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- W2600761307 abstract "Color segmentation of breast thermogramsThermograms can have a crucial performance in tumor detection. There is a relation between blood vessel activity and the surrounding area temperatureTemperature . Once a cancer increases blood vessel activity, the cancer cells and their surrounding tissue become hotter than normal tissue. Pre-cancer and cancer cells need plenty of nutrients to multiply and survive consequently; they are highly metabolic tissueTissue and have different thermal patterns compared to the normal one. In this paper, a comparison work is presented for three modeled color segmentation approaches: K-means, mean shift (MS), and fuzzy c-means (FCM)Fuzzy c-means (FCM) applied to infraredInfrared breast images. There are some drawbacks for K-means and MS approaches. Almost empty clusters may be obtained in the segmentation results using K-means algorithm. In addition, we frequently confront almost empty clusters with MS algorithm due to its sensitiveness to the window size parameter. Choosing an appropriate window size parameter is not an easy task. On the other hand, the fuzzy inherent breast thermal images aid the FCM technique to obtain more precise outcomes. Malignant tumors show hotter thermal patterns than healthy tissues and even with benign tissues. Segmenting different parts of two breasts in terms of their temperature has potential helping to identify abnormal breast tissues." @default.
- W2600761307 created "2017-04-07" @default.
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- W2600761307 date "2017-01-01" @default.
- W2600761307 modified "2023-10-18" @default.
- W2600761307 title "Color Segmentation of Breast Thermograms: A Comparative Study" @default.
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- W2600761307 doi "https://doi.org/10.1007/978-981-10-3147-2_6" @default.
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