Int. J. Rough Sets Data Anal.最新文献_第8页

Improving Efficiency of K-Means Algorithm for Large Datasets 提高大数据集K-Means算法的效率

Int. J. Rough Sets Data Anal. Pub Date : 2016-04-01 DOI: 10.4018/IJRSDA.2016040101

C. Swapna, V. Kumar, J. Murthy

引用次数: 18

Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition 应用扩展神经网络分析步态流图像和步态高斯图像的步态识别

Int. J. Rough Sets Data Anal. Pub Date : 2016-04-01 DOI: 10.4018/IJRSDA.2016040104

Parul Arora, S. Srivastava, Shivank Singhal

引用次数: 15

EEG Analysis of Imagined Speech 想象语音的脑电图分析

Int. J. Rough Sets Data Anal. Pub Date : 2016-04-01 DOI: 10.4018/IJRSDA.2016040103

Sadaf Iqbal, Muhammed Shanir P.P., Y. Khan, O. Farooq

引用次数: 10

Hybrid Data Mining Approach for Image Segmentation Based Classification 基于图像分割分类的混合数据挖掘方法

Int. J. Rough Sets Data Anal. Pub Date : 2016-04-01 DOI: 10.4018/IJRSDA.2016040105

M. Panda, A. Hassanien, A. Abraham

引用次数: 38

Multimodality Medical Image Fusion using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy 基于m波段小波和复小波变换的多模医学图像融合

Int. J. Rough Sets Data Anal. Pub Date : 2015-07-01 DOI: 10.4018/IJRSDA.2015070101

S. Chavan, S. Talbar

引用次数: 14

Identification of Chronic Wound Status under Tele-Wound Network through Smartphone 智能手机远程创伤网络下慢性创伤状态的识别

Int. J. Rough Sets Data Anal. Pub Date : 2015-07-01 DOI: 10.4018/IJRSDA.2015070104

Chinmay Chakraborty, B. Gupta, S. Ghosh

引用次数: 28

GPU Based Modified HYPR Technique: A Promising Method for Low Dose Imaging 基于GPU的改进HYPR技术:一种很有前途的低剂量成像方法

Int. J. Rough Sets Data Anal. Pub Date : 2015-07-01 DOI: 10.4018/IJRSDA.2015070103

S. Desai, L. Kulkarni

{"title":"GPU Based Modified HYPR Technique: A Promising Method for Low Dose Imaging","authors":"S. Desai, L. Kulkarni","doi":"10.4018/IJRSDA.2015070103","DOIUrl":"https://doi.org/10.4018/IJRSDA.2015070103","url":null,"abstract":"Medical imaging has grown tremendously over the decades. The computed tomography CT and Magnetic resonance imaging MRI are considered to be most widely used imaging modalities. MRI is less harmful, but one cannot underestimate the harmful side effects of CT. A recent study reveals the fact of increasing risk of cancer as a side effect for patients undergoing repeated CT scans. Hence the design of the low dose imaging protocol is about the immense importance in the current scenario. In this paper, the authors present modified highly constrained back projection M-HYPR as a most promising technique to address low dose imaging. Highly constrained back projection HYPR being iterative in nature is computational savvy, and is one of the main reasons for being neglected by CT developers. The weight matrix module, being root cause for huge computation time is modified in this work. Considerable speed up factor is recorded, as compared original HYPR O-HYPR on a single thread CPU implementation. The quality of the reconstructed image in each platform has been analyzed. Recorded results upholds M-HYPR algorithm, and appreciates usage of graphical processing units GPU in medical imaging applications.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128011807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection 基于改进距离正则化水平集分割的肾结石检测分析

Int. J. Rough Sets Data Anal. Pub Date : 2015-07-01 DOI: 10.4018/ijrsda.2015070102

K. Viswanath, R. Gunasundari

{"title":"Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection","authors":"K. Viswanath, R. Gunasundari","doi":"10.4018/ijrsda.2015070102","DOIUrl":"https://doi.org/10.4018/ijrsda.2015070102","url":null,"abstract":"The abnormalities of the kidney can be identified by ultrasound imaging. The kidney may have structural abnormalities like kidney swelling, change in its position and appearance. Kidney abnormality may also arise due to the formation of stones, cysts, cancerous cells, congenital anomalies, blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using distance regularized level set segmentation DR-LSS, since it yields better results. It uses a two-step splitting methods to iteratively solve the DR-LSS equation, first step is iterating LSS equation, and then solving the Sign distance equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The DR is included for LSS for eliminating of anti-leakages on image boundary. The DR-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution DRLSE1, DRLSE2 and DRLSE3. Extracted region of the kidney after segmentation is applied to Symlets Sym12, Biorthogonal bio3.7, bio3.9 & bio4.4 and Daubechies Db12 lifting scheme wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron MLP and Back Propagation BP ANN to identify the type of stone with an accuracy of 98.6%.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117109518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Medical Image Fusion in Wavelet and Ridgelet Domains: A Comparative Evaluation 小波和脊波域医学图像融合的比较评价

Int. J. Rough Sets Data Anal. Pub Date : 2015-07-01 DOI: 10.4018/IJRSDA.2015070105

V. Bhateja, Abhinav Krishn, Himanshi Patel, Akanksha Sahu

{"title":"Medical Image Fusion in Wavelet and Ridgelet Domains: A Comparative Evaluation","authors":"V. Bhateja, Abhinav Krishn, Himanshi Patel, Akanksha Sahu","doi":"10.4018/IJRSDA.2015070105","DOIUrl":"https://doi.org/10.4018/IJRSDA.2015070105","url":null,"abstract":"Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature i.e. the information content or the structural properties of an image. Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis PCA based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from 'The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy E, Fusion Factor FF, Structural Similarity Index SSIM and Edge Strength QFAB. The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134445364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive Survey on Metal Artifact Reduction Methods in Computed Tomography Images 计算机断层扫描图像中金属伪影还原方法综述

Int. J. Rough Sets Data Anal. Pub Date : 2015-07-01 DOI: 10.4018/IJRSDA.2015070106

S. Desai, L. Kulkarni

引用次数: 5