2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)最新文献

MR image reconstruction based on compressed sensing using Poisson sampling pattern 基于压缩感知的泊松采样模式磁共振图像重建

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802884

Amruta Kaldate, B. Patre, R. Harsh, Dharmesh Verma

{"title":"MR image reconstruction based on compressed sensing using Poisson sampling pattern","authors":"Amruta Kaldate, B. Patre, R. Harsh, Dharmesh Verma","doi":"10.1109/CCIP.2016.7802884","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802884","url":null,"abstract":"Magnetic Resonance Imaging is a medical imaging modality used to produce good quality images of soft tissue in ligaments and other internal body organs. MRI is non-invasive scanning technique based on the principle of Nuclear Magnetic Resonance. The MRI scan time depends on the size of the scanned area and the number of images being reconstructed. This scan time reduction may reduce the artifacts in the reconstruction by improving the patient comfort. Compressed sensing (CS) theory helps MRI to reduce the scan time by reconstructing MR images with fewer sampled measurements. Application of CS to MRI gives acceleration in MR image acquisition. This paper focuses on randomly under sampled k-space data and use of CS-MR image reconstruction. This work compares variable density mask and Poisson mask and show their usefulness in Compressed Sensing applied to MRI image reconstruction. Image reconstruction using Nonlinear conjugate gradient method has been performed on the cardiac dataset at different acceleration factors. Further in the paper, reconstructed images are quantified by Peak Signal To Noise Ratio (PSNR).","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121828957","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: 5

Segmentation of coconut crop bunch from tree images 从树木图像中分割椰子作物束

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802865

S. Siddesha, S. Niranjan, V. N. Manjunath Aradhya

引用次数: 3

Performance evaluation of image retrieval systems using shape feature based on wavelet transform 基于小波变换的形状特征图像检索系统性能评价

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802876

P. Desai, J. Pujari, Anita Kinnikar

{"title":"Performance evaluation of image retrieval systems using shape feature based on wavelet transform","authors":"P. Desai, J. Pujari, Anita Kinnikar","doi":"10.1109/CCIP.2016.7802876","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802876","url":null,"abstract":"Digital era has produced large volume of images which created many challenges in computer science field to store, retrieve and manage images efficiently and effectively. Many techniques and algorithms have been proposed by different researcher to implement Content Based Image Retrieval (CBIR) systems. This paper discusses performance of different CBIR systems implemented using combined features colour, texture and shape as a prominent feature based on wavelet transform. Choice of the feature extraction technique used in image retrieval determines performance of CBIR systems. In this paper evaluation of performance of three CBIR systems based on wavelet decomposition using threshold, wavelet decomposition using morphology operators and wavelet decomposition using Local Binary Patterns (LBP) is done. Also the performance of these methods is compared with the existing methods SIMPLIcity and FIRM. Average precision is used to compare the performance of the implemented systems. Results indicate that performance of CBIR systems using wavelet decomposition give better results than simplicity and FIRM, also wavelet decomposition with Local Binary Patterns (LBP) exhibit better retrieval efficiency compared to wavelet decomposition using threshold and morphological operators. Theses CBIR systems have been tested on bench mark Wang's image database. Precision versus Recall graphs for each system shows the performance of respective systems.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125527069","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: 10

Recognizing cardiovascular risk from photoplethysmogram signals using ELM 利用ELM从光容积图信号识别心血管风险

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802864

Shobitha S, Sandhya R, Niranjana Krupa, M. Alauddin, M. Ali

引用次数: 6

Sequential ELM for financial markets 金融市场的连续ELM

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802879

Ashwin S. Ravi, Akshay Sarvesh, K. George

引用次数: 2

An enhanced effect-size thresholding method for the diagnosis of Autism Spectrum Disorder using resting state functional MRI 静息状态功能MRI诊断自闭症谱系障碍的增强型效应大小阈值方法

