ICTACT Journal on Image and Video Processing最新文献_第6页

RETRIEVAL OF DEFORMATION FIELDS BY USING STOCHASTIC MUTUAL INFORMATION BASED OPTIMIZATION IN AUTOMATIC REGISTRATION OF SATELLITE IMAGES 卫星图像自动配准中基于随机互信息优化的形变场检索

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2018.0237

Subbiah Manthira Moorth, R. Sivakumar

引用次数: 0

PROGNOSTICATE THE DRUGS FOR MULTIPLE MYELOMA PATIENTS BY USING GENE EXPRESSION TECHNIQUE WITH POLYCLONAL AND MONOCLONAL SAMPLES 应用多克隆和单克隆基因表达技术预测多发性骨髓瘤患者用药

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2017.0235

R. Hemalatha, T. Devi

{"title":"PROGNOSTICATE THE DRUGS FOR MULTIPLE MYELOMA PATIENTS BY USING GENE EXPRESSION TECHNIQUE WITH POLYCLONAL AND MONOCLONAL SAMPLES","authors":"R. Hemalatha, T. Devi","doi":"10.21917/ijivp.2017.0235","DOIUrl":"https://doi.org/10.21917/ijivp.2017.0235","url":null,"abstract":"A major protest in cancer treatment is predicting the clinical response to anti-cancer drugs for each individual patient. For complex diseases such as Myeloma, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the pathological processes at the molecular level. Myeloma is one of the horrible diseases in the world claiming plurality of lives. Accurately predicting drug responses to Myeloma is a most important problem preventing oncologists’ efforts to ensemble the most powerful drugs to treat Myeloma, which is a root goal in precision medicine. It entails the design of therapies that are matched for each individual patient. In this article, it considers a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to the personalized Cancer therapy. There are a total of 44 drug sensitivity prediction algorithms. In that the gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. The proposed algorithm surpassed Bayesian Multitask Multiple Kernel Learning (BMMKL) classification which currently represent the state-of-the-art in drug-response prediction and finally passed the gene expression data to Cytoscape for visualization.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1673-1680"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41351602","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: 1

MODIFIED NLM MODEL FOR DESPECKLING ULTRASOUND IMAGES USING FCM CLUSTERING BASED PRE CLASSIFICATION AND RIBM 基于FCM聚类的预分类和肋的超声图像去斑改进NLM模型

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2018.0240

K. M. Prabusankarlal, P. Thirumoorthy, R. Manavalan, R. Sivaranjani

{"title":"MODIFIED NLM MODEL FOR DESPECKLING ULTRASOUND IMAGES USING FCM CLUSTERING BASED PRE CLASSIFICATION AND RIBM","authors":"K. M. Prabusankarlal, P. Thirumoorthy, R. Manavalan, R. Sivaranjani","doi":"10.21917/ijivp.2018.0240","DOIUrl":"https://doi.org/10.21917/ijivp.2018.0240","url":null,"abstract":"Speckle noise is an inherent characteristic of ultrasound which reduces the classification accuracy of computer aided diagnosis (CAD) systems. A modified non local means (NLM) filter for despeckling ultrasound images is proposed in this article. The proposed NLM model utilizes a preclassification method in which the feature vectors of the input image are constructed using moment invariants and then they are clustered using fuzzy c means (FCM) algorithm. The rotationally invariant block matching (RIBM) algorithm is applied among the blocks within each cluster instead of the entire image. This intra cluster block matching reduces computational complexity of NLM process without the elimination of any pixel candidate. Further, the rotationally invariant moment distance measure improves the noise reduction performance of the algorithm by increasing the chance of getting more similar candidates for NLM process. Extensive experiments are conducted using synthetic images, phantom images and ultrasound images. The method is comparatively evaluated with other denoising methods using statistical parameters such as MSE, PSNR, SSIM, EPI and ENL. The quantitative results suggested that the proposed method outperforms other four state of the art methods in despeckling and preservation of image details.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1708-1715"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43839421","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

