571 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Brain Tumor Detection and Segmentation in Multisequence MRI
Tato prĂĄce se zabĂœvĂĄ detekcĂ a segmentacĂ mozkovĂ©ho nĂĄdoru v multisekvenÄnĂch MR obrazech se zamÄĆenĂm na gliomy vysokĂ©ho a nĂzkĂ©ho stupnÄ malignity. Jsou zde pro tento ĂșÄel navrĆŸeny tĆi metody. PrvnĂ metoda se zabĂœvĂĄ detekcĂ prezence ÄĂĄstĂ mozkovĂ©ho nĂĄdoru v axiĂĄlnĂch a koronĂĄrnĂch Ćezech. JednĂĄ se o algoritmus zaloĆŸenĂœ na analĂœze symetrie pĆi rĆŻznĂœch rozliĆĄenĂch obrazu, kterĂœ byl otestovĂĄn na T1, T2, T1C a FLAIR obrazech. DruhĂĄ metoda se zabĂœvĂĄ extrakcĂ oblasti celĂ©ho mozkovĂ©ho nĂĄdoru, zahrnujĂcĂ oblast jĂĄdra tumoru a edĂ©mu, ve FLAIR a T2 obrazech. Metoda je schopna extrahovat mozkovĂœ nĂĄdor z 2D i 3D obrazĆŻ. Je zde opÄt vyuĆŸita analĂœza symetrie, kterĂĄ je nĂĄsledovĂĄna automatickĂœm stanovenĂm intenzitnĂho prahu z nejvĂce asymetrickĂœch ÄĂĄstĂ. TĆetĂ metoda je zaloĆŸena na predikci lokĂĄlnĂ struktury a je schopna segmentovat celou oblast nĂĄdoru, jeho jĂĄdro i jeho aktivnĂ ÄĂĄst. Metoda vyuĆŸĂvĂĄ faktu, ĆŸe vÄtĆĄina lĂ©kaĆskĂœch obrazĆŻ vykazuje vysokou podobnost intenzit sousednĂch pixelĆŻ a silnou korelaci mezi intenzitami v rĆŻznĂœch obrazovĂœch modalitĂĄch. JednĂm ze zpĆŻsobĆŻ, jak s touto korelacĂ pracovat a pouĆŸĂvat ji, je vyuĆŸitĂ lokĂĄlnĂch obrazovĂœch polĂ. PodobnĂĄ korelace existuje takĂ© mezi sousednĂmi pixely v anotaci obrazu. Tento pĆĂznak byl vyuĆŸit v predikci lokĂĄlnĂ struktury pĆi lokĂĄlnĂ anotaci polĂ. Jako klasifikaÄnĂ algoritmus je v tĂ©to metodÄ pouĆŸita konvoluÄnĂ neuronovĂĄ sĂĆ„ vzhledem k jejĂ znĂĄme schopnosti zachĂĄzet s korelacĂ mezi pĆĂznaky. VĆĄechny tĆi metody byly otestovĂĄny na veĆejnĂ© databĂĄzi 254 multisekvenÄnĂch MR obrazech a byla dosĂĄhnuta pĆesnost srovnatelnĂĄ s nejmodernÄjĆĄĂmi metodami v mnohem kratĆĄĂm vĂœpoÄetnĂm Äase (v ĆĂĄdu sekund pĆi pouĆŸitĂœ CPU), coĆŸ poskytuje moĆŸnost manuĂĄlnĂch Ășprav pĆi interaktivnĂ segmetaci.This work deals with the brain tumor detection and segmentation in multisequence MR images with particular focus on high- and low-grade gliomas. Three methods are propose for this purpose. The first method deals with the presence detection of brain tumor structures in axial and coronal slices. This method is based on multi-resolution symmetry analysis and it was tested for T1, T2, T1C and FLAIR images. The second method deals with extraction of the whole brain tumor region, including tumor core and edema, in FLAIR and T2 images and is suitable to extract the whole brain tumor region from both 2D and 3D. It also uses the symmetry analysis approach which is followed by automatic determination of the intensity threshold from the most asymmetric parts. The third method is based on local structure prediction and it is able to segment the whole tumor region as well as tumor core and active tumor. This method takes the advantage of a fact that most medical images feature a high similarity in intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with -- and even exploiting -- this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the ``local structure prediction'' of local label patches. Convolutional neural network is chosen as a learning algorithm, as it is known to be suited for dealing with correlation between features. All three methods were evaluated on a public data set of 254 multisequence MR volumes being able to reach comparable results to state-of-the-art methods in much shorter computing time (order of seconds running on CPU) providing means, for example, to do online updates when aiming at an interactive segmentation.
Computer assisted enhanced volumetric segmentation magnetic imaging data using a mixture of artificial neural networks
An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest. The method was applied to magnetic resonance imaging data collected from an experimental acute inflammatory model (T(2) weighted) and from a clinical study of human Alzheimer's disease (T(1) weighted) to evaluate the proposed method. In the first case, a high correlation and parallelism was registered between the volumetric measurements, of the injured and healthy tissue, by the proposed method with respect to the manual measurements (r = 0.82 and p < 0.05) and to the histopathological studies (r = 0.87 and p < 0.05). The method was also applied to the clinical studies, and similar results were derived of the manual and semi-automatic volumetric measurement of both hippocampus and the corpus callosum (0.95 and 0.88
Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study
Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer
Advances in Analysis and Exploration in Medical Imaging
With an ever increasing life expectancy, we see a concomitant increase in diseases capable of disrupting normal cognitive processes. Their diagnoses are difficult, and occur usually after daily living activities have already been compromised. This dissertation proposes machine learning methods for the study of the neurological implications of brain lesions. It addresses the analysis and exploration of medical imaging data, with particular emphasis to (f)MRI. Two main research directions are proposed. In the first, a brain tissue segmentation approach is detailed. In the second, a document mining framework, applied to reports of neuroscientific studies, is described. Both directions are based on retrieving consistent information from multi-modal data.
A contribution in this dissertation is the application of a semi-supervised method, discriminative clustering, to identify different brain tissues and their partial volume information. The proposed method relies on variations of tissue distributions in multi-spectral MRI, and reduces the need for a priori information. This methodology was successfully applied to the study of multiple sclerosis and age related white matter diseases. It was also showed that early-stage changes of normal-appearing brain tissue can already predict decline in certain cognitive processes.
Another contribution in this dissertation is in neuroscience meta-research. One limitation in neuroimage processing relates to data availability. Through document mining of neuroscientific reports, using images as source of information, one can harvest research results dealing with brain lesions. The context of such results can be extracted from textual information, allowing for an intelligent categorisation of images. This dissertation proposes new principles, and a combination of several techniques to the study of published fMRI reports. These principles are based on a number of distance measures, to compare various brain activity sites. Application to studies of the default mode network validated the proposed approach.
The aforementioned methodologies rely on clustering approaches. When dealing with such strategies, most results depend on the choice of initialisation and parameter settings. By defining distance measures that search for clusters of consistent elements, one can estimate a degree of reliability for each data grouping. In this dissertation, it is shown that such principles can be applied to multiple runs of various clustering algorithms, allowing for a more robust estimation of data agglomeration
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