CAD for detection of microcalcification and classification in mammograms

Date

2014-10-17

Author

AKBAY, Cansu
Gençer, Nevzat Güneri
GENÇER, Gülay

Metadata

Show full item record

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

Item Usage Stats

189
views

0
downloads

In this study, computer aided diagnosis (CAD) is developed to detect microcalficication cluster which is one of the important radiological findings of breast cancer diagnosis and classificiation. For this purpose, image processing and pattern recognition algorithms are applied on mamographic images. To make microcalcifications more visible wavelet transform and nonsubsampled contourlet transform (NSCT) methods are used for image enhancement. Their performances are compared. 52 features are extracted from the enhanced images. To reduce the dimension of the feature space, linear discriminant analysis is applied. It is observed that nonsubsampled contourlet transform outperforms the wavelet transform. Microcalcification clusters were classified by using support vector machine (SVM) by 94,6% correct rate.

Subject Keywords

Wavelet transforms, Computers, Feature extraction, Design automation, Support vector machines, Cancer

URI

https://hdl.handle.net/11511/40672

DOI

https://doi.org/10.1109/biyomut.2014.7026349

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

ANALYSIS OF NEUROONCOLOGICAL DATA TO PREDICT SUCCESS OF OPERATION THROUGH CLASSIFICATION Bagherzadi, Negin; BÖRCEK, ALP ÖZGÜN; TOKDEMİR, GÜL; ÇAĞILTAY, NERGİZ; MARAŞ, HADİ HAKAN (2016-10-05) Data mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classificatio...
Bivariate random effects and hierarchical meta-analysis of summary receiver operating characteristic curve on fine needle aspiration cytology Erte, İdil; Baykal, Nazife; Akçil, Mehtap; Department of Medical Informatics (2011) In this study, meta-analysis of diagnostic tests, Summary Receiver Operating Characteristic (SROC) curve, bivariate random effects and Hierarchical Summary Receiver Operating Characteristic (HSROC) curve theories have been discussed and accuracy in literature of Fine Needle Aspiration (FNA) biopsy that is used in the diagnosis of masses in breast cancer (malignant or benign) has been analyzed. FNA Cytological (FNAC) examination in breast tumor is, easy, effective, effortless, and does not require special tr...
GST isoenzymes in matched normal and neoplastic breast tissue OĞUZTÜZÜN, SERPİL; Abu-Hijleh, A.; ÇOBAN, TÜLAY; Bulbul, D.; KILIÇ, MURAT; İŞCAN, Mümtaz; İşcan, Mesude (AEPress, s.r.o., 2011-01-01) The potential to metabolize endogenous and exogenous substances may influence breast cancer development and tumor growth. Therefore we investigated GST activity and the protein expression of glutathione S-transferases (GSTs) isoenzymes known to be involved in the metabolism of endogenous and exogenous carcinogens in breast cancer tissue to obtain new information on their possible role in tumor progression.
Implementation of a fast simulation tool for the analysis of contrast mechanisms in HMMDI and enhancement of the SNR in the experimental set-up İrgin, Ümit; Gençer, Nevzat Güneri; Top, Can Barış; Department of Electrical and Electronics Engineering (2021-9-06) Clinical method for breast tumor detection is Mammography (X-rays), which have limitations and may yield inaccurate results. Alternative novel techniques are required to characterize the breast tissues and extract accurate information for identification of malignancies in the tissue. Harmonic Motion Microwave Doppler Imaging (HMMDI), which enhances hybridizing microwave signals and ultrasound techniques, has been recently proposed for detection of tumors in the tissue. In HMMDI method, the data is a combina...
Automated cancer stem cell recognition in H&E stained tissue using convolutional neural networks and color deconvolution Aichinger, Wolfgang; Krappe, Sebastian; ÇETİN, AHMET ENİS; Atalay, Rengül; ÜNER, AYŞEGÜL; Benz, Michaela; Wittenberg, Thomas; Stamminger, Marc; Muenzenmayer, Christian (2017-02-13) The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our wor...

Citation Formats

C. AKBAY, N. G. Gençer, and G. GENÇER, “CAD for detection of microcalcification and classification in mammograms,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40672.