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Automatic fuzzy-neural based segmentation of microscopic cell images

Sara Colantonio, Igor B. Gurevich, Ovidio Salvetti
2008 International Journal of Signal and Imaging Systems Engineering  
In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images.  ...  The proposed method follows a two-step approach to, firstly, find the nuclei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus.  ...  Acknowledgment This work was partially supported by INTAS Grant N 04-77-7067, by the Cooperative grant "Image Analysis and Synthesis: Theoretical Foundations and Prototypical Applications in Medial Imaging  ... 
doi:10.1504/ijsise.2008.017769 fatcat:55e6cknizbh2lc6od5lflbgmbi

Automatic Fuzzy-neural Based Segmentation of Microscopic Cell Images [chapter]

Sara Colantonio, Igor Gurevich, Ovidio Salvetti
Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry  
In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images.  ...  The proposed method follows a two-step approach to, firstly, find the nuclei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus.  ...  Acknowledgment This work was partially supported by INTAS Grant N 04-77-7067, by the Cooperative grant "Image Analysis and Synthesis: Theoretical Foundations and Prototypical Applications in Medial Imaging  ... 
doi:10.1007/978-3-540-76300-0_12 dblp:conf/massdata/ColantonioGS07 fatcat:4jewursev5d4zoggjoys5ym3gy

Spiral Bacterial Cell Image Analysis using Active Contour Method

P.S. Hiremath, Parashuram Bannigidad
2012 International Journal of Computer Applications  
The objective of the present study is to develop an automatic tool to identify and classify the different types of spiral bacterial cells in digital microscopic cell images using active contour method.  ...  , namely, 3 classifier, K-NN classifier, Neural Network classifier and Neuro Fuzzy classifiers.  ...  The process of semi-automatic image analysis of cells to evaluate these aspects of microbial communities are; image acquisition, digitization, and segmentation; automatic measurement to extract features  ... 
doi:10.5120/4626-6650 fatcat:yti4kyeawjbkznkkki5oy5nrgq

Automatic Segmentation of Leukocytes for the Detection of Leukemia Using a New Computing Algorithm

Aldrin Karunaharan Kanakaraj, Om Prakash
2018 International Journal of Advancements in Technology  
This paper explores the techniques used in the automatic segmentation of leukocytes using a new computing technique. Volume 9 • Issue 2 • 1000204 Int J Adv Technol, an open access journal 3.  ...  Restoration of original image by setting the proper size of window and recover the original image.  ...  Segmentation Based on Fuzzy Cellular Neural Networks (FCNN) In this paper, a successful attempt has been made to combine the advantages of threshold segmentation based on mathematical morphology as well  ... 
doi:10.4172/0976-4860.1000204 fatcat:yuye6l2ilregxpib4fc36fzzsa

Design of a Hybrid Intelligent System for Transitional Bladder Cell Carcinoma Diagnosis

Nada Saleem, Khalid Saleem
2009 ˜Al-œRafidain journal for computer sciences and mathematics  
The proposed system is composed of two main phases, the first phase is the "cell analysis phase" which consists of three main stages including segmentation stage using "Genetic Optimization Based Fuzzy  ...  Image Segmentation Algorithm" (GOFISA), morphometric and photometric feature extraction stage and "Neuro-Fuzzy Classifier Model" (NFCM) stage that has been developed and implemented to classify a set  ...  GOFISA Image Segmentation Method The delineation of the cell image into a cell nucleus and background is an essential component of an automatic computerized cell image analysis system in the proposed method  ... 
doi:10.33899/csmj.2009.163763 fatcat:6idjlwx6kvbpllmp4y6nt7hpny

Automated Leukemia Detection with Image Processing and Machine Learning: A Review

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The use of image processing techniques, including microscopic color imaging, segmentation, feature extraction, classification, and clustering allows identification of leukemia in patients.  ...  Manual inspection of microscopic images is time consuming, with a questionable accuracy at times.  ...  ANN based Neural-oriented approaches segment the image on the basis of pixel data obtained from a convolution window or from information on local features provided to the neural classifier.  ... 
doi:10.30534/ijatcse/2020/161952020 fatcat:lh3qx33chrgobdzduj5acfaacu

Automatic Detection of Proliferative Cells in Immunohistochemically Images of Meningioma Using Fuzzy C-Means Clustering and HSV Color Space

Vahid Anari, Mina Bakhshi
2019 Zenodo  
In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal)  ...  Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically.  ...  Cells nuclei segmentation is the key issue in automatic cell images analysis. Pixel based cell segmentation method combining the shape information of the cell nuclei was proposed in [7] .  ... 
doi:10.5281/zenodo.3566416 fatcat:wth7sr4bcbca3cu6sdzo32a2my

Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

2019 International Journal of Engineering and Advanced Technology  
algorithm with FOA that are capable to segment the accurate blood cell region from microscopic images.  ...  On the way to achieve this goal, we proposed ALL-DC model that combines recent developments in deep learning with fuzzy based CNN structure and for ROBC segmentation, hybridization of K-means segmentation  ...  = bwboundaries (MaskImg) 23) Segmented Cell Region = Boundaries 24) For i1: P 25) ROI = M-Image X Segmented Cell Region 26) End 27) Return: ROI of Microscopic Image as Cell ROI Microscopic Image Figure  ... 
doi:10.35940/ijeat.f9001.088619 fatcat:jff4v7itlfbfzechioa7j3enci

