Ilesanmi Ademola Enitan, Idowu Oluwagbenga Paul, Makhanov Stanislav S
School of ICT,Sirindhorn International Institute of Technology,Thammasat University, Pathumthani 12000, Thailand.
Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Comput Biol Med. 2020 Oct;125:103879. doi: 10.1016/j.compbiomed.2020.103879. Epub 2020 Jul 6.
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.
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.
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.
The proposed method outperforms selected state-of-the-art segmentation methods with an average segmentation accuracy of 94%.
在医学诊断中，乳腺超声是一种成本低廉且灵活的成像方式。对乳腺超声进行分割以识别肿瘤区域是一项具有挑战性且复杂的任务。有效识别肿瘤的主要问题是斑点噪声、伪像和低对比度。分割的金标准是手动处理；然而，手动分割是一项繁琐的任务。为了解决这个问题，提出了用于乳腺超声分割的自动多尺度超像素方法。
将原始乳腺超声图像转换为多尺度图像，然后对多尺度图像进行预处理。接下来，对多尺度图像进行边界高效超像素分解。最后，通过边界图割分割方法生成肿瘤区域。使用泰国玛希隆大学医院数据库中的120张图像对所提出的方法进行评估。该数据集包括30张恶性肿瘤、30张良性肿瘤、60张纤维瘤和60张囊肿图像。使用了诸如准确率、灵敏度、特异性、骰子系数、杰卡德指数和豪斯多夫距离等常用指标进行评估。
结果表明，所提出的方法对良性肿瘤的分割准确率达到97.3%，对恶性肿瘤为94.2%，对囊肿为96.4%，对纤维瘤为96.7%。结果验证了所提出的模型优于选定的现有最先进分割方法。
所提出的方法优于选定的现有最先进分割方法，平均分割准确率为94%。
