Multiscale superpixel method for segmentation of breast ultrasound
Introduction
The World Health Organization estimated that over 9.8 million people died from cancer related diseases in 2018. Statistically, 1 in 6 global deaths is cancer related. An estimated 627,000 women died from breast cancer in 2018, which is 15% of the cancer deaths among women [1]. Early detection, screening and treatments are urgent steps to avoid complications in breast cancer. Imaging techniques like Magnetic Resonance Imaging (MRI), mammography, ultrasounds, and breast Computer Tomography (CT) are used for medical diagnosis. From the list of available imaging techniques, ultrasounds are commonly used because they are non-invasive, cheap and easy to access. However, the major problems with ultrasounds are their poor quality, acoustic shadowing and speckle noise. Speckle noise is a multiplicative noise that appears in a granular form. Furthermore, speckle noise is associated with echoes that originate from obstruction patterns. Speckle noise destroys the resolution, brightness and luminance of ultrasound images [2]. (see Fig. 12)
The gold standard for the segmentation of Breast Ultrasounds (BUSs) is manual segmentation. Manual segmentation is a procedure in which an experienced radiologist manually outlines and identifies tumours. Unfortunately, this procedure is cumbersome and time consuming, and hence the Computer-Aided Diagnostic (CAD) system has been introduced. CAD is a computerized tool to analyse medical images with a limited performance apparatus. Kandemir et al. [3] discussed the usage of CAD for ultrasound and medical images. Specifically, CAD is not a tool to replace clinicians; rather, it is a tool to act as a second interpreter for diagnosing medical images. In general, CAD is categorized into: semi-automatic and automatic. A semi-automatic CAD outlines tumours using user interaction tools; alternatively, automatic CAD does not require user interaction [4]. This paper utilized the automatic process to segment breast ultrasounds. In a nutshell, the proposed method creates multiscaled images from the original BUS image, and then the multiscaled images are preprocessed and a superpixel decomposition is created
The remainder of this paper is as follows. A literature review is given in Section 2. The materials and proposed method are reported in Section 3. The results of the numerical experiment are reported in Section 4. Finally, a discussion is given in Section 5 and the paper is concluded in Section 6.
Section snippets
Related work
Several methods have been proposed for the segmentation of BUSs. The commonly used methods are the watershed, graph cut, and active contour methods [5]. A detailed review of BUS segmentation is in Refs. [6,7]. In this section, a brief insight into various superpixel methods and related segmentation is given. A superpixel is a set of connected pixels with a similar colour, intensity or other low-level properties [8]. The major advantages of using superpixels are: reduced computational costs and
Data acquisition
The database used in this study contains 180 BUS images, i.e. 30 malignant, 30 benign tumors, 60 fibroadenoma, and 60 cyst images. Malignant, benign and fibroadenoma images were selected from patients with a histopathological diagnose. The malignant tumours have irregular boundaries; fibroadenoma images are characterized by low contrast; and cyst images include large irregular shadows, artefacts and random noise (see Fig. 1). The images were obtained by a Philips iU22 ultrasound machine from
Results
All experiments for this paper were implemented on Matlab R2018a with a system configuration of an Intel(R) 3.60 GHz CPU, 16 GB of RAM and a Windows 10 Operating System. In this paper, four cases of BUS segmentation have been reported. Case 1 is the result of the segmentation of other benign BUSs, Case 2 is the result of the segmentation of malignant BUSs, case 3 is the result of segmentation of cyst BUSs, and case 4 is the results of the segmentation of fibroadenoma BUSs.
Discussion
The proposed method involves the segmentation and evaluation of breast ultrasounds (case 1 to case 4). The results of the visual analysis indicate that the proposed method was close to the ground truth. The mean and standard deviation have been recorded for the proposed method and the competing methods. The values of different measures as seen from the different tables indicate that the proposed method is effective for the segmentation of breast ultrasounds (see Table 1).
Visual comparisons
Conclusion
In this paper, a novel multiscale superpixel method for segmentation of BUS images based on the boundary efficient graph cut method was presented. A real dataset of breast ultrasounds obtained from Thammasat University Hospital was used for the evaluation. Specifically, a multiscale image from the original BUS image was created, and then the BAS was used to remove speckle noise. Subsequently, a distance transform superpixel decomposition of the multiscale images was generated before finally
Declaration of competing interest
All authors in this paper have no potential conflict of interests.
Acknowledgements
This research is supported by the Thailand Research Fund grant RSA6280098 and the Center of Excellence in Biomedical Engineering of Thammasat University. We wish to thank the anonymous referees for their valuable remarks.
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