Contextual Level-Set Method for Breast Tumor Segmentation

Abstract

Breast ultrasound image segmentation is the foundation of the diagnosis and treatment of breast cancer. The level set method is widely used for medical image segmentation. However, it remained a challenge for traditional level set methods because they cannot fully understand the tumor regions with complex characteristics by only low-level features. Considering that contextual features can provide complementary discriminative information to low-level features, this paper proposed a contextual level set method for breast tumor segmentation. Firstly, an encoder-decoder architecture network such as UNet is developed to learn high-level contextual features with semantic information. After that, the contextual level set method has been proposed to incorporate the novel contextual energy term. The proposed term has the ability to embed the high-level contextual knowledge into the level set framework. The learned contextual features with semantic information can provide more discriminative information, which has been directly associated with category labels, instead of the original intensity. Therefore, it is robust to serious intensity inhomogeneity, which is helpful to improve segmentation performance. The experiments had taken place with the help of three databases, which indicates that the proposed method outperformed traditional methods. INDEX TERMS Breast ultrasound images, contextual feature, Level-set method, tumor segmentation.

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

“…S. Hussain et al [24], have proposed contextual level-set method for breast tumor segmentation, by developing an encoder-decoder architecture network such as UNet to learn highlevel contextual features with semantic information, then the contextual level set method has been introduced for incorporating the contextual energy term, the proposed term can embed the high-level contextual knowledge into the level set framework, then more discriminative PLOS ONE information been directly related to class labels (instead of original intensity) can be provided by the learned contextual features with semantic information. W. Al-Dhabyani et al [33], have introduced their study about data augmentation and classification for breast masses in BUS images by DL approaches, validating their work by two different approaches (CNN and TL) with and without augmentation.…”

“…Efficient DL based automatic SS, being a challenging task, is aiming to label each pixel in an image with a corresponding class using supervised learning [11,[17][18][19][20][21][22]. For BUS images, SS is the classification task for two tissue's classes: normal and abnormal [10,11,14,16,23,24]. Image preprocessing enhancement before automatic SS could play an important role in achieving more accurate and efficient segmented image.…”

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

“…One study [ 14 ] worked on fuzzy interpolative reasoning and selected features using a feature-ranking technique, but it achieved 91.65% accuracy with less sensitivity. Similarly, a deep learning-based ultrasonic image classification, proposed by [ 22 ], used submodules with parameter selection to achieve a 96.41% accuracy. Another research study [ 31 ] used SVM, KNN, Discriminant Analysis, and random forest classifiers and achieved 82.69%, 63.49%, 78.85%, and 65.83% accuracy, respectively.…”

“…The semantic information is gathered using U-net, and contextual features are then added to create a new energy term [ 22 ]. The encoded U-Net approach is used for breast tumor segmentation, and it achieves nearly 90.5 percent dice score on the 510 image dataset.…”

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

“…Hussain et al [ 50 ] presented a contextual level set method for segmentation of breast tumors. They designed a UNet-style encoder-decoder architecture network to learn high-level contextual aspects from semantic data.…”