Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105788
Title: Level set method for segmentation of infrared breast thermograms
Authors: Golestani, N.
Ng, E. Y. K.
EtehadTavakol, M.
Keywords: DRNTU::Engineering::Mechanical engineering::Control engineering
Issue Date: 2014
Source: Golestani, N., EtehadTavakol, M., & Ng, E. Y. K. (2014). Level set method for segmentation of infrared breast thermograms. EXCLI journal, 12, 241-251.
Series/Report no.: EXCLI journal
Abstract: Breast thermography is a physiological test that provides information based on the tempera-ture changes in breast. It records the temperature distribution of a body using the infrared ra-diation emitted by the surface of that body. Precancerous tissue and the area around a cancer-ous tumor have higher temperature due to angiogenesis, and higher chemical and blood vessel activity than a normal breast; hence breast thermography has potential to detect early abnor-mal changes in breast tissues. It can detect the first sign of forming up cancer before mam-mography can detect. The thermal information can be shown in a pseudo colored image where each color represents a specific range of temperature. Various methods can be applied to extract hot regions for detecting suspected regions of interests in the breast infrared images and potentially suspicious tissues. Image segmentation techniques can play an important role to segment and extract these regions in the breast infrared images. Shape, size and borders of the hottest regions of the images can help to determine features which are used to detect ab-normalities. In this paper, three image segmentation methods: k-means, fuzzy c-means and level set are discussed and compared. These three methods are tested for different cases such as fibrocystic, inflammatory cancer cases. The hottest regions of thermal breast images in all cases are extracted and compared to the original images. According to the results, level set method is a more accurate approach and has potential to extract almost exact shape of tumors.
URI: https://hdl.handle.net/10356/105788
http://hdl.handle.net/10220/20931
Rights: © 2014 The Authors(EXCLI Journal). This paper was published in EXCLI Journal and is made available as an electronic reprint (preprint) with permission of The Authors. The paper can be found at the following official URL: [http://www.excli.de/vol13/Ng_12032014_proof.pdf]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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