A ROBUST OPTIMIZED FEATURE SET BASED AUTOMATIC CLASSIFICATION OF ALZHEIMER’S DISEASE FROM BRAIN MR IMAGES USING K-NN AND ADABOOST 基于稳健优化特征集的K-NN和ADABOOST脑MR图像阿尔茨海默病自动分类

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2017.0234

R. Kamathe, K. Joshi

{"title":"A ROBUST OPTIMIZED FEATURE SET BASED AUTOMATIC CLASSIFICATION OF ALZHEIMER’S DISEASE FROM BRAIN MR IMAGES USING K-NN AND ADABOOST","authors":"R. Kamathe, K. Joshi","doi":"10.21917/ijivp.2017.0234","DOIUrl":"https://doi.org/10.21917/ijivp.2017.0234","url":null,"abstract":"For individuals suffering from some cognitive impairment, treatment plans will be greatly help patients and medical practitioners, if early and accurate detection of Alzheimer’s disease (AD) is carried out. Brain MR Scans of patients’ with health history and supportive medical tests results can lead to distinguish between Healthy/ Normal Controls (NC), Mild Cognitive Impairment (MCI) and AD patients. However manual techniques for disease detection are labour intensive and time consuming. This work is towards the development of Computer Aided Diagnosis (CAD) tool for Alzheimer’s disease detection and its classification into the early stage of AD i.e. MCI and later stage –AD. The paper is about selection of robust optimized feature set using combination of forward selection and/or backward elimination method with K-NN classifier and validation of results with features selected (using forward selection method); with Ada-boost for improved classification accuracy. The features are extracted on Gray Level Cooccurrence Matrix (GLCM). The experimentation is based on Public Brain Magnetic Resonance datasets named Open Access Series of Imaging Studies (OASIS) [7] with patients diagnosed with NC, MCI and AD. The four models considered for automatic classification are – i. Abnormal vs. Normal; ii. AD vs. MCI; iii. MCI vs. NC and iv. AD vs. NC. Feature set optimized using K-NN and validated with AdaBoost has given improved classification accuracy for each model. The output of developed CAD system is compared with Radiologists opinion for test images and has shown 100% match between the output of computer algorithm and experts opinion for some important models under consideration.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1665-1672"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42259951","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: 8

AN INGENIOUS TEXTURE AND SHAPE FEATURE EXTRACTION IN REMOTE SENSING IMAGES BY MEANS OF MULTI KERNEL PRINCIPAL COMPONENT ANALYSIS WITH PYRAMIDAL WAVELET TRANSFORM AND CANNY EDGE DETECTION METHOD 利用锥体小波变换的多核主成分分析和精细边缘检测方法，巧妙地提取了遥感图像的纹理和形状特征

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2017.0236

N. Balakumar, K. Ragul

{"title":"AN INGENIOUS TEXTURE AND SHAPE FEATURE EXTRACTION IN REMOTE SENSING IMAGES BY MEANS OF MULTI KERNEL PRINCIPAL COMPONENT ANALYSIS WITH PYRAMIDAL WAVELET TRANSFORM AND CANNY EDGE DETECTION METHOD","authors":"N. Balakumar, K. Ragul","doi":"10.21917/ijivp.2017.0236","DOIUrl":"https://doi.org/10.21917/ijivp.2017.0236","url":null,"abstract":"In the rapid growth of the digital world, the dealing of remote sensing image is increased day to day in context with the extraction of information. The feature extractions had been an exigent part among the research to classify the remote sensing images for legitimate information reclamation. In such context this paper focus on the extraction of information from remote sensing images by means of classification of spectral classes. Texture and shape is one of the important features in computer vision for many applications. Most of the attention has been focused on texture features with window selection and noise models. This problem can be overcome through Multi Kernel Principal Component analysis with pyramidal wavelet transform and canny edge detection method for extracting feature in high resolute images based on texture and shape. In this paper, proposed Multi Kernel Principal Component analysis utilizes to extract common information and specify common sets of features for further process and reduces dimensionality. Pyramidal wavelet transform is used to extract texture perception for visual interpretation and it decomposes the images into number of descriptors. So texture can be extracted in an image with tree-structured wavelet. Finally, an edge detection technique identifies the boundary regions from the classified remote sensing image, which is taken as shape feature extraction. The performance of this proposed work is measured through peak signal to noise ratio, Execution time, Kappa analysis and structural similarity for a various remote sensing dataset images.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1681-1685"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44885122","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

