[New] Generating Region Proposals for Histopathological Whole Slide Image Retrieval
Yibing Ma, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yushan Zheng, Huaqiang Shi, Yu Zhao and Jun Shi
Computer Methods and Programs in Biomedicine, 2018
Abstract   BibTeX  

Background and objective Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. Methods This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus–Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. Results The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. Conclusions The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems.

@article{maCMPB2018,
  title   = {Generating region proposals for histopathological 
             whole slide image retrieval},
  author  = {Yibing Ma and Zhiguo Jiang and Haopeng Zhang and Fengying Xie 
             and Yushan Zheng and Huaqiang Shi and Yu Zhao and Jun Shi},
  journal = {Computer Methods and Programs in Biomedicine},
  volume  = {159},
  pages   = {1 - 10},
  year    = {2018},
  issn    = {0169-2607},
  url     = {http://www.sciencedirect.com/science/article/pii/S0169260717312154},
}      
                    

[New] Robust Spacecraft Component Detection in Point Clouds
Quanmao Wei, Zhiguo Jiang and Haopeng Zhang
Sensors, 2018
PDF   Abstract   BibTeX   Supplementary   Code  

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density.

@article{weiSensors18,
  author  = {Quanmao Wei and Zhiguo Jiang and Haopeng Zhang},
  title   = {Robust Spacecraft Component Detection in Point Clouds},
  journal = {Sensors},
  volume  = {18},
  year    = {2018},
  number  = {4},
  article number = {933},
  url     = {http://www.mdpi.com/1424-8220/18/4/933},
  issn    = {1424-8220},
  doi     = {10.3390/s18040933}
}
                  

[New] Vision-based Pose Estimation for Textureless Space Objects by Contour Points Matching
Xin Zhang, Zhiguo Jiang, Haopeng Zhang and Quanmao Wei
IEEE Transactions on Aerospace and Electronic Systems, 2018
Preprint   Abstract   BibTeX  

This paper presents a novel vision-based method to solve the 6-degree-of-freedom pose estimation problem of textureless space objects from a single monocular image. Our approach follows a coarse-to-fine procedure, utilizing only shape and contour information of the input image. To achieve invariance to initialization, we select a series of projection images which are similar to the input image and establish many-to-one 2D-3D correspondences by contour feature matching. Intensive attention is focused on outlier rejection and we introduce an innovative strategy to fully utilize geometric matching information to guide pose calculation. Experiments based on simulated images are carried out, and the results manifest that pose estimation error of our approach is about 1% even in situations with heavy outlier correspondences.

@article{zhangTAES18,
  author  = {Xin Zhang and Zhiguo Jiang and Haopeng Zhang and Quanmao Wei},
  journal = {IEEE Transactions on Aerospace and Electronic Systems},
  title   = {Vision-based Pose Estimation for Textureless Space Objects
             by Contour Points Matching},
  year    = {2018},
  month   = {},
  volume  = {PP},
  number  = {99},
  pages   = {1—-1},
  issn    = {0018-9251},
  doi     = {10.1109/TAES.2018.2815879}
}
                    

[New] Histopathological Whole Slide Image Analysis Using Context-based CBIR
Yushan Zheng, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yibing Ma, Huaqiang Shi and Yu Zhao
IEEE Transactions on Medical Imaging, 2018
PDF   Abstract   BibTeX   Supplementary  

Histopathological image classification (HIC) and content-based histopathological image retrieval (CBHIR) are two promising applications for histopathological whole slide image (WSI) analysis. HIC can efficiently predict the type of lesion involved in a histopathological image. In general, HIC can aid pathologists in locating high-risk cancer regions from a WSI by providing a cancerous probability map for the WSI. In contrast, CBHIR was developed to allow searches for regions with similar content for a region of interest (ROI) from a database consisting of historical cases. Sets of cases with similar content are accessible to pathologists, which can provide more valuable references for diagnosis. A drawback of the recent CBHIR framework is that a query ROI needs to be manually selected from a WSI. An automatic CBHIR approach for a WSI-wise analysis needs to be developed. In this paper, we propose a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR. In our framework, CBHIR is automatically processed throughout the WSI, based on which a probability map regarding the malignancy of breast tumors is calculated. Through the probability map, the malignant regions in WSIs can be easily recognized. Furthermore, the retrieval results corresponding to each sub-region of the WSIs are recorded during the automatic analysis and are available to pathologists during their diagnosis. Our method was validated on fully annotated WSI datasets of breast tumors. The experimental results certify the effectiveness of the proposed method.

@article{zhengTMI18,
  author  = {Yushan Zheng and Zhiguo Jiang and Haopeng Zhang and Fengying Xie
             and Yibing Ma and Huaqiang Shi and Yu Zhao},
  title   = {Histopathological Whole Slide Image Analysis Using Context-based CBIR},
  journal = {IEEE Transactions on Medical Imaging},
  doi     = {10.1109/TMI.2018.2796130},
  year    = {Epub 2018 January 23}
}