Layer-based visualization and biomedical information exploration of multi-channel large histological data. (March 2019)

• Record Type: Journal Article
• Authors: Zhang, Qi; Peters, Terry; Fenster, Aaron
• Is Part Of: Computerized medical imaging and graphics. Volume 72(2019)
• Page Start: 34
• Page End: 46
• Publication Date: 2019-03
• Subjects: Visualization -- Large histological data -- Biomedical information -- Volume of interest -- Texture extraction and mapping -- Layer-based data navigation; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2019.01.004
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: This paper presents a multi-channel biomedical data and information computing and visualization system that can efficiently process large histological image acquired from high-resolution microscopes. Our system can dynamically display a volume of interest and extract tissue information using a new layer-based data navigation and exploration scheme. During data exploring, the actual resolution of the data loaded can be dynamically determined and updated. The large data rendering is synchronized in four display windows at each data layer, where 2D textures are extracted from the Medical volume and mapped onto the displayed clipping planes with arbitrary orientations. To test the efficiency and scalability of this system, we performed extensive evaluations using different hardware systems and large histological color datasets and experimental results demonstrated a solid performance. Our system can deliver interactive data navigation speed and display detailed imaging information in real time, which is beyond the capability of commonly available biomedical data exploration software platforms. Taking advantage of both CPU (central processing unit) main memory and GPU (graphics processing unit) graphics memory, this software platform can efficiently compute, process and visualize very large biomedical data and enhance data information. Abstract: Background and objective: Modern microscopes can acquire multi-channel large histological data from tissues of human beings or animals, which contain rich biomedical information for disease diagnosis and biological feature analysis. However, due to the large size, fuzzy tissue structure, and complicated multiple elements integrated in the image color space, it is still a challenge for current software systems to effectively calculate histological data, show the inner tissue structures and unveil hidden biomedical information. Therefore, we developed new algorithms and a software platform to address this issue. Methods: This paper presents a multi-channel biomedical data computing and visualization system that can efficiently process large 3D histological images acquired from high-resolution microscopes. A novelty of our system is that it can dynamically display a volume of interest and extract tissue information using a layer-based data navigation scheme. During the data exploring process, the actual resolution of the loaded data can be dynamically determined and updated, and data rendering is synchronized in four display windows at each data layer, where 2D textures are extracted from the imaging volume and mapped onto the displayed clipping planes in 3D space. Results: To test the efficiency and scalability of this system, we performed extensive evaluations using several different hardware systems and large histological color datasets acquired from a CryoViz 3D digital system. The experimental results demonstrated that our system can deliver interactive data navigation speed and display detailed imaging information in real time, which is beyond the capability of commonly available biomedical data exploration software platforms. Conclusion: Taking advantage of both CPU (central processing unit) main memory and GPU (graphics processing unit) graphics memory, the presented software platform can efficiently compute, process and visualize very large biomedical data and enhance data information. The performance of this system can satisfactorily address the challenges of navigating and interrogating volumetric multi-spectral large histological image at multiple resolution levels.