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 in the image frames.… 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 obtained from cluster analysis showed… 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 by the intrinsic characteristics of… More >

Caining Zhang¹, Huaguang Li², Xiaoya Guo³, David Molony⁴, Xiaopeng Guo², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Rencan Nie^2,*, Jinde Cao^3,*, Dalin Tang^1,*,7

Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >

Yuxiang Huang¹, Chuliu He¹, Jiaqiu Wang², Yuehong Miao¹, Tongjin Zhu¹, Ping Zhou¹, Zhiyong Li^1,2,*

Molecular & Cellular Biomechanics, Vol.15, No.2, pp. 117-125, 2018, DOI: 10.3970/mcb.2018.02478

Abstract Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and… More >

R. V. Uddanwadiker^*

Molecular & Cellular Biomechanics, Vol.10, No.4, pp. 289-302, 2013, DOI:10.3970/mcb.2013.010.289

Abstract The aim of the study is to find most optimum combination of crown material and adhesive to avoid loosening and thereby failure of restored tooth. This study describes the Thermo-Mechanical analysis of restored molar tooth crown for determination of the stress levels due to thermal and mechanical loads on restored molar tooth. The potential use of the 3-D model was demonstrated and analyzed using different materials for crown. Thermal strain, stress and deformation were measured at hot and cold conditions in ANSYS and correlated with analytical calculation and existing experimental data for model validation and optimization. It is concluded that… More >

Hao Wu¹, Qi Liu^{2, 3, *}, Xiaodong Liu⁴

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 575-597, 2019, DOI:10.32604/cmc.2019.03595

Abstract Deep learning technology has brought great impetus to artificial intelligence, especially in the fields of image processing, pattern and object recognition in recent years. Present proposed artificial neural networks and optimization skills have effectively achieved large-scale deep learnt neural networks showing better performance with deeper depth and wider width of networks. With the efforts in the present deep learning approaches, factors, e.g., network structures, training methods and training data sets are playing critical roles in improving the performance of networks. In this paper, deep learning models in recent years are summarized and compared with detailed discussion of several typical networks… More >

Mamouni El Mamoun^1,*, Zennaki Mahmoud¹, Sadouni Kaddour¹

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 55-66, 2019, DOI:10.32604/cmc.2019.06444

Abstract The paper discusses the segmentation of words into characters, which is an essential task in the development process of character recognition systems, as poorly segmented characters will automatically be unrecognized. The segmentation of offline handwritten Arabic text poses a greater challenge because of its cursive nature and different writing styles. In this article, we propose a new approach to segment handwritten Arabic characters using an efficient analysis of the vertical projection histogram. Our approach was tested using a set of handwritten Arabic words from the IFN/ENIT database, and promising results were obtained. More >

Sultan Noman Qasem^1,2, Amar Nazar³, Attia Qamar⁴, Shahaboddin Shamshirband^5,6,*, Ahmad Karim⁴

CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 713-727, 2019, DOI:10.32604/cmc.2019.05617

Abstract Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition. In this paper, we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor. We considered watershed segmentation technique for brain tumor detection. The proposed methodology is divided as follows: pre-processing, computing foreground applying watershed, extract and supply features to machine learning algorithms. Consequently, this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain tumor. More >

Peng Fu^1,2, Qianqian Xu¹, Jieyu Zhang³, Leilei Geng^4,*

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 509-515, 2019, DOI:10.32604/cmc.2019.05250

Abstract The superpixel segmentation has been widely applied in many computer vision and image process applications. In recent years, amount of superpixel segmentation algorithms have been proposed. However, most of the current algorithms are designed for natural images with little noise corrupted. In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise, we propose a noise-resistant superpixel segmentation (NRSS) algorithm in this paper. In the proposed NRSS, the spectral signatures are first transformed into frequency domain to enhance the noise robustness; then the two widely spectral similarity measures-spectral angle mapper (SAM) and spectral information… More >