniendo los siguientes resultados: las evaluaciones del autómata muestran una efectividad del 70 al 90% en relación a las salidas, un 90 a 95% de eficiencia en el seguimiento de trayectoria. La aplicación de predicción de ventas muestra una velocidad de respuesta entre 1.8 a 2.1 segundos. Además, se observó una relación inversamente proporcional entre el porcentaje de error de predicción y el número de registros de venta por producto. En conclusión, se diseñó e implemento el prototipo de control de inventario en la empresa de cueros el AL-CE y como resultado hubo una mejora cualitativa sobre el control de los productos en el almacén. Se recomeinda a la empresa alojar la aplicación web de control de inventario en un servidor, permitiendo el acceso rápido y sencillo a la información a través de internet.This work describes the activities developed for the design and implementation of a prototype based on artificial intelligence for the inventory control of finished products in the AL-CE leather factory, to improve the inventory management and automate the control of the inputs and outputs of the warehouse. Through checklists, interviews and observation visits, the current inventory control situation was determined, identifying its weaknesses and strengths. The results of this review made it possible to identify the company's needs and thus the requirements to be met by the new inventory control system. After the qualification of possible control alternatives to be implemented with the factor weighting method, the following set of elements was chosen inventory control web application, application with artificial intelligence for sales prediction and an automaton in charge of transporting the product to its location in the warehouse. At the end, the components of the prototype were evaluated, obtaining the following results: the automaton evaluations show an effectiveness of 70 to 90% concerning the outputs, 90 to 95% efficiency in trajectory tracking. The sales prediction application shows a response speed between 1.8 to 2.1 seconds. In addition, an inversely proportional relationship was observed between the percentage of prediction error and the number of sales records per product. In conclusion, the inventory control prototype was designed and implemented in the ALCE leather company and as a result there was a qualitative improvement in the control of the products in the warehouse. It is recommended to the company to host the inventory control web application on a server, allowing quick and easy access to information through the Internet

Functional and structural MRI image analysis for brain glial tumors treatment

This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient