Foued Derraz¹, Laurent Peyrodie², Antonio PINTI³, Abdelmalik Taleb-Ahmed³, Azzeddine Chikh⁴, Patrick Hautecoeur⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.67, No.2, pp. 95-118, 2010, DOI:10.3970/cmes.2010.067.095

Abstract We propose a new semi-automatic segmentation based Active Contour Model and statistic prior knowledge of Multiple Sclerosis (MS) Lesions in Regions Of Interest (RIO) within brain Magnetic Resonance Images(MRI). Reliable segmentation of MS lesion is important for at least three types of practical applications: pharmaceutical trails, making decision for drug treatment, patient follow-up. Manual segmentation of the MS lesions in brain MRI by well qualified experts is usually preferred. However, manual segmentation is hard to reproduce and can be highly cost and time consuming in the presence of large volume of MRI data. In other… More >

Maria João M. Vasconcelos¹, João Manuel R. S. Tavares¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.10, No.1, pp. 3-4, 2009, DOI:10.3970/icces.2009.010.003

Abstract In the last years, researchers from the Computational Vision working field have been developing new methods to perform image segmentation for human motion analysis. The development of computational techniques suitable to automatically identify the structures involved is necessary to obtain more representative and robust features to be further used in the analysis of human motion from image sequences.
The first step of human motion analysis from image sequences is strongly related with image segmentation. In fact, the first goal of any system designed for this aim is the identification of the structures’ features to be analysed… More >

Patrícia C.T. Gonçalves^1,2, João Manuel R.S. Tavares¹, R.M. Natal Jorge¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.3, pp. 203-204, 2009, DOI:10.3970/icces.2009.009.203

Abstract The main goals of the present work are to automatically extract the contour of an object and to simulate its deformation using a physical approach. In this work, to segment an object represented in an image, an initial contour is manually defined for it that will then automatically evolve until it reaches the border of the desired object. In this approach, the contour is modelled by a physical formulation using the finite element method, and its temporal evolution to the desired final contour is driven by internal and external forces. The internal forces are defined… More >

Stephen R. Niezgoda¹, Surya R. Kalidindi^1,2

CMC-Computers, Materials & Continua, Vol.14, No.2, pp. 79-98, 2009, DOI:10.3970/cmc.2009.014.079

Abstract The generalized Hough transform is a common technique for feature detection in image processing. In this paper, we develop a size invariant Hough framework for the detection of arbitrary shapes in three dimensional digital microstructure datasets. The Hough transform is efficiently implemented via kernel convolution with complex Hough filters, where shape is captured in the magnitude of the filter and scale in the complex phase. In this paper, we further generalize the concept of a Hough filter by encoding other parameters of interest (e.g. orientation of plate or fiber constituents) in the complex phase, broadening More >

Maria João M. Vasconcelos¹, João Manuel R. S. Tavares¹

CMES-Computer Modeling in Engineering & Sciences, Vol.36, No.3, pp. 213-242, 2008, DOI:10.3970/cmes.2008.036.213

Abstract In this work we developed methods to automatically extract significant points of objects like hand palms and faces represented in images that can be used to build Point Distribution Models automatically. These models are further used to segment the modelled objects in new images, through the use of Active Shape Models or Active Appearance Models. These models showed to be efficient in the segmentation of objects, but had as drawback the fact that the labelling of the landmark points was usually manually made and consequently time consuming. Thus, in this paper we describe some methods More >

Patrícia C.T. Gonçalves^1,2, João Manuel R.S. Tavares^1,2, R.M. Natal Jorge^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.32, No.1, pp. 45-56, 2008, DOI:10.3970/cmes.2008.032.045

Abstract The main goals of the present work are to automatically extract the contour of an object and to simulate its deformation using a physical approach. In this work, to segment an object represented in an image, an initial contour is manually defined for it that will then automatically evolve until it reaches the border of the desired object. In this approach, the contour is modelled by a physical formulation using the finite element method, and its temporal evolution to the desired final contour is driven by internal and external forces. The internal forces are defined… More >

Xueying Huang¹, Chun Yang², Jie Zheng³, Pamela K. Woodard³, Dalin Tang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.1, No.1, pp. 29-34, 2007, DOI:10.3970/icces.2007.001.029

Abstract Atherosclerotic plaques may rupture without warning and cause acute cardiovascular syndromes such as heart attack and stroke. Accurate identification of plaque components will improve the accuracy and reliability of computational models. In this article, we present a segmentation method using a cluster analysis technique to quantify and classify plaque components from magnetic resonance images (MRI). 3D in vivo and ex vivo multi-contrast (T1-, proton density-, and T2-weighted) MR Images were acquired from a patient of cardiovascular disease. Normal distribution Bayes classifier was performed on ex vivo and in vivo MR Images respectively. The resulting segmentation More >