Analysis of White Blood Cell Segmentation Techniques and Classification Using Deep Convolutional Neural Network for Leukemia Detection

Snehal Laddha
2018 Helix  
Then cells are detected and boundaries are traced using segmentation techniques for morphological analysis. Typical image processing steps for WBC segmentation are as shown in figure 2.  ...  In this study, we have performed comparative analysis of white blood cells segmentation techniques and evaluated the performance of pretrained deep CNN with multiclass models for Support Vector Machine  ...  Acknowledgement: We extend our sincere thanks to Fabio Scotti, Department of Information Technology, and University of Milan, Italy for making dataset ALL-IDB of Peripheral Blood samples of normal individuals  ... 
doi:10.29042/2018-4519-4524 fatcat:m7bw3unqhrfkfmql5sgiro2lxi

A State-of-the-art Survey for Microorganism Image Segmentation Methods and Future Potential

Frank Kulwa, Chen Li, Xin Zhao, Bencheng Cai, Ning Xu, Shouliang Qi, Shuo Chen, Yueyang Teng
2019 IEEE Access  
INDEX TERMS Microorganism segmentation, content-based microscopic image analysis, feature extraction, microscopic images, classical methods, machine learning.  ...  A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened.  ...  For successful segmentation of yeast cells in poor contrast and low illumination images captured from live cells in [103] , a SegNet based neural network is deployed.  ... 
doi:10.1109/access.2019.2930111 fatcat:3xuu5nj7xnfdhpt6nsvrrlbe5u

Computer-assisted Diagnosis of Cancer on Medical Images: A Survey
English

D Hevin Rajesh, KS Angel Viji
2017 West Indian medical journal  
Results : Most of the classification techniques used on various medical images has been utilized for supervised learning approach such as support vector machine, artificial neural networks etc.  ...  Objective: To evaluate the cancer classification techniques based on sensitivity, specificity and accuracy.  ...  In this regard, the image based medical diagnosis has surfaced as one of the vital service segments in this domain.  ... 
doi:10.7727/wimj.2016.122 fatcat:nigl3gpmpfbrtlj4q2cscpnu7i

Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

G. Arutperumjothi, K. Suganya Devi, C. Rani, P. Srinivasan
2023 Intelligent Automation and Soft Computing  
In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil  ...  In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means (FCM) model whereas its centroid point optimality method with Slap Swarm based optimization  ...  Optimization Based Fuzzy C-Means Segmentation Approach In automatic blood cells examination, segmentation plays a vital role for locating individual cell type and grouping similar cell found in PBS analysis  ... 
doi:10.32604/iasc.2023.028423 fatcat:m7qqutfoibh7teetwcq2sww5l4

Classification of Corneal Pattern Based on Convolutional Neural Network

Nehad T. Haggag, Ahmed Sedik, Ghada M. Elbanby, Adel S. El-Fishawy, Waleed El-Shafai, Ashraf Khalaf, El-Sayed M. El-Rabie, Moawad I- Dessouky, Nabil A. Ismail, Fathi E. Abd El-Samie
2020 Menoufia Journal of Electronic Engineering Research  
The proposed technique was tested and evaluated based MATLAB environment on a set of corneal images. These images were collected for patients based on confocal microscopy.  ...  This paper presents an efficient approach for the classification of normal and abnormal corneal patterns based on deep learning. Convolutional Neural Networks (CNNs) are utilized for this purpose.  ...  Fabijańska 2019, introduced an approach to study the corneal health status based efficient automatic segmentation of corneal endothelial images.  ... 
doi:10.21608/mjeer.2020.103259 fatcat:an5bhk7ev5glrexzonikjxyzka

A SURVEY ON BRAIN TUMOR IDENTIFICATION THROUGH MEDICAL IMAGES

Priyanshu Tripathi
2017 International Journal of Advanced Research in Computer Science  
There are various methods which can be used to detect the tumor such as Segmentation, Thresholding, Fuzzy clustering or Artificial neural network.  ...  Brain Tumor can be described as a cluster of abnormal cells which grows inside the brain by uncontrolled growth in tissues of the brain which needs to be treated, If left untreated it can grow beyond to  ...  [9] proposed image segmentation using scale based Fuzzy and fuzzy object model to segment the parenchym of part of newly born brain MRI scan image.  ... 
doi:10.26483/ijarcs.v8i7.4296 fatcat:zfhq42n3obcp5j2sqrb4l2thcy

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Sarmad Shafique, Samabia Tehsin
2018 Computational and Mathematical Methods in Medicine  
In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia.  ...  Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the contents of this article.  ... 
doi:10.1155/2018/6125289 pmid:29681996 pmcid:PMC5851334 fatcat:n3w56rlfhncbtnshzbykotjnza
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