ICTACT Journal on Image and Video Processing最新文献_第10页

NEW HUMAN SEMEN ANALYSIS SYSTEM (CASA) USING MICROSCOPIC IMAGE PROCESSING TECHNIQUES 使用显微图像处理技术的新型人类精液分析系统(casa)

ICTACT Journal on Image and Video Processing Pub Date : 2016-11-01 DOI: 10.21917/IJIVP.2016.0201

M. ChaudhariN, B. Pawar

{"title":"NEW HUMAN SEMEN ANALYSIS SYSTEM (CASA) USING MICROSCOPIC IMAGE PROCESSING TECHNIQUES","authors":"M. ChaudhariN, B. Pawar","doi":"10.21917/IJIVP.2016.0201","DOIUrl":"https://doi.org/10.21917/IJIVP.2016.0201","url":null,"abstract":"Computer assisted semen analysis (CASA) helps the pathologist or fertility specialist to evaluate the human semen. Detail analysis of spermatozoa like morphology and motility is very important in the process of intrauterine insemination (IUI) or In-vitro fertilization (IVF) in infertile couple. The main objective for this new semen analysis is to provide a low cost solution to the pathologist and gynecologist for the routine raw semen analysis, finding the concentration of the semen with dynamic background removal and classify the spermatozoa type (grade) according to the motility and structural abnormality as per the WHO criteria. In this paper a new system , computer assisted semen analysis system is proposed in which hybrid approach is used to identify the moving object, scan line algorithm is applied for confirmation of the objects having tails, so that we can count the actual number of spermatozoa. For removal of background initially the dynamic background generation algorithm is proposed to create a background for background subtraction stage. The standard data set is created with 40× and 100× magnification from the different raw semen s. For testing the efficiency of proposed algorithm, same frames are applied to the existing algorithm. Another module of the system is focused on finding the motility and Type classification of individual spermatozoa.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"7 1","pages":"1381-1391"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68388727","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

SINGLE FRAME SUPER RESOLUTION OF NONCOOPERATIVE IRIS IMAGES 非合作虹膜图像的单帧超分辨率

ICTACT Journal on Image and Video Processing Pub Date : 2016-11-01 DOI: 10.21917/IJIVP.2016.0198

A. Deshpande, P. Patavardhan

引用次数: 4

AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS 利用gabor特征和深度神经网络自动检测乳腺癌组织病理学图像中的有丝分裂图像

ICTACT Journal on Image and Video Processing Pub Date : 2016-11-01 DOI: 10.21917/IJIVP.2016.0200

Maqlin Paramanandam, Robinson Thamburaj, J. Mammen

{"title":"AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS","authors":"Maqlin Paramanandam, Robinson Thamburaj, J. Mammen","doi":"10.21917/IJIVP.2016.0200","DOIUrl":"https://doi.org/10.21917/IJIVP.2016.0200","url":null,"abstract":"The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief NetworkDeep Neural Network architecture (DBN-DNN). The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSISATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs) from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenges.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"7 1","pages":"1366-1372"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68388718","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

ANALYSIS OF ABNORMALITIES IN COMMON CAROTID ARTERY IMAGES USING MULTIWAVELETS 应用多小波分析颈总动脉图像异常

ICTACT Journal on Image and Video Processing Pub Date : 2016-11-01 DOI: 10.21917/IJIVP.2016.0195

R. Nandakumar, B. JayanthiK.

引用次数: 0

CHARACTER RECOGNITION OF VIDEO SUBTITLES 视频字幕的字符识别

ICTACT Journal on Image and Video Processing Pub Date : 2016-11-01 DOI: 10.21917/IJIVP.2016.0196

Satish S Hiremath, K. Suresh

引用次数: 2

A SURVEY OF RETINA BASED DISEASE IDENTIFICATION USING BLOOD VESSEL SEGMENTATION 基于视网膜的血管分割疾病识别综述

ICTACT Journal on Image and Video Processing Pub Date : 2016-11-01 DOI: 10.21917/IJIVP.2016.0197

P. Kuppusamy, B. Divya

引用次数: 3

REAL-TIME OBJECT DETECTION IN PARALLEL THROUGH ATOMIC TRANSACTIONS 通过原子事务并行进行实时对象检测

ICTACT Journal on Image and Video Processing Pub Date : 2016-11-01 DOI: 10.21917/IJIVP.2016.0199

