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Journal ArticleDOI

M-FISH Image Segmentation and Classification using Fuzzy Logic

31 May 2013-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 70, Iss: 25, pp 46-51
About: This article is published in International Journal of Computer Applications.The article was published on 2013-05-31 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Fuzzy logic & Image segmentation.

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Citations
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Book ChapterDOI
01 Jan 2016
TL;DR: An overview on different classification techniques elaborated with microscopic images is presented to guide the reader through the advanced knowledge of major quantitative image classification approaches.
Abstract: Microscopic image analysis plays a foremost role for understanding biological processes, diagnosis of diseases and cells/ tissues identification. Microscopic image classification is one of the challenging tasks that have a leading role in the medical domain. In this chapter, an overview on different classification techniques elaborated with microscopic images is presented to guide the reader through the advanced knowledge of major quantitative image classification approaches. Applied examples are conducted to classify different Albino rats’ samples captured using light microscope for three different organs, namely hippocampus, renal and pancreas. The Bag-of-Features (BoF) technique was employed for features extraction and selection. The BoF selected features were used as input to the multiclass linear support vector machine classifier. The proposed classifier achieved 94.33% average classification accuracy for the three classes. Additionally, for binary classification the achieved average accuracy was 100% for hippocampus and pancreas sets classification.

4 citations

Proceedings ArticleDOI
06 Mar 2014
TL;DR: The principal assets of the paper are that predicting the diseases diabetic and cancer with efficient training mechanism in a way that less human endeavor and higher correctness are achieved.
Abstract: Image segmentation in conventional learning approaches, the consumer applies only labeled or unlabelled training data set. The advanced application of segmentation in semi supervised learning to build better understanding of learners such a way that user could able to use both labeled data and un labeled data. In this research work focus to multi image model for semi supervised segmentation in retina and cancer cell images. The principal assets of the paper are that predicting the diseases diabetic and cancer with efficient training mechanism in a way that less human endeavor and higher correctness are achieved. We highlight the semi supervised segmentation in multi image model to classify diseased image or non diseased image.

3 citations

References
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Book
31 Jul 1981
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

15,662 citations

Journal ArticleDOI
TL;DR: The data suggest that multiplex-fluorescence in situ hybridization (M-FISH) could have wide clinical utility and complement standard cytogenetics, particularly for the characterization of complex karyotypes.
Abstract: We have developed epifluorescence filter sets and computer software for the detection and discrimination of 27 different DNA probes hybridized simultaneously. For karyotype analysis, a pool of human chromosome painting probes, each labelled with a different fluor combination, was hybridized to metaphase chromosomes prepared from normal cells, clinical specimens, and neoplastic cell lines. Both simple and complex chromosomal rearrangements could be detected rapidly and unequivocally; many of the more complex chromosomal abnormalities could not be delineated by conventional cytogenetic banding techniques. Our data suggest that multiplex-fluorescence in situ hybridization (M-FISH) could have wide clinical utility and complement standard cytogenetics, particularly for the characterization of complex karyotypes.

1,333 citations

Journal ArticleDOI
TL;DR: 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images, and its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.
Abstract: An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

841 citations

Journal ArticleDOI
TL;DR: This paper used an epifluorescence microscope equipped with a digital imaging camera and computer software for pseudocoloring and merging images to distinguish up to seven different probes using only three fluorochromes.
Abstract: Combinatorial labeling of probes (i.e., with two or more different reporters) increases the number of target sequences that can be detected simultaneously by fluorescence in situ hybridization. We have used an epifluorescence microscope equipped with a digital imaging camera and computer software for pseudocoloring and merging images to distinguish up to seven different probes using only three fluorochromes. Chromosome-specific centromere repeat clones and chromosome-specific "composite" probe sets were generated by PCR in which different mixtures of modified nucleotides, including fluorescein-conjugated dUTP, were incorporated. Cosmid clones were labeled similarly by nick-translation. The technique has been used to delineate the centromeres of seven different human chromosomes, on both 4',6-diamidino-2-phenylindole-stained metaphase spreads and interphase nuclei, to map six cosmid clones in a single hybridization experiment and to detect chromosome translocations by chromosome painting. Multiparameter hybridization analysis should facilitate molecular cytogenetics, probe-based pathogen diagnosis, and gene mapping studies.

462 citations

Journal ArticleDOI
TL;DR: A novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images is presented.

455 citations