9 Citations (Scopus)

Abstract

According to Breast Cancer Institute (BCI), Breast cancer is one of the most dangerous types of cancer that affects women all around the world. Based on clinical guidelines, the use of mammogram for an early detection of this cancer is an important step in reducing its danger. Thus, computer aided detection using image processing techniques in analyzing mammogram images and localizing abnormalities such as mass has been used. A False Positive (FP) rate is considered a challenge in localizing mass in mammogram images. Hence, in this paper, the rejection model based on the Support Vector Machine (SVM) has been used in reducing the FP rate of segmented mammogram images using the Chan-Vese method, initialized by the Marker Controller Watershed (MCWS) algorithm. Firstly, a mammogram image is segmented using the MCWS algorithm. Then, the segmentation is refined using Chan-Vese. After that, the SVM rejection model is built and is used in rejecting the non-correct segmented nodules. The dataset which consists of 16 nodules and 28 non-nodules has been obtained from the UKM Medical Centre. The experiment has shown the effectiveness of the SVM rejection model in reducing the FP rate compared to the result without the use of the SVM rejection model.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014
PublisherIEEE Computer Society
Pages31-36
Number of pages6
ISBN (Print)9781479915323
DOIs
Publication statusPublished - 2014
Event2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014 - Kuala Lumpur
Duration: 7 Mar 20149 Mar 2014

Other

Other2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014
CityKuala Lumpur
Period7/3/149/3/14

Fingerprint

Support vector machines
Learning systems
Watersheds
Controllers
Image processing
Experiments

Keywords

  • Breast Cancer
  • Chan-Vese
  • Mammogram
  • MCWS
  • SVM

ASJC Scopus subject areas

  • Signal Processing

Cite this

Qasem, A., Sheikh Abdullah, S. N. H., Sahran, S., Tengku Wook, T. S. M., Iqbal Hussain, R., Abdullah, N., & Ismail, F. (2014). Breast cancer mass localization based on machine learning. In Proceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014 (pp. 31-36). [6805715] IEEE Computer Society. https://doi.org/10.1109/CSPA.2014.6805715

Breast cancer mass localization based on machine learning. / Qasem, Ashwaq; Sheikh Abdullah, Siti Norul Huda; Sahran, Shahnorbanun; Tengku Wook, Tengku Siti Meriam; Iqbal Hussain, Rizuana; Abdullah, Norlia; Ismail, Fuad.

Proceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014. IEEE Computer Society, 2014. p. 31-36 6805715.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Qasem, A, Sheikh Abdullah, SNH, Sahran, S, Tengku Wook, TSM, Iqbal Hussain, R, Abdullah, N & Ismail, F 2014, Breast cancer mass localization based on machine learning. in Proceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014., 6805715, IEEE Computer Society, pp. 31-36, 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014, Kuala Lumpur, 7/3/14. https://doi.org/10.1109/CSPA.2014.6805715
Qasem A, Sheikh Abdullah SNH, Sahran S, Tengku Wook TSM, Iqbal Hussain R, Abdullah N et al. Breast cancer mass localization based on machine learning. In Proceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014. IEEE Computer Society. 2014. p. 31-36. 6805715 https://doi.org/10.1109/CSPA.2014.6805715
Qasem, Ashwaq ; Sheikh Abdullah, Siti Norul Huda ; Sahran, Shahnorbanun ; Tengku Wook, Tengku Siti Meriam ; Iqbal Hussain, Rizuana ; Abdullah, Norlia ; Ismail, Fuad. / Breast cancer mass localization based on machine learning. Proceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014. IEEE Computer Society, 2014. pp. 31-36
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