Segmentation of Bright Region of The Optic Disc for Eye Disease Prediction 视盘亮区分割用于眼病预测

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/IJIVP.2018.0239

Rahul Jadhav, M. Narnaware

{"title":"Segmentation of Bright Region of The Optic Disc for Eye Disease Prediction","authors":"Rahul Jadhav, M. Narnaware","doi":"10.21917/IJIVP.2018.0239","DOIUrl":"https://doi.org/10.21917/IJIVP.2018.0239","url":null,"abstract":"Eye is a vital organ of vision of the human body. Eyes are used almost in every activity whether in reading, affect developmental learning, working and in other untold ways. But, the eye diseases like Cataracts, Macular degeneration, Retinopathy, Glaucoma etc. gradually influence on the eye and leads to blindness. For the early detection of symptoms of eye diseases the ophthalmologist uses the manual observation method. But, that is time consuming and error prone. In this paper, to save the time and reduce the probability of the error, eye disease prediction approach for Glaucoma is developed. For this eye disease prediction approach firstly, the continuous and non-continuous Blood Vessels are segmented using the Coye Filter Approach. Secondly, the bright region of the Optic Disc is segmented using the MRF and Compensation Factor Method. Finally, the channels intensities of the bright region of Optic Disc is compared with the range of channels intensity of the set of bright region of the healthy Retinal images for prediction of the Glaucoma affection. The range for each channel consist of the intensity value starts from minimum to maximum intensities from the set of healthy Retinal images. For this, the Retinal Fundus image is captured by digital Fundus camera with the field of view between 35o to 50o. The Coye Filter Approach, MRF and Compensation Factor Method is applied for the Diaretdb1 and DRIVE which successfully segment the Blood Vessels as well as Optic Disc and also the eye disease prediction approach is applied for the 10 Glaucoma images which correctly predict for the Glaucoma affection.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1696-1707"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47124599","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: 2

THERAPEUTIC MR IMAGE SEGMENTATION BASED ON UPGRADED FUZZY CLUSTERING ALGORITHM WITH BIAS FIELD ESTIMATION 基于偏差场估计的改进模糊聚类算法的磁共振治疗图像分割

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2018.0238

L. Kumar

引用次数: 0

AGE ESTIMATION WITH REGARD FOR CLASSIFIABLE ABILITY OF EACH COMPONENT IN REDUCED DIMENSION AGE MANIFOLD 降维年龄流形中各成分可分类能力的年龄估计

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2018.0241

Pak DuHo, Ri KumHyok, Hyon CunGyong

引用次数: 0

PERFORMANCE ANALYSIS OF IMAGE COMPRESSION BASED ON FAST FRACTIONAL WAVELET TRANSFORM COMBINED WITH SPIHT FOR MEDICAL IMAGES 基于快速分数小波变换结合SPIHT的医学图像压缩性能分析

ICTACT Journal on Image and Video Processing Pub Date : 2018-02-01 DOI: 10.21917/ijivp.2018.0242

K. Ezhilarasan, D. Jayadevappa, S. Mala

引用次数: 3

SCENE TEXT SEGMENTATION BY APPLYING TRIMMED MEDIAN FILTER USING ENERGETIC EDGE 采用带能量边缘的中值滤波对场景文本进行分割

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/IJIVP.2017.0228

T. Bell, Bm K Jeya Kumar

引用次数: 0