Asian Journal of Information Technology

Year: 2016
Volume: 15
Issue: 18
Page No. 3506 - 3512

Denoising and Automatic Detection of Breast Tumor in Ultrasound Images

Authors : Telagarapu Prabhakar and S. Poonguzhali

Abstract: Over the past three decades, Breast cancer has been the leading cause of death. Detection of breast cancer at the early stage is a critical procedure. So far Mammography is used for screening and detection, but this method is actually found to be uncomfortable among young woman. Whereas ultrasound can be the best replacement for mammography as imaging of human organs and soft tissue can be done much more easily in ultrasound without much pain and it is cost effective as well. In the ultrasound, the only drawback is its poor quality which is affected by speckle noise which in turn makes the segmentation and classification of interested lesion problematic. Usually, active contour segmentation technique is used which is proved to be ineffective when we go for automatic detection and more over it usually causes improper segmentation and classification. So in order to escape improper segmentation and classification we have developed a scheme which capable to locate region of lesions automatically. This method involves Tetrolet Transform speckle reduction method followed by statistical features of the lesion region and K-Nearest Neighbor (KNN) classifier. This technique is tested over 110 lesion images of breast. The accuracy of this method is around 91.51% and Sensitivity is around 94.42%. The Dice similarity which is found to be 91.27% is obtained between segmented ROIs and ground truth images. Hence, the automatic segmentation of lesion region is made possible. This method will help the radiologist to detect the lesion boundary automatically.

How to cite this article:

Telagarapu Prabhakar and S. Poonguzhali, 2016. Denoising and Automatic Detection of Breast Tumor in Ultrasound Images. Asian Journal of Information Technology, 15: 3506-3512.

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