Classification of Human Carcinoma Cells Using Multispectral Imagery

Download

index.pdf

Date

2016-03-03

Author

Çinar, Umut
Çetin, Yasemin
Atalay, Rengül
Cetin, Enis

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

174
views

0
downloads

In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.

Subject Keywords

Multispectral imaging, Automatic classification, Cancer cells, Gabor features, Microscopy

URI

https://hdl.handle.net/11511/57776

DOI

https://doi.org/10.1117/12.2217022

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

DETECTION OF CANCER STEM CELLS IN MICROSCOPIC IMAGES BY USING REGION COVARIANCE AND CODIFFERENCE METHOD Oguz, Oguzhan; Muenzenmayer, Christian; Wittenberg, Thomas; ÜNER, AYŞEGÜL; ÇETİN, AHMET ENİS; Atalay, Rengül (2015-10-30) This paper presents a cancer stem cell detection method using region covariance and codifference method. It focuses on detection of Cancer Stem Cell (CSC) in microscopic images which are stained with CD13 marker. Features of CSC images are extracted by using both covariance method and its multiplication free version codifference method and these features are fed into a Support Vector Machine (SVM) for classification. Experimental results are presented.
Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function Çinar, Umut; Cetin Atalay, Rengul; Çetin, Yasemin (2023-02-01) This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial featur...
Classification of hyperspectral images based on weighted DMPs Ulusoy, İlkay; MURA, Mauro Dalla (2012-07-27) This paper presents a classification method for hyperspectral images utilizing Differential Morphological Profiles (DMPs) which permit to include in the analysis spatial information since they can provide an estimate of the size and contrast characteristics of the structures in an image. Due to the wide variety of objects present in a scene, the pixels belonging to the same semantic structure may not have homogeneous spatial and spectral features. In addition, instead of a single peak (which can be related ...
Representation of human brain by mesh networks Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017) In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
A cell formation algorithm: Hypergraph approximation - Cut tree Kandiller, L (1998-09-16) A new cell formation technique is presented and analyzed in this paper. The cell formation problem is defined using the hypergraph representation of the manufacturing systems. The proposed method approximates the hypergraph model by graphs so that the cuts are less affected by the approximation. Consequently, a Gomory-Hu cut tree of the graph approximation is obtained. The minimum cuts between all pairs of vertices are calculated easily by means of this tree, and a partition tree is produced. Our cell forma...

Citation Formats

U. Çinar, Y. Çetin, R. Atalay, and E. Cetin, “Classification of Human Carcinoma Cells Using Multispectral Imagery,” 2016, vol. 9791, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57776.