A Semi‑Supervised Method for Tumor Segmentation in Mammogram Images
Publish place: Journal of medical signals and sensors، Vol: 10، Issue: 1
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMSI-10-1_002
تاریخ نمایه سازی: 28 تیر 1402
Abstract:
Background: Breast cancer is one of the most common cancers in women. Mammogram images
have an important role in the treatment of various states of this cancer. In recent years, machine
learning methods have been widely used for tumor segmentation in mammogram images. Pixelbased
segmentation methods have been presented using both supervised and unsupervised learning
approaches. Supervised learning methods are usually fast and accurate, but they usually use a
large number of labeled data. Besides, providing these samples is very hard and usually expensive.
Unsupervised learning methods do not require the labels of the training data for decision making and
they completely ignore the prior knowledge that may lead to a low performance. Semi-supervised
learning methods which use a small number of labeled data solve the problem of providing the
high number of samples in supervised methods, while they usually result in a higher accuracy in
comparison to the unsupervised methods. Methods: In this study, we used a semisupervised method
for tumor segmentation in which the pixel information is used for the classification. The static
and gray level run length matrix features for each pixel are considered as the features, and Fisher
discriminant analysis (FDA) is used for feature reduction. A cotraining algorithm based on support
vector machine and Bayes classifiers is proposed for tumor segmentation on MIAS data set. Results
and Conclusion: The results show that the proposed method outperforms both supervised methods.
Keywords:
Bayes classifier , co‑training algorithm , mammogram images , support vector machine classifier , tumor segmentation
Authors
Hanie Azary
School of Computer Engineering, Iran University of Science and Engineering
Monireh Abdoos
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran