Udayakumar E, Santhi S, Vetrivelan P
Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India.
Department of ECE, PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India.
Indian J Med Paediatr Oncol. 2017 Jul-Sep;38(3):340-344. doi: 10.4103/ijmpo.ijmpo_127_17.
Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time.
To improve the primary sign of this disease, computer-aided diagnosis schemes have been developed. Using monitor, digital images of mammography are displayed and they can be lightened or darkened before they are printed on the film. Time factor is important to identify the abnormality in body such as breast cancer and lung cancer. Hence, to detect the tissues and treatment stages, image-processing techniques are improved in several medical areas. In this project, using low-level preprocessing techniques and image segmentation, the breast cancer detection is done.
With the help of Bayes algorithm and neural networks (NNs), the type of the mammogram and stages is identified. For segmentation process, region-growing algorithm is used, which helps to find the affected portion, i.e., region of interest. Gray-level co-occurrence matrix (GLCM) and texture feature are used for feature extraction.
Bayes algorithm is used for probability of identification, whereas NNs is used to reduce the probability level from 0-1000 to 0-1 in case of classification.
乳腺癌是对女性最大的威胁。乳腺X线摄影是早期检测和筛查乳腺癌最有效的方法。对于放射科医生来说，阅读乳腺X线摄影是一项艰巨的挑战，因为每次结果并不一致。
为了改善这种疾病的主要体征，已经开发了计算机辅助诊断方案。利用显示器，可以显示乳腺X线摄影的数字图像，并且在打印到胶片之前可以对其进行变亮或变暗处理。时间因素对于识别身体中的异常情况（如乳腺癌和肺癌）很重要。因此，为了检测组织和治疗阶段，在几个医学领域改进了图像处理技术。在这个项目中，利用低级预处理技术和图像分割来进行乳腺癌检测。
借助贝叶斯算法和神经网络（NNs），识别乳腺X线摄影的类型和阶段。对于分割过程，使用区域生长算法，该算法有助于找到受影响的部分，即感兴趣区域。灰度共生矩阵（GLCM）和纹理特征用于特征提取。
贝叶斯算法用于识别概率，而在分类时，神经网络用于将概率水平从0 - 1000降低到0 - 1。
