Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images

Abstract

Breast cancer is already one of the most common form of cancer worldwide. Mammography image analysis is still the most effective diagnostic method to promote the early detection of breast cancer. Accurately segmenting tumors in digital mammography images is important to improve diagnosis capabilities of health specialists and avoid misdiagnosis. In this work, we evaluate the feasibility of applying GrowCut to segment regions of tumor and we propose two GrowCut semi-supervised versions. All the analysis was performed by evaluating the application of segmentation techniques to a set of images obtained from the Mini-MIAS mammography image database. GrowCut segmentation was compared to Region Growing, Active Contours, Random Walks and Graph Cut techniques. Experiments showed that GrowCut, when compared to the other techniques, was able to acquire better results for the metrics analyzed. Moreover, the proposed semi-supervised versions of GrowCut was proved to have a clinically satisfactory quality of segmentation. main described by Litière, Werutsky, et al. (2012). However, imaging devices used by BMH (Brazilian Ministry of Health) for the detection of breast cancer are quite inefficient at the evaluation of the nodule size, and these methods depend substantially on the professional examiner's experience (Porto, Teixeira and da Silva 2013). Furthermore, digital image diagnosis is complex, mainly because of the large variability of cases. Many cases seen in clinic practice do not fit classic images and descriptions precisely (JUHL, Crummy and Kuhlman 2000). For these reasons, mammography computer aided diagnosis (CAD) has been playing an import role to assist radiologists in improving the accuracy of their diagnoses. Consequently, traditional techniques in image processing have been applied in the medical field to make diagnosis less susceptible to errors through accurate identification of anatomic anomalies (Da-xi, Yuan and Sheng 2010; Ye, Zheng and Hao 2010).Recent works, as those of Liu, Xu, et al. (2011) and Mohamed, Behiels and Dewaele (2009), are quite accurate in identifying the location of tumors. However, little research has been done to verify the quality of segmentation. The shape of the segmented tumor is a determinant factor in the mammogram diagnosis. It is related to the gravity of the tumor and the difference of a few centimeters in the maximum diameter can determine if a surgery is necessary or not. However, it can be very difficult to detect the contour of the tumor accurately depending on several factors, such as shape of the tumor, density, size, location and image quality.? uses the general purpose user interactive technique GrowCut (Vezhnevets and Konouchine 2005) applied to segment images of mammography. This technique has been successfully applied in other areas, including medical image segmentation (Yamasaki, Chen, et al. 2012). The GrowCut also has the advantage of having no parameters to tune, which usually is not common in segmentation methods. ? applies the GrowCut algorithm for the first t...

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“…CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) raphy (de Vasconcelos et al, 2018;Pereira et al, 2020b;Santana et al, 2020) and mammography images (de Lima et al, 2016;Lima et al, 2015;de Lima et al, 2014;Silva et al, 2020;Cordeiro et al, 2017Cordeiro et al, , 2016de Lima et al, 2014;Cruz et al, 2018).…”

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