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802874

B. S. Mahanand, S. Vigneshwaran, S. Suresh, N. Sundararajan

{"title":"An enhanced effect-size thresholding method for the diagnosis of Autism Spectrum Disorder using resting state functional MRI","authors":"B. S. Mahanand, S. Vigneshwaran, S. Suresh, N. Sundararajan","doi":"10.1109/CCIP.2016.7802874","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802874","url":null,"abstract":"Autism Spectrum Disorders (ASD) represent a cluster of relatively common developmental conditions which require an early and accurate diagnosis for an effective remedial therapy. Resting state functional MRI (rs-fMRI) is considered an important tool to investigate the differences in functional connectivity due to ASD. In this paper, an Enhanced Effect-Size Thresholding (EEST) method is developed for extracting connectivity based features to diagnose ASD automatically from rs-fMRI. In this method, a whitening step is first used to decorrelate the Blood Oxygen Level Dependent (BOLD) signals (time-series) from the 90 representative regions of the brain based on the Automated Anatomical Labeling (AAL) template. Using these whitened time-series signals, the group-wise (ASD versus Neurotypical) differences in pairwise-connectivity are compared based on their effect-size. The connections corresponding to larger values of effect-size are alone considered for feature extraction. The z-transformed correlation co-efficients are used as features and the classification is performed using a support vector machine. The publicly available Autism Brain Imaging data Exchange (ABIDE) dataset is used to evaluate the performance of EEST and it is found that EEST can achieve better classification performance when compared to the earlier method.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125712975","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: 5

Early detection of grapes diseases using machine learning and IoT 利用机器学习和物联网对葡萄病害进行早期检测

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802887

S. S. Patil, S. Thorat

引用次数: 92

A foreground marker based centroid initialized Geodesic active contours for histopathological image segmentation 一种基于前景标记的质心初始化组织病理图像的测地线活动轮廓

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802859

P. Shivamurthy, T. N. Nagabhushan, V. Basavaraj

{"title":"A foreground marker based centroid initialized Geodesic active contours for histopathological image segmentation","authors":"P. Shivamurthy, T. N. Nagabhushan, V. Basavaraj","doi":"10.1109/CCIP.2016.7802859","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802859","url":null,"abstract":"Nuclear segmentation is considered to be one of the major challenge in the field of Histopathological Imaging. Various segmentation approaches have been proposed in the literature. The quality of the histopathological images have posed various challenges to those proposed techniques and they all suffer with deficiencies due to poor edge information and irregularities of the boundary. Active contours are considered to be the promising solutions to such a challenging task. The major issues with Active contours are computation of gradient information, initialization and occlusion detection. To address these issues effectively, an edge gradient driven Geodesic active contour(GAC) with a novel approach of detecting seed points based on foreground markers is proposed in this paper. The experimentation is performed on breast cancer tissue images and the efficiency measures such object detection accuracy and overlap resolution have been computed and compared with that of GAC without foreground markers as referred to the ground truth opined by the pathologists from Department of Pathology, JSS Hospital.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283845","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: 3

Study of Complex-valued Learning algorithms for Post-surgery survival prediction 术后生存预测的复值学习算法研究

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802856

Sivachitra Muthusamy, Savitha Ramasamy

{"title":"Study of Complex-valued Learning algorithms for Post-surgery survival prediction","authors":"Sivachitra Muthusamy, Savitha Ramasamy","doi":"10.1109/CCIP.2016.7802856","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802856","url":null,"abstract":"Prediction of post-surgery survival of breast cancer patients is critical for long term medical care. In this paper, we study the performances of several complex-valued classifiers in predicting the post-surgical survival, based on the real world Haber data set available in the UCI machine learning repository. The complex-valued classifiers used in the study include the Fully Complex-valued Radial Basis Function (FC-RBF), Fully Complex-valued Relaxation Network (FCRN), Meta-cognitive Fully Complex-valued Relaxation Network (McFCRN), Fully Complex-valued Fast Learning Classifier (FC-FLC), Meta-cognitive Fully Complex-valued Fast Learning Classifier (Mc-FCFLC), Fully Complex-valued Functional Link Network (FCFLN), and Meta-cognitive Fully Complex-valued Functional Link Network (Mc-FCFLN). As the classification performance of the complex-valued classifiers is boosted by the presence of orthogonal decision boundaries, all these classifiers perform better than the state-of-the-art real-valued classifiers. Performance results also show that the Mc-FCFLC and McFCRN outperform other classifiers used in the study. This can be attributed to the meta-cognition that helps in strategic learning in these classifiers.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115004014","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

Feature extraction and classification of retinal images for automated detection of Diabetic Retinopathy 用于糖尿病视网膜病变自动检测的视网膜图像特征提取与分类

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802862

R. Harini, N. Sheela

引用次数: 29