Kavinayan Sivakumar, P. Shanmugapriya

引用次数: 0

AN EFFICIENT SELF-UPDATING FACE RECOGNITION SYSTEM FOR PLASTIC SURGERY FACE 一种高效的整形面部自更新识别系统

ICTACT Journal on Image and Video Processing Pub Date : 2016-08-01 DOI: 10.21917/ijivp.2016.0191

A. Devi, A. Marimuthu

{"title":"AN EFFICIENT SELF-UPDATING FACE RECOGNITION SYSTEM FOR PLASTIC SURGERY FACE","authors":"A. Devi, A. Marimuthu","doi":"10.21917/ijivp.2016.0191","DOIUrl":"https://doi.org/10.21917/ijivp.2016.0191","url":null,"abstract":"Facial recognition system is fundamental a computer application for the automatic identification of a person through a digitized image or a video source. The major cause for the overall poor performance is related to the transformations in appearance of the user based on the aspects akin to ageing, beard growth, sun-tan etc. In order to overcome the above drawback, Self-update process has been developed in which, the system learns the biometric attributes of the user every time the user interacts with the system and the information gets updated automatically. The procedures of Plastic surgery yield a skilled and endurable means of enhancing the facial appearance by means of correcting the anomalies in the feature and then treating the facial skin with the aim of getting a youthful look. When plastic surgery is performed on an individual, the features of the face undergo reconstruction either locally or globally. But, the changes which are introduced new by plastic surgery remain hard to get modeled by the available face recognition systems and they deteriorate the performances of the face recognition algorithm. Hence the Facial plastic surgery produces changes in the facial features to larger extent and thereby creates a significant challenge to the face recognition system. This work introduces a fresh Multimodal Biometric approach making use of novel approaches to boost the rate of recognition and security. The proposed method consists of various processes like Face segmentation using Active Appearance Model (AAM), Face Normalization using Kernel Density Estimate/Point Distribution Model (KDE-PDM), Feature extraction using Local Gabor XOR Patterns (LGXP) and Classification using Independent Component Analysis (ICA). Efficient techniques have been used in each phase of the FRAS in order to obtain improved results.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"7 1","pages":"1307-1317"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68388232","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: 4

A Comprehensive Study on Text Information Extraction from Natural Scene Images 自然场景图像文本信息提取的综合研究

ICTACT Journal on Image and Video Processing Pub Date : 2016-08-01 DOI: 10.21917/ijivp.2016.0188

Anit V. Manjaly, S. B

引用次数: 0

EVALUATION OF IMMUNOHISTOCHEMISTRY (IHC) MARKER HER2 IN BREAST CANCER 免疫组化(ihc)标志物her2在乳腺癌中的评价

ICTACT Journal on Image and Video Processing Pub Date : 2016-08-01 DOI: 10.21917/ijivp.2016.0192

Prasanna G. Shete, Gajanan K. Kharate

{"title":"EVALUATION OF IMMUNOHISTOCHEMISTRY (IHC) MARKER HER2 IN BREAST CANCER","authors":"Prasanna G. Shete, Gajanan K. Kharate","doi":"10.21917/ijivp.2016.0192","DOIUrl":"https://doi.org/10.21917/ijivp.2016.0192","url":null,"abstract":"The paper discusses a novel approach involving algorithm implementation and hardware Devkit processing for estimating the extent of cancer in a breast tissue sample. The process aims at providing a reliable, repeatable, and fast method that could replace the traditional method of manual examination and estimation. Immunohistochemistry (IHC) and Fluorescence in situ Hybridization (FISH) are the two main methods used to detect the marker status in clinical practice. FISH is though more reliable than IHC, but IHC is widely used as it is cheaper, convenient to operate and conserve, the morphology is clear. The IHC markers are Estrogen receptor (ER, Progesterone receptor (PR), Human Epidermal Growth Factor (HER2) that give clear indications of the presence of cancer cells in the tissue sample. HER2 remains the most reliable marker for the detection of breast cancer. The Human Epidermal Growth Factor Receptor (HER2) markers are discussed in the paper, as it gives clear indications of the presence of cancer cells in the tissue sample. HER2 is identified based on the color and intensity of the cell membrane staining. The color and intensity is obviously based on the thresholding for classifying the cancerous cells into severity levels in terms of score to estimate the extent of spread of cancer in breast tissue. For HER2 evaluation, the percentage of staining is calculated in terms of ratio of stain pixel count to the total pixel count. The evaluation of HER2 is obtained through simulation software (MATLAB) using intensity based algorithm and same is run on embedded processor evaluation board Devkit 8500. The results are validated with doctors.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"7 1","pages":"1318-1323"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68388407","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