Search results for "segmentation"

Segmentation Integrating Watershed and Shape Priors Applied to Cardiac Delayed Enhancement MR Images

2017

International audience; Background: In recent years, there has been a rapid rise in the use of shape priors applied to segmentation process of medical images. Previous approaches on left ventricle segmentation from Delayed-Enhancement Magnetic Resonance Imaging (DE-MRI) have focused on the extraction of myocardium or just diseased region in short axis orientation. However these studies did not take into account the segmentation of non-diseased myocardium from DE-MRI. The segmentation of non-diseased myocardium from DE-MRI, has some useful applications. For instance it can simplify the PET-MR registration process.Methods: This paper presents a novel semi-automatic segmentation method of non-…

The Proximity Tourism: Behaviours of Sicilian Tourists on Holiday in Cefalù

2007

FABC: Retinal Vessel Segmentation Using AdaBoost

2010

This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as we…

Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation.

2019

In this paper, we present a novel convolutional neural network architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Since the shape prior is computed offline only once, the execution of our model is not limited by its calculation. Our system takes as input raw magnetic resonance images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. Wit…

Fuzzy technique for microcalcifications clustering in digital mammograms

2012

Abstract Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from …

Baļķu skaita, izmēru un formu noteikšana no fotogrāfijas

2015

Maģistra darba ietvaros tika izpetītas iespējas – kā var veikt veikt analīzi fotogrāfijām ar baļķiem. Lai mērķi sasniegt tika definētas prasības algoritmam, kuras aprakstīs – kadus paramētrus ir nepieciešams nolasīt no fotografijas, kadas ir prasības apparatūrai un kāds ir pieeņemams kļudu limenis. Papildus tika definētas prasības ievaddatiem. Tika aprakstīti iespējami soļi, kuros var sadalīt atpazīšanas algoritmu un detalizēti aprakstīts iespējams risinājums katram solim. Darba ietvaros tika izpetīti pieejamie riķi problēmas risināšanai, aprakstīti rīku priekšrocības un trūkumi. Darba rezultāta tika ieguts teoretisks pamats programmas izveidošanai un izveidots programmas pirmais prototips.

MRI resolution enhancement using total variation regularization

2009

We propose a novel method for resolution enhancement for volumetric images based on a variational-based reconstruction approach. The reconstruction problem is posed using a deconvolution model that seeks to minimize the total variation norm of the image. Additionally, we propose a new edge-preserving operator that emphasizes and even enhances edges during the up-sampling and decimation of the image. The edge enhanced reconstruction is shown to yield significant improvement in resolution, especially preserving important edges containing anatomical information. This method is demonstrated as an enhancement tool for low-resolution, anisotropic, 3D brain MRI images, as well as a pre-processing …

An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders

2008

This work describes an automatic method for discrimination in microphotographs between normal and pathological human megakaryocytes and between two kinds of disorders of these cells. A segmentation procedure has been developed, mainly based on mathematical morphology and wavelet transform, to isolate the cells. The features of each megakaryocyte (e.g. area, perimeter and tortuosity of the cell and its nucleus, and shape complexity via elliptic Fourier transform) are used by a regression tree procedure applied twice: the first time to find the set of normal megakaryocytes and the second to distinguish between the pathologies. The output of our classifier has been compared to the interpretati…

Semantic Analysis of the Driving Environment in Urban Scenarios

2021

Understanding urban scenes require recognizing the semantic constituents of a scene and the complex interactions between them. In this work, we explore and provide effective representations for understanding urban scenes based on in situ perception, which can be helpful for planning and decision-making in various complex urban environments and under a variety of environmental conditions. We first present a taxonomy of deep learning methods in the area of semantic segmentation, the most studied topic in the literature for understanding urban driving scenes. The methods are categorized based on their architectural structure and further elaborated with a discussion of their advantages, possibl…

Compréhension de scènes urbaines basées sur la polarisation

2021

Humans possess an innate ability to interpret scenes under any condition. Computer Vision tends to mimic these capabilities by implementing intelligent algorithms to address complex understanding problems. In this regard, we are interested in understanding outdoor urban scenes in various weather conditions. This thesis specifically addresses the problems arising from the presence of specularity in the scenes. To this end, we aim to take advantage of polarization indices to define such surfaces in addition to traditional objects. In terms of understanding, we aim to introduce polarization to the fields of computer vision and deep learning.This thesis focuses on the following underlying challeng…