Background

In medical diagnostics, breast ultrasound is an inexpensive and flexible imaging modality. The segmentation of breast ultrasounds to identify tumour regions is a challenging and complex task. The major problems of effective tumour identification are speckle noise, artefacts and low contrast. The gold standard for segmentation is manual processing; however, manual segmentation is a cumbersome task. To address this problem, the automatic multiscale superpixel method for the segmentation of breast ultrasounds is proposed.

Methods

The original breast ultrasound image was transformed into multiscaled images, and then, the multiscaled images were preprocessed. Next, a boundary efficient superpixel decomposition of the multiscaled images was created. Finally, the tumour region was generated by the boundary graph cut segmentation method. The proposed method was evaluated with 120 images from the Thammassat University Hospital database. The dataset consists of 30 malignant, 30 benign tumors, 60 fibroadenoma, and 60 cyst images. Popular metrics, such as the accuracy, sensitivity, specificity, Dice index, Jaccard index and Hausdorff distance, were used for the evaluation.

Results

The results indicate that the proposed method achieves segmentation accuracy of 97.3% for benign tumors, 94.2% for malignant, 96.4% for cysts and 96.7% for fibroadenomas. The results validate that the proposed model outperforms selected state-of-the-art segmentation methods.

Conclusions

The proposed method outperforms selected state-of-the-art segmentation methods with an average segmentation accuracy of 94%.

中文翻译：

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乳房超声分割的多尺度超像素方法。

背景

在医学诊断中，乳房超声检查是一种廉价且灵活的成像方式。乳房超声的分割以识别肿瘤区域是一项艰巨而复杂的任务。有效识别肿瘤的主要问题是斑点噪声，伪影和低对比度。细分的黄金标准是手动处理；但是，手动分段是一项繁琐的任务。为了解决这个问题，提出了一种自动多尺度超像素分割乳房超声的方法。

方法

将原始的乳房超声图像转换为多比例图像，然后对多比例图像进行预处理。接下来，创建了多尺度图像的边界有效超像素分解。最后，通过边界图切割分割方法产生肿瘤区域。用Thammassat大学医院数据库中的120张图像对提出的方法进行了评估。该数据集包含30个恶性肿瘤，30个良性肿瘤，60个纤维腺瘤和60个囊肿图像。评估使用了流行的指标，例如准确性，敏感性，特异性，Dice指数，Jaccard指数和Hausdorff距离。

结果

结果表明，所提出的方法对良性肿瘤的分割精度为97.3％，对恶性肿瘤的分割精度为94.2％，对于囊肿的分割精度为96.4％，对于纤维腺瘤的分割精度为96.7％。结果验证了所提出的模型优于所选的最新细分方法。

结论

所提出的方法以94％的平均细分精度优于精选的最新细分方法。