Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India.
Department of Electronics and Communication Engineering, VNR VJIET, Hyderabad, India.
Comput Methods Programs Biomed. 2017 Jul;146:59-68. doi: 10.1016/j.cmpb.2017.05.003. Epub 2017 May 15.
For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step.
To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells.
The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches.
The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection.
对于从微观活检图像中进行癌症检测，用于细胞和核分割的图像分割步骤起着重要作用。分割方法的准确性主导着最终结果。此外，微观活检图像具有内在的泊松噪声，如果图像中存在噪声，则分割结果可能不准确。目的是提出一种有效的基于模糊 c-均值的分割方法，该方法还可以在分割过程中处理图像中的噪声，即噪声消除和分割结合在一步中。
为了解决上述问题，本文提出了一种基于四阶偏微分方程（FPDE）的非线性滤波器，该滤波器适用于具有模糊 c-均值分割方法的泊松噪声。该方法能够有效地处理块状伪影的分割问题，同时在分割过程中实现良好的泊松噪声去除和边缘保持之间的权衡，以便从细胞中检测癌症。
该方法在包含 31 个良性和 27 个恶性图像的 896×768 大小的乳腺癌微观活检数据集上进行了测试。该数据集还提供了所有 58 个图像的感兴趣区域（ROI）分割地面实况图像。最后，将所提出的方法的结果与流行的分割算法的结果进行了比较；模糊 c-均值、颜色 k-均值、基于纹理的分割和总变分模糊 c-均值方法。
实验结果表明，与用于癌症检测的其他分割方法相比，所提出的方法在各种性能指标（如 Jaccard 系数、骰子指数、Tanimoto 系数、曲线下面积、准确性、真阳性率、真阴性率、假阳性率、假阴性率、随机指数、全局一致性误差和信息方差）方面提供了更好的结果。
