Chowhan S.S.¹, G.N. Shinde²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 67-74, 2009, DOI:10.3970/icces.2009.009.067

Abstract Feature selection, often used as a pre-processing step to machine learning, is designed to reduce dimensionality, eliminate irrelevant data and improve accuracy. Iris Basis is our first attempt to reduce the dimensionality of the problem while focusing only on parts of the scene that effectively identify the individual. Independent Component Analysis (ICA) is to extract iris feature to recognize iris pattern. Principal Component Analysis (PCA) is a dimension-reduction tool that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set. Image quality is very important… 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 >

Lingjie Yu^1,2, Guanlin Wang¹, Chao Zhi¹, Bugao Xu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 365-372, 2019, DOI:10.32604/cmes.2019.04494

Abstract A fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional (3D) network with dense microscopic open channels. The geometrical/morphological attributes, such as orientations, curvatures and compactness, of fibers in the network is the key to the filtration performance of the material. However, most of the previous studies were based on materials’ 2D micro-images, which were unable to accurately measure these important 3D features of a filter’s structure. In this paper, we present an imaging method to reconstruct the 3D structure of a fibrous filter from its optical microscopic images. Firstly, a series of images of… More >

Mohammad Behrouzian Nejad¹, Mohammad Ebrahim Shiri Ahmadabadi^{1, 2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.1, pp. 185-205, 2019, DOI:10.32604/cmes.2019.01838

Abstract The main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this… More >

Xingting Liu¹, Jianming Zhang^2,*, Xudong Li², Siwang Zhou¹, Siyuan Zhou², Hye-JinKim³

Journal of Cyber Security, Vol.1, No.1, pp. 29-41, 2019, DOI:10.32604/jcs.2019.06013

Abstract The theory of compressed sensing (CS) has been proposed to reduce the processing time and accelerate the scanning process. In this paper, the image recovery task is considered to outsource to the cloud server for its abundant computing and storage resources. However, the cloud server is untrusted then may pose a considerable amount of concern for potential privacy leakage. How to protect data privacy and simultaneously maintain management of the image remains challenging. Motivated by the above challenge, we propose an image encryption algorithm based on chaotic system, CS and image saliency. In our scheme, we outsource the image CS… More >

Ruihan Zhang^1,2, Junhao Yang¹, Chunxiao Chen^1,*

Molecular & Cellular Biomechanics, Vol.15, No.3, pp. 177-187, 2018, DOI: 10.3970/mcb.2018.04292

Abstract The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on… More >

Jiancheng Zou^1,*, Zhengzheng Li¹, Zhijun Guo¹, Don Hong²

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 163-177, 2019, DOI:10.32604/cmc.2019.05795

Abstract In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array… More >

Xingxing Xiao¹, Yang^1,*, Rui Li², Weiming Zhang³

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 101-115, 2019, DOI:10.32604/cmc.2019.05293

Abstract The medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients’ privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at the lesion area accurately for… 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 >