Image segmentation based on merging of sub-optimal segmentations

MSMCNet: Differential context drives accurate localization and edge smoothing of lesions for medical image segmentation

Medical image segmentation plays a crucial role in clinical assistance for diagnosis. The UNet-based network architecture has achieved tremendous success in the field of medical image segmentation. However, most methods commonly employ element-wise addition or channel merging to fuse features, resulting in smaller differentiation of feature information and excessive redundancy. Consequently, this leads to issues such as inaccurate lesion localization and blurred boundaries in segmentation. To alleviate these problems, the Multi-scale Subtraction and Multi-key Context Conversion Networks (MSMCNet) are proposed for medical image segmentation. Through the construction of differentiated contextual representations, MSMCNet emphasizes vital information and achieves precise medical image segmentation by accurately localizing lesions and enhancing boundary perception. Specifically, the construction of differentiated contextual representations is accomplished through the proposed Multi-scale Non-crossover Subtraction (MSNS) module and Multi-key Context Conversion Module (MCCM). The MSNS module utilizes the context of MCCM coding and redistribute the value of feature map pixels. Extensive experiments were conducted on widely used public datasets, including the ISIC-2018 dataset, COVID-19-CT-Seg dataset, Kvasir dataset, as well as a privately constructed traumatic brain injury dataset. The experimental results demonstrated that our proposed MSMCNet outperforms state-of-the-art medical image segmentation methods across different evaluation metrics.

RFPNet: Reorganizing feature pyramid networks for medical image segmentation

Medical image segmentation is a crucial step in clinical treatment planning. However, automatic and accurate medical image segmentation remains a challenging task, owing to the difficulty in data acquisition, the heterogeneity and large variation of the lesion tissue. In order to explore image segmentation tasks in different scenarios, we propose a novel network, called Reorganization Feature Pyramid Network (RFPNet), which uses alternately cascaded Thinned Encoder-Decoder Modules (TEDMs) to construct semantic features in various scales at different levels. The proposed RFPNet is composed of base feature construction module, feature pyramid reorganization module and multi-branch feature decoder module. The first module constructs the multi-scale input features. The second module first reorganizes the multi-level features and then recalibrates the responses between integrated feature channels. The third module weights the results obtained from different decoder branches. Extensive experiments conducted on ISIC2018, LUNA2016, RIM-ONE-r1 and CHAOS datasets show that RFPNet achieves Dice scores of 90.47%, 98.31%, 96.88%, 92.05% (Average between classes) and Jaccard scores of 83.95%, 97.05%, 94.04%, 88.78% (Average between classes). In quantitative analysis, RFPNet outperforms some classical methods as well as state-of-the-art methods. Meanwhile, the visual segmentation results demonstrate that RFPNet can excellently segment target areas from clinical datasets.

Wavelet Frame-Based Fuzzy C-Means Clustering for Segmenting Images on Graphs

In recent years, image processing in a Euclidean domain has been well studied. Practical problems in computer vision and geometric modeling involve image data defined in irregular domains, which can be modeled by huge graphs. In this paper, a wavelet frame-based fuzzy C -means (FCM) algorithm for segmenting images on graphs is presented. To enhance its robustness, images on graphs are first filtered by using spatial information. Since a real image usually exhibits sparse approximation under a tight wavelet frame system, feature spaces of images on graphs can be obtained. Combining the original and filtered feature sets, this paper uses the FCM algorithm for segmentation of images on graphs contaminated by noise of different intensities. Finally, some supporting numerical experiments and comparison with other FCM-related algorithms are provided. Experimental results reported for synthetic and real images on graphs demonstrate that the proposed algorithm is effective and efficient, and has a better ability for segmentation of images on graphs than other improved FCM algorithms existing in the literature. The approach can effectively remove noise and retain feature details of images on graphs. It offers a new avenue for segmenting images in irregular domains.

A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

Describing reflectances for color segmentation robust to shadows, highlights, and textures

The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost.

An edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation

This correspondence proposes an edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. Two improvement techniques are proposed for the two key steps of maker extraction and pixel labeling, respectively, to make it more effective and efficient for high spatial resolution image segmentation. Moreover, the edge information, detected by the edge detector embedded with confidence, is used to direct the two key steps for detecting objects with weak boundary and improving the positional accuracy of the objects boundary. Experiments on different images show that the proposed method has a good generality in producing good segmentation results. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation.

Cross-Resolution Image Similarity Modeling

A cross-resolution image similarity model employing probabilistic interpretations of the similarity values turned into fuzzy values combined with suitable merge functions is presented. Masking the negative particularities of individual region-based image similarity models, five region-based image similarity models are used at the same time when calculating the overall image similarity. By employing aggregation operators, capturing of a variety of conjunctive, disjunctive, and other non-linear combinations of similarity criteria is allowed. Empirical evaluation of the proposed model on four test databases, containing 4,444 images in 150 semantic categories was carried out. The results obtained from the evaluation revealed that cross-resolution image similarity modeling results in optimal retrieval performance. Compared to two well-known image retrieval systems, SIMPLicity and WBIIS, the proposed model brings an increase of 1.7% and 22% respectively in average retrieval precision. The experimental evaluation presented may thus be helpful and suggest possible further improvements can be achieved along the same line of research directions in various computer vision tasks.