Venkatachalam Nirmala, Shanmugam Leninisha, Heltin Genitha C, Govindarajan G, Sasipriya P
Computer Science and Engineering, St. Joseph's College of Engineering, OMR, Chennai 600 119, India.
School of Computer Science and Engineering, VIT University, Chennai, India.
J Oncol. 2021 Apr 21;2021:5566853. doi: 10.1155/2021/5566853. eCollection 2021.
Effective analysis of breast thermography needs an accurate segmentation of the inflamed region in Infrared Breast Thermal Images (IBTI) which helps in the diagnosis of breast cancer. However, IBTI suffers from intensity inhomogeneity, overlapping regions of interest, poor contrast, and low signal-to-noise ratio (SNR) due to the imperfect image acquisition process. To mitigate this, this work proposes an enhanced segmentation of the inflamed Region of Interest (ROI) using an active contour method driven by the multiscale local and global fitted image (MLGFI) model. The first phase proposes a bilateral histogram difference-based thresholding (BHDT) method for locating the inflamed ROI. This is then used for automatic initialization of active contours driven by MLGFI to segment the inflamed ROI from IBTI effectively. To prove the effectiveness of this segmentation method, its performance is compared with ground truth image and its accuracy is also evaluated with the state-of-the-art methods (Fuzzy C Means (FCM), Chan-Vese (CV-ACM), and K-means). From the analysis, it is found that the proposed method not only increases the precision and the segmentation accuracy but also reduces the oversegmentation and undersegmentation rate significantly. In the second phase, area-based feature (AF) and average intensity-based feature (AIF) along with the GLCM (gray level cooccurrence matrix) based second-order statistical features are extracted from the inflamed ROI. Based on these features, a system is developed to effectively classify the benign and malignant breast conditions. From the results, it is observed that the proposed model exhibits an improved accuracy of 91.5%, sensitivity of 91%, and specificity of 92% compared to the whole breast thermogram. Hence, it is concluded that the proposed method will improve the efficacy of thermal imaging in the diagnosis of breast cancer.
对乳腺热成像进行有效的分析需要在红外乳腺热图像（IBTI）中准确分割出炎症区域，这有助于乳腺癌的诊断。然而，由于图像采集过程不完善，IBTI存在强度不均匀、感兴趣区域重叠、对比度差以及信噪比（SNR）低等问题。为了缓解这些问题，这项工作提出了一种使用多尺度局部和全局拟合图像（MLGFI）模型驱动的主动轮廓方法对炎症感兴趣区域（ROI）进行增强分割。第一阶段提出了一种基于双边直方图差异的阈值化（BHDT）方法来定位炎症ROI。然后将其用于由MLGFI驱动的主动轮廓的自动初始化，以有效地从IBTI中分割出炎症ROI。为了证明这种分割方法的有效性，将其性能与真实图像进行比较，并使用最先进的方法（模糊C均值（FCM）、Chan-Vese（CV-ACM）和K均值）对其准确性进行评估。通过分析发现，所提出的方法不仅提高了精度和分割准确性，还显著降低了过分割和欠分割率。在第二阶段，从炎症ROI中提取基于面积的特征（AF）和基于平均强度的特征（AIF）以及基于灰度共生矩阵（GLCM）的二阶统计特征。基于这些特征，开发了一个系统来有效地对良性和恶性乳腺状况进行分类。从结果可以看出，与整个乳腺热成像图相比，所提出的模型具有91.5%的改进准确率、91%的灵敏度和92%的特异性。因此，可以得出结论，所提出的方法将提高热成像在乳腺癌诊断中的功效。
