Computerized Segmentation Method For Breast Lesions on Ultrasound Images
P. Venkateswari1, P. Umamaheswari2, S.Ramya3, J. Glory Thephoral4

1P.Venkateswari, Asst. Professor, Department of CSE, SRC SASTRA Deemed to be University, Kumbaonam, Tamil Nadu, India.
2P.Umamaheswari, Asst. Professor, Department of CSE, SRC SASTRA Deemed to be University, Kumbaonam, Tamil Nadu, India.
3S.Ramya, , Dept of computer science &engineering, SRC, SASTRA Deemed University, TamilNadu, India.
4J.Glory Thephoral, Asst. Professor, Department of CSE, SRC SASTRA Deemed to be University, Kumbaonam, Tamil Nadu, India.

Manuscript received on 23 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 3228-3231 | Volume-8 Issue-11, September 2019. | Retrieval Number: K25250981119/2019©BEIESP | DOI: 10.35940/ijitee.K2525.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In recent days, the machine learning techniques are comprehensively utilized in the field of identifying breast carcinoma. It is a vital driving reason for death for women everywhere throughout the world. Since the reasons for the infection stay obscure, early detection and analysis are the keys to breast cancer control, and it can expand the accomplishment of treatment, spare life and also reduce cost. Digital mammogram based on the depth of mammogram images to recognizing the masses. Mammography is a standout amongst the best tool that has been generally utilized in early identification for breast cancer. It comprises are four phases, they are pre-handling, division, order, and highlight extraction. ROI [region of interest] technique mainly used to identify the limit of a disease bit and measure its size.
Keywords: Masses detection, digital mammogram, ROI [region of interest] technique, homographic filter.
Scope of the Article: Signal and